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Total Received Papers: 587 | Total Accepted Papers: 118
Total Rejected Papers: 469 | Acceptance Rate: 20.10%

S. No

Volume-8 Issue-3, February 2019, ISSN: 2249-8958 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication

Page No.

1.

Authors:

Seini Aboh Samuel, L.A.Oparaku, I.N.Itodo

Paper Title:

Physico–Chemical and Mechanical Properties of Soils of Owukpa Lower Coal Measure Geological Formation of Anambra Basin-Nigeria

Abstract: A study was undertaken to determine the physico – chemical and mechanical properties of soils of Owukpa Lower Coal Measure Geological Formation of Anambra Basin, Nigeria. The study was conducted using composite of the three land uses from different locations viz., forested, cultivated and residential. The physico – chemical and mechanical properties vary from one location to another. Soil moisture content ranges 11.84% to 16.82%, soil organic carbon 0.58% to 0.72%, which translated to organic matter content of 1.00% to 1.24% respectively, dry bulk density 1.25g/cm3 to 1.34g/cm3, particle density 2.49g/cm3 to 2.77g/cm3, porosity 46.93% to 52.81%, specific gravity 2.43 to 2.63 and hydrogen ion concentration 5.88 to 6.78 while permeability ranges from 9.45cm/h to 12.33cm/h with moderate permeability class and code 3. The soil grain size distribution were in the ranges of 45% to 65% for sand, 28% to 44% for silt, while clay soil percentages ranges from 8% to 16%. The mechanical properties such as Liquid limit was in range of 9.33% to 22%, Plastic limit 3.80% to 12.84%, shrinkage limit 1.19% to 3.33%, plastic index 2.43% to 8.45%, cohesion 9.67KN/M2 to 73.67KN/M2, angle of internal friction of 11 0 to 18.33 0, and shear strength of 33.89 N to 185.70 N. The results show that Ipiga does not exhibit any of the mechanical properties carried out. The soils from the study area were classified as Loam and sandy loam suitable for all kinds of crops cultivation. This data on soils of Owukpa Lower Coal Measure Geological Formation of Anambra Basin area will be useful for land use classification and developing appropriate soil tillage practices, erosion control design criteria and geotechnical parameter for the study area.

Keywords: Soil, Physico – chemical properties, Mechanical properties, Owukpa Lower Coal Measure, Geological Formation, Anambra Basin.

References:

  1. Food and Agricultural Organization of United State portal (2006) http://www.fao.org/soils-portal/about/all-definitions/en/ accessed on 7/10/16
  2. Brady CN, Weil RR (2005). The Nature and Properties of Soils. 13th Edition, Prentice Hall: New Jersey.
  3. Eneje, R. C. and Mbagwu, J. S. C. 2005 Effects of organic wastes on the physical properties of some tropical soils. Journal of Sustainable Agriculture and the Environment 7: (1), 99-112.
  4. O’Geen A. T, Rachel Elkins, and David Lewis (2006), Erodibility of Agricultural Soils with examples in Lake and Mendocino Counties; ANR Publication 8194. Retrieved Oct 9, 2015 http://anarcatalog.ucdavis.edu.
  5. Price, D.G. (2008). Engineering geology principles and practices, Springller ISBN, 3540292497 pp44 -65
  6. Toy, T.J.; G.R. Foster and K.G. Renard. 2002. Soil Erosion: Processes, Prediction, Measurement, and Control, New York: John Wiley and Sons.
  7. Morgan, R.P.C. 2005 Soil erosion and conservation. (Third Edition), Blackwell publishing Ltd.
  8. Oyediran, A. and Durojaiye, H.F., 2011, Variability in the geotechnical properties of some residual claysoils from south western Nigeria., IJSER., 2 (9), 1-6
  9. Tuncer, E.R. and Lohnes, R.A., 1977, An engineering classification for basalt-derived lateritic soils.Eng. Geol., 4, 319– 339.
  10. Raj, P.P. (2012) Soil Mechanics and Foundation Engineering, Dorling Kindersley (India) Pvt. Ltd., New Delhi, 2012.
  11. Bowles E.J (2012), Engineering Properties of Soils and their Measurements, 4th edition, McGraw Hill Education (India) Private Limited, New Delhi, 2012 [14] Kaniraj R.S, (1988) Design Aids in Soil Mechanics and Foundation Engineering, McGraw Hill Education (India) Private Limited, New Delhi, 1988.
  12. Evans, A. C. 1980. Influence of organic matter on the physical properties of some East Anglia soils of high silt content. J. Soil Sci. 28:11-22(ISI)
  13. Lal, R. (2001): Soil Degradation by Erosion. Land Degradation and Development, 12:519-539.
  14. Roy, S and Bhalla, K.S (2017). Role of Geotechnical Properties of Soil on Civil Engineering Structure .Resources and Environment 2017, 7(4): 103 – 109
  15. Skempton, A.W., (1953), The Colloidal activity of clays; Proc. 3rd Conf. Soil Mechanics and Foundation Engineering (London)., 1, 47–61, 1953.
  16. Laskar, A. and Pal, S.K., 2012, Geotechnical characteristics of two different soils and their mixture and relationships between parameters. EJGE, 17, 2821-2832.
  17. Prakash, S and Jain, K.P (2002), Engineering Soil Testing, Nem Chand & Bros, Roorkee, 2002.
  18. Burland .B (2005) soil mechanics emma. Elegant, rigorous and relevant inaugural lecture honorany fellow, Emmanuel college London, pp23-28
  19. Akayuli, C., Ofosu, B., Nyako, S.O. and Opuni, K.O., 2013, The influence of observed clay content on shear strength and compressibility of residual sandy soils., Int J Eng Res Appl., 3 (4), Jul-Aug, 2538-2542.
  20. Shanyoug, W., Chan, D., Lam, K.C., 2009, Experimental study of the fines content on dynamic compaction grouting incompletely decomposed granite of Hong Kong., CONSTR BUILD MATER., 23, 1249 -1264.
  21. Andreassian, C. K, Panabrokke, C.R., and J.P Quirk. (2004): Effect of Initial Water Content on Stability of Soil Aggregates in Water. Soil Sc. Vol 83. 185-195.
  22. Isikwe M.O., Odumeke G., Matthew O.H.A., (2016) Saturated Hydraulic Conductivity (Ks) of Lower Coal Measure Geological Formation of Owukpa in the River Benue Trough, Nigeria. American Journal of Engineering Research (AJER) Volume-5, Issue-7, pp-47-52
  23. Walkley, A. and C.A Black, (1934). An examination of the digestion jareff method of determining soilorganic Matter and a proposed modification of chronic acid titration method. Soil Sci., 37: 29 – 38

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2.

Authors:

Bharat Singh, Shabana Urooj

Paper Title:

Blood Pressure Control by Deterministic Learning Based Fuzzy Logic Control

Abstract: Automatic control of blood pressure after cardiac operation of patient is wanted in favor of enhanced patient concern; it decreases work of personnel and expenses. Automation of medical drug infusion for controlling of mean arterial pressure (MAP) is extremely advantageous in much clinical function. An assimilating self-tuning control approach for the regulation of mean arterial pressure by infusing sodium nitroprusside is discussed. This paper focuses on omnipresent and verified FUZZY controllers based on reinforcement learning for arterial blood pressure control. The major problem is patient’s sensitivity in different condition although is same condition at different time. To extract the patient’s parameter reinforcement learning approach is proposed and verified. Complete & convenient model of hypertensive patient is effectively developed and processed; with drug response model depiction. Intend and execution of such control arrangement will be controlled using FUZZY logic controllers and for parameter extraction deterministic learning is used. MATLAB Simulation of the designed system models are done for revelation..

Keywords: Drug Delivery system, Deterministic Learning, Fuzzy Inference System, Mean Arterial Pressure

References:

  1. Slate JB, Sheppard LC. Automatic Control of Blood Pressure by Drug Infusion, IEEEProc. 1982; 29: 639-645.
  2. Ejaz K, Yang JS. Controlling Depth of Anesthesia Using PID Tuning A Comparative Model-Based Study”. Proceedings of the 2004 IEEE international Conference on Control ApplicationsTaipei, Taiwan, September 2-4,2004
  3. Isaka S. Control Strategies for arterial blood pressure regulation, IEEE trans. on Bio. Engg vol. 1993; 40(4).
  4. Bequette BW. Systems and Control Education: A Drug Infusion Case Study, Proceedings of The first Joint BMESI/EMBS Serving Humanity, Advancing Technofogy od,1999; 13(16).
  5. Sandu C, Popescu D, Dimon C. Blood Pressure Regulation – Robust Control, 20th International Conference on Control Systems and Science,2015
  6. Isaka S, Sebald AV. An Optimization Approach for Fuzzy Controller Design. IEEE transactions on Biomedical engineering.1993; 40(3).
  7. Delapasse JS, Behbehani K, Tsui K, Klein KW. Accommodation of time delay variationsin automatic infusion of sodium nitroprusside, IEEE Trans. Biomed. Eng., 1994; 41(1): 1083-1091.
  8. Zheng H, Zhu K. Automated post-operative blood pressure control, Jour. Cont. theory andAppln,2005; 3: , 227-212.
  9. Gao Y, Er MJ, An Intelligent Adaptive Control Scheme For Postsurgical Blood Pressure Regulation, IEEE Trans. Neural Networks, 2005; 16: 475-483.
  10. Wang QG., Lee TH, Fung HW, Bi Q, Zhang Y. PID tuning for improved performance, IEEETrans. Control Systems Technology, 1999; 7(4): 457-465.
  11. Srinivasa Y, Timmonsa WD, Durkinb J. A comparative study of three expert systems for blood pressure control, ExpertSystems with Applications, 2001; 20: 267-274.
  12. Gao Y, Er MJ. Adaptive Fuzzy Neural Control of Mean Arterial Pressure Through Sodium Nitroprusside Infusion, Proceedings ofthe 42nd IEEE Conference on Decision and Control Maui, Hawaii USA, December 2003
  13. Elamvazuthi,. Aymen OM, Salih Y, Tawfeig H. An intelligent control of blood pressure system using PID and neural network, 8th IEEE Conference on Industrial Electronics and Applications, Melbourne, Jun 2013; 1049-1053.
  14. Gao Y, Er MJ. An Intelligent Adaptive Control Scheme for Postsurgical Blood Pressure Regulation”. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2005; 16 (2).
  15. Ma S, Maka S.” PI Controller Based Closed Loop Drug Delivery for the Long Term Blood Pressure Regulation,” Annual IEEE India Conference (INDICON), Kochi, 2012; 998-1002.
  16. Isaka S,. Sebald AV. An adaptive fuzzy controller for blood pressure regulation. IEEE transactions on systems, man, and cybernetics, November/December 1992; 22(6).
  17. Urooj S, Singh B. Control of mean arterial pressure using fractional PID controller, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, 2016; 1556-1559.
  18. Malagutti N, Dehghani A, Kennedy RA, Robust control design for automatic regulation of blood pressure, Published in IET Control Theory and Applications 2012
  19. Feng J, Bo Q, Kuanyi Z, “Implementation of Drug Delivery system for blood pressure regulation”. 9th International Conference on Control, Automation, Robotics and Vision, Singapore, 2006; 1-5.
  20. Shahin M, Maka S. Control relevant physiological model of the long term blood pressure regulatory system, Int. Jnl. Biomed. Engg. and Tech., 2011; 5 (4): 371-389,
  21. Cheriyachan A, Nafeesa K, Bedeeuzzaman K. Arterial Blood Pressure Regulation in Hypertensive Patients Using Fuzzy Logic Control Annual IEEE India Conference (INDICON), New Delhi, 2015; 1-5.
  22. Singh B, Urooj S. Adaptive Parameter Estimation Based Drug delivery System for Blood pressure regulation in Information and Decision Sciences, Advances in Intelligent Systems and Computing, vol 701. Springer,2018; 701: 465-472.

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3.

Authors:

Anil Khandelwal, Yogendra Kumar Jain

Paper Title:

An efficient fuzzy c-means with SAW and WPM algorithms for the cluster head selection

Abstract: Wireless sensor network (WSN) is a type of ad hoc network self-configured and infrastructure less. This study provides the efficient approach for cluster heads (CHs) selection for achieving synchronous data sink operation. We have proposed FCM based clustering approach along with the simple additive weighting (SAW) and weighted product method (WPM) for the inner CHs selection based on the priority ranking. First the node weights were assigned based on the node operation. These values were considered for clustering. The cluster data provides the total coverage area and it shows the need of the nodes in the complete area. Then for the selection of CHs from the cluster, SAW and WPM methods have been applied. The results from the SAW and WPM provide an efficient way of inner cluster selection. The results comparison considered with the same parameters and the higher packet size. Despite of using the higher size the results from our approach is better than the traditional approaches in terms of packet delivery and energy consumption.

Keywords: Wireless sensor network, FCM, Simple additive weighting, Weighted product method.

References:

  1. Bergelt R, Vodel M, Hardt W. Energy efficient handling of big data in embedded, wireless sensor networks. In sensors applications symposium 2014 (pp. 53-58). IEEE.
  2. Vodel M, Hardt W. Data aggregation in resource-limited wireless communication environments—differences between theory and praxis. In international conference on control, automation and information sciences 2012 (pp. 208-213). IEEE.
  3. Sarma HK. Grid Based Data Gathering in Multi-channel Wireless Sensor Network. In international conference on information technology 2016 (pp. 114-117). IEEE.
  4. Muzakkari BA, Mohamed MA, Kadir MFA, Mohamad Z, Jamil N. Recent advances in energy efficient-QoS aware MAC protocols for wireless sensor networks. International Journal of Advanced Computer Research. 2018; 8(38): 212-28.
  5. Irandegani M, Bagherizadeh M. Designing an asynchronous multi-channel media access control protocol based on service quality for wireless sensor networks. International Journal of Advanced Computer Research. 2017; 7(32):190.
  6. Hung CC, Hsieh CC. Big data management on wireless sensor networks. Big Data Analytics for Sensor-Network Collected Intelligence. 2017. Elsevier.
  7. Roadknight C, Parrott L, Boyd N, Marshall IW. Real-time data management on a wireless sensor network. International Journal of Distributed Sensor Networks. 2005; 1(2):215-25.
  8. Upadhyay H, Mehta M. Improved APAC algorithm for minimizing delay in wireless sensor network with mobile sink. International Journal of Advanced Computer Research. 2017; 7(28):23.
  9. Rayenizadeh M, Rafsanjani MK, Saeid AB. Cluster head selection using hesitant fuzzy in wireless sensor networks. In Iranian joint congress on fuzzy and intelligent systems 2018 (pp. 139-141). IEEE.
  10. Al Rasyid MU, Lee BH, Syarif I, Arkham MM. LEACH Partition Topology for Wireless Sensor Network. In international conference on consumer electronics-Taiwan 2018 (pp. 1-5). IEEE.
  11. Darabkh KA, Zomot JN. An improved cluster head selection algorithm for wireless sensor networks. In international wireless communications & mobile computing conference 2018 (pp. 65-70). IEEE.
  12. Khandelwal A, Jain YK. An efficient k-means algorithm for the cluster head selection based on SAW and WPM. International Journal of Advanced Computer Research. 2018; 8(37):191-202.
  13. Darabkh KA, Al-Jdayeh L. A new fixed clustering based algorithm for wireless sensor networks. In international wireless communications & mobile computing conference 2018 (pp. 71-76). IEEE.
  14. Juwaied A, Jackowska-Strumitto L. Analysis of cluster heads positions in stable election protocol for wireless sensor network. In international interdisciplinary PhD workshop 2018 (pp. 367-370). IEEE.
  15. Razzaq M, Ningombam DD, Shin S. Energy efficient k-means clustering-based routing protocol for WSN using optimal packet size. In international conference on information networking 2018 (pp. 632-635). IEEE.
  16. Rubel MS, Kandil N, Hakem N. Priority management with clustering approach in Wireless Sensor Network (WSN). In international conference on digital information, networking, and wireless communications 2018 (pp. 7-11). IEEE.
  17. Dubey AK, Gupta U, Jain S. Comparative study of k-means and fuzzy c-means algorithms on the breast cancer data. International Journal on Advanced Science, Engineering and Information Technology. 2018; 8(1):18-29.
  18. Nagamalar T, Rangaswamy TR. Energy efficient cluster based approach for data collection in wireless sensor networks with multiple mobile sink. InIndustrial Instrumentation and Control (ICIC), 2015 International Conference on 2015 May 28 (pp. 348-353). IEEE.

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4.

Authors:

Anshuman Kumaar Singh, Akhil Pillai, Bibhash Kundu, Hrithik Mohan, Gowreesh S.S

Paper Title:

Design and Development of an Automatic Solar Powered Digging, Seed Sowing and Dripping Machine.

Abstract: Agriculture forms an integral part of the Indian economy. The methodology implemented in carrying out agricultural activities faces many constraints such as non-availability of labor, low–productivity rate, irregularity due to weather constraints and fatigue. These constraints along with the widespread usage of the fossil fuels to power I.C engines or external combustible engines have added to the plight of the agriculture sector in our country. With the objective of eradicating such bottlenecks the idea of an automatic solar powered seed-sowing machine is introduced, which could effectively carry out the digging, sowing, and watering of the land at a reduced cost and with no harm to the environment. The mechanism involves the use of a solar panel to capture the solar radiation and simultaneously convert it into electrical energy for further storage. The electrical energy thus stored charges a 12V battery, which in turn provides the required input power to the shunt wound D.C motor. The motor transmits the power to the battery-controlled wheels and enables movement of the system. To enhance the functionality of the system a remote controlling operation and a water dripping unit is attached which shall help in maneuvering the system in the field and enable constant water supply after each digging and sowing operation. Seed sowing machine shall execute ground digging, seed sowing, and watering operation simultaneously with reduced cost and fatigue.

Keywords: direct current shunt wound motor, Solar panel, 12V battery, Rotary encoder, Microcontroller,, water- dripping unit, battery controlled wheels.

References:

  1. Adityakawadaskar, dr. S. S. Chaudhari“Review of methods of seed sowing and concept of multi-purpose seed sowing machine” “international journal of pure and applied research in engineering and technology”IJPRET, 2013; Volume 1(8): 267-276.
  2. “Solar Powered Variable Pitch Smart Seed Sowing System with Herbicides Sprayer” International Journal of Engineering Trends and Technology( IJETT)- VOLUME 46 Number 7 April 2017
  3. “Solar Powered Digging and Seed Sowing Machine”. International Journal for Research in Applied Science & Engineering Technology (IJRASET).Volume 5 Issue III, March 2017 ISSN: 2321-9653.
  4. “Design and Development of Automatic Operated Seeds Sowing Machine”.International Journal on Recent and Innovation Trends in Computing and Communication. ISSN: 2321-8169 Volume: five Issue 2. 277 – 279.
  5. International Journal of Emerging Technology & Research Volume 1, Issue 3, Mar-Apr, 2014 (www.ijetr.org) ISSN (E): 2347-5900 ISSN(P): 2347-6079“Automatic Seed Planter Punching Type”

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5.

Authors:

Pankaj Pathak, Parashu Ram Pal, Manish Shrivastava, Priyanka Ora

Paper Title:

Fifth Revolution: Applied AI & Human Intelligence with Cyber Physical Systems

Abstract: Rapid advances in sophisticated technologies, especially those engaging robots have always mesmerized mankind, driving consistent quality and flow in manufacturing processes. The focus has been on eliminating or at least reducing the dull, dangerous, and dirty jobs for human workforce. The industry seems to continuously reincarnate from mass production and customized production into mass personalization objectives to fulfil customer responsiveness while also achieving cost efficiencies, a concept that explains an industrial revolution involving the human touch to be christened as Industry 5.0. The concept even though still visionary yet a realistic one includes Collaboration of human and artificial intelligence with IOT enabled devices. Fifth revolution restricted the advantages cultivated from fourth revolution and it brings humans back into the picture. Fifth industrial revolution demands high skilled people and robots working together to create personalized products, services and experiences. The purpose of this paper is to implement a systematic study of Industry 5.0 to construct an overview of its core dimensions, identifying key constructs that inter-relate to achieve the objective of integrating human touch with technology. The study also lists out the key focus area involved in its implementation and also describes the approach of human computer collaboration. Fifth revolution still is in its initial stage but companies are trying to act sooner upon it causes they wants to be ahead from their competitors. Consequently, the knowledge emerging from the review of the Industry 5.0 is further synthesized for delineating further research agenda.

Keywords: Industry 5.0, mass personalization, human touch, fifth industrial revolution, robots. 

References:

  1. Alippi, C. and Ozawa, S., 2019. Computational Intelligence in the Time of Cyber-Physical Systems and the Internet of Things. In Artificial Intelligence in the Age of Neural Networks and Brain Computing(pp. 245-263). Academic Press.
  2. Akgun, M. Cakmak, J.W. Yoo, A.L. Thomaz, Trajectories and keyframes for kinesthetic teaching: a human-robot interaction perspective, Proceedings of the seventh annual ACM/IEEE int. conf. on Human-Robot Interaction, ACM, 2012, pp. 391–398.
  3. Brown, S. and Pierson, H.A., 2018. A Collaborative Framework for Robotic Task Specification. Procedia Manufacturing17, pp.270-277.
  4. Hu, S.J., 2013. Evolving paradigms of manufacturing: from mass production to mass customization and personalization. Procedia CIRP7, pp.3-8.
  5. Kormushev, P., Calinon, S., & Caldwell, D. G. (2011). Imitation learning of positional and force skills demonstrated via kinesthetic teaching and haptic input. Advanced Robotics25(5), 581-603.
  6. Monostori L, Kádár B, Bauernhansl T, Kondoh S, Kumara S, Reinhart G, Sauer O, Schuh G, Sihn W, Ueda K (2016) Cyber-Physical Systems in Manufacturing. CIRP Annals—Manufacturing Technology 65(2):621–641.
  7. Wrede, C. Emmerich, R. Grünberg, A. Nordmann, A. Swadzba, J. Steil, A user study on kinesthetic teaching of redundant robots in task and configuration space, J. Hum. Robot Interact. 2 (1) (2013) 56–81.
  8. Schou, C., Andersen, R. S., Chrysostomou, D., Bøgh, S., & Madsen, O. (2018). Skill-based instruction of collaborative robots in industrial settings. Robotics and Computer-Integrated Manufacturing53, 72-80.
  9. Simpson, T.W., Maier, J.R. and Mistree, F., 1999, September. A product platform concept exploration method for product family design. In ASME Design Theory and Methodology(Vol. 9, pp. 1-219).
  10. Stern, H. and Becker, T., 2017. Development of a Model for the Integration of Human Factors in Cyber-physical Production Systems. Procedia Manufacturing9, pp.151-158.
  11. Yao, B., Zhou, Z., Wang, L., Xu, W., Yan, J. and Liu, Q., 2018. A function block based cyber-physical production system for physical human–robot interaction. Journal of Manufacturing Systems.
  12. Tan, C., Hu, S.J., Chung, H., Barton, K., Piya, C., Ramani, K. and Banu, M., 2017. Product personalization enabled by assembly architecture and cyber physical systems. CIRP Annals66(1), pp.33-36.
  13. Jarrahi, M.H., 2018. Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons.
  14. Zezulka, F., Marcon, P., Vesely, I. and Sajdl, O., 2016. Industry 4.0–An Introduction in the phenomenon. IFAC-PapersOnLine, 49(25), pp.8-12.
  15. Gurkaynak, G., Yilmaz, I. and Haksever, G., 2016. Stifling artificial intelligence: Human perils. Computer Law & Security Review, 32(5), pp.749-758.

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6.

Authors:

Vinod Kumar Saroha, Sanjeev Rana

Paper Title:

Performance evaluation in implementing a multi-layer job scheduling approach with energy efficient resource utilization over a cloud

Abstract: A cloud computing provides the platform where numerous users and companies are connected for accessing different types of services such as software, applications, platforms and infrastructure. This technique utilizes various online web based processing structures. There is one major issue regarding the energy consumption and dissipation of cloud server during processing of their routine tasks. In this research work, our chief focus is to investigate and reduce energy consumption with enhanced scheduling approach based on multi-layer architecture. Here, we propose and implement a multi-layer scheduling approach and request the load balancer for managing multiple job queues along with effective resources utilization over cloud network. This is performed by the creation of client server database on the cloud where the servers are categorized as highest, intermediate and lowest priority server on the basis of the configurational parameters of processing speed, RAM and time; while the client requests are classified on the basis of urgency (processing need) and assigned the priorities likewise. A high priority job request is executed by higher configuration server while the lower tasks are accomplished by the lower configuration servers; that helps in energy saving..We evaluate our work with various performance parameters viz. energy, network (processor) utilization and response (processing) time to get optimal results. The evaluation work involves different scheduling and load balancing cloud computing algorithms viz Round Robin procedure, Minimum Completion Time (MCT) algorithm and Opportunistic Load Balancing (OLB) etc.; for efficiently utilizing the resources. The comparative study of the proposed algorithmic approach outperforms the earlier ones and yields better energy efficiency.

Keywords: Resource scheduling, Efficiency of energy, Throughput, Response time, Processor Utilization. 

References:

  1. Ning Liu, Ziqian Dong, et al., 2013, “Task scheduling and server provisioning for energy-efficient cloud-computing data centers” in 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops.
  2. Qi Zhang and Raouf Boutaba, et al. 2014 “Dynamic Workload Management in Heterogeneous Cloud Computing Environments”
  3. Gupta, P.K. and N. Rakesh, 2010. “Different job scheduling methodologies for web application and web server in a cloud computing environment”, Proceedings of the 3rd International Conference on Emerging Trends in Engineering and Technology, Nov. 19-21, IEEE Xplore Press, Goa, pp: 569-572. DOI: 10.1109/ICETET.2010.24
  4. Vinod Kumar Saroha, Dr. Sanjeev Rana Review: Analysis, Design and working performance of scheduling algorithms in Cloud Network” Pg. 51-in IJETAE (International Journal of Emerging Technology and Advanced Engineering) Vol-07, Issue-06, June 2017 (ISSN 2250-2459 online)
  5. Vinod Kr. Saroha, Sanjeev Rana; “Towards Energy Efficient Multilayer job scheduling approach over a cloud Network” IEEE Digital Explore; International Conference on Power Control, Signals and Instrumentation Engineering (ICPC& I-2017)
  6. Tolia, Z. Wang, “Delivering energy proportionality with  non  energy            proportional
  7. Systems”.In proc-hotpower, San Diego, Dec. 2008.
  8. Suman Rani, Vinod Saroha, Sanjeev rana “Hybrid approach of round robin, Throttle and equally spaced technique for load balancing in cloud Enviornment” IJIACS, Vol-06, Issue-08, August-2017, ISSN-2347-8616.
  9. Divya, Anil Arora, “Resource Allocation Scheme On Multiple Clouds” published in National Conference On Advances In Computing, Communication and Information (NCACCI-2015) page No.28
  10. Orgerie and L. Leffevre. “When Clouds become Green: the Green Open Cloud Architecture”, Parallel Computing, 19:228 - 237, 2010.
  11. Tang and S. Chanson, “Optimizing static job scheduling in a network of heterogeneous computers”, In Parallel Processing International Conference 2000 pages 373 - 382..
  12. Swachil Patel, Upendra Bhoi, “Priority Based Job Scheduling Techniques In Cloud Computing: A Systematic Review”, International Journal Of Scientific & Technology Research VOLUME 2, ISSUE 11, NOVEMBER 2013.
  13. Kandula,and I.Stoica. “What slows datacenter jobs and what to do about it”.RAD Lab, CA, January 2010.
  14. Komal, Vinod Saroha, Sanjeev Rana, “An efficient service allocation and deallocation in cloud environment” IJRASET, Volume-05, Issue-08, July-2017.
  15. Lipsa Tripathy, Rasmi Ranjan Patra, “Scheduling In Cloud Computing”, International Journal on Cloud Computing: Services and Architecture (IJCCSA) ,Vol. 4, No. 5, October 2014.
  16. Nima Jafari Navimipour and Farnaz Sharifi Milani, “Task Scheduling in the Cloud Computing Based on the Cuckoo Search Algorithm”, International Journal of Modeling and Optimization, Vol. 5, No. 1, February 2015.
  17. Surveillance System for monitoring in Cloud Computing using IOI”, Nikita, Vinod     Saroha, Sanjeev Rana, IJRASET, Vol-05, Issue07, July 2017.

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7.

Authors:

P.Naga Gunapriya, K.Nagabhushanam, R.Kiranmayi

Paper Title:

An Anfis Based Control Approach For Network Connected Spv Framework

Abstract:A Network connected two-leg 3 stage 3 wire SPV (solar photo voltaic) structure with ANFIS (An Adaptive Neuro Fuzzy Inference framework) controller is exhibited in this paper, in which a lift converter is utilized as an essential stage to be used for the normal for MPPT (Maximum Power Tracking point) and a 3-leg VSC (Voltage Source Converter) is utilized to give the isolated SPV vitality alongside connected conveyance framework for development in the power quality.The THD(Total Harmonic Distortion) values of proposed algorithm is compared with neuron based control approach via simulation. The reduction of unwanted dc component, balancing of network currents and non active power compensation of the load flows are additional characteristics awarded by the proposed methodology. The dynamic performance of SPV system due to effects of insolation is obviously presented. The active power sparkly bit of load current is evaluated with an ANFIS control based control approach. The output of current part that considered system side expeditiously deals with the DC connect voltage. In the proposed methodology, the freight, PV exhibit are kept decoupled.. The obtainability of proposed control algorithm is affirmed through MATLAB/SIMULINK RESULTS.

Keywords: ANFIS, Solar PV; Two-leg; Power Quality; MPPT.

References:

  1. N Y Dahlan, Mohd Afifi Jusoh and W N A W Abdullah, "Sun based structure value for Malaysia: Analysis utilizing establishment turns," IEEE Power Engineering and Optimization Conference (PEOCO), 2014, pp.461-466.
  2. Reichelstein and M. Yorston, "The prospects for cost focused sun powered PV control extraordinary zone: Long run changes to functional cash related structures in the European Union and past," Energy Policy, vol. 55, pp. 117– 127, 2013.
  3. B. Kjaer, J.K. Pedersen and F. Blaabjerg, "An investigation of single-arrange cross segment related inverters for photovoltaic modules," IEEE Trans. on Industry Applications, vol.41, no.5, pp.1292-1306, Sept.- Oct. 2005.
  4. Yang Chen and K.M. Smedley, "A financially insightful single-organize inverter with most phenomenal power point following," IEEE Trans. on Power Electronics,vol.19, no.5, pp.1289-1294, Sept. 2004.
  5. Anand, S. K. Gundlapalli and B. G. Fernandes, "Transformer-Less Grid FeedingCurrent Source Inverter for Solar Photovoltaic System," IEEE Trans. Indus. Electron., vol. 61, no. 10, pp. 5334-5344, 2014.
  6. J. Kish, J. J. Lee and P. W. Lehn, "Appearing and control of photovoltaic sheets using the continuous conductance framework for most exceptional power point following," IET Renewable Power Generation, vol.6, no.4, pp.259-266, July 2012.
  7. A. Elgendy, B. Zahawi and D. J. Atkinson,"Evaluation of aggravate and watch MPPT figuring execution procedures," IET Renewable Power Generation Conference, 2011, pp.1-6.
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  10. Chinmay Jain and Bhim Singh "A Frequency Shifter Based Simple Control for Multifunctional Solar PV Grid Interfaced System" 37th National System Conference, 2013, pp 1-6.
  11. S. Aldobhani, R. John, "Most extreme Power point following of PV framework utilizing ANFIS forecast and fluffy rationale following", Proc. Int. Multiconf. Of Engineeris and Computer Scientists IMECS, CD-ROM, vol. II, 19– 21 March, 2008.
  12. J.- S.R. Jong, "ANFIS: versatile system based fluffy deduction framework", Systems Man and Cybernetics IEEE Transactions on, vol. 23, no. 3, pp. 665-685, May/Jun 1993.

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8.

Authors:

Embeti Sangeetha, T.Manohar

Paper Title:

Estimation of Disturbances by using Adaptive Fuzzy for SISO System

Abstract: The adaptive fuzzy output is proposed for an original observer to estimate unknown and periodic input of a LTI SISO discrete time system. An important advantage is it does not involve inverting the model of OLS, as a result it can be applicable to both MP and NMP systems. The exactness of the model needs zero gain of compensator. The effectiveness of the observer design is assessed by a numerical example including model error and measurement noises, using Matlab/ Simulink software.

Keywords: DOB/UIOB (disturbance/ unknown input observer), Adaptive Fuzzy systems, filtering.

References:

  1. Umeno, T. Kaneko, and Y. Hori, “Robust Servosystem design with two degrees of freedom and its application to novel motion control of robot manipulators,” IEEE Transactions on Industrial Electronics, vol. 40, no. 8, pp. 473–485, 1993.
  2. M. Trujillo and H. R. Busby, ractical Inverse Analysis in Engineering. CRC Press,  1997.
  3. Radke and Z. Gao, “A survey of state and disturbance observers for practitioners,” in Proceedings of the 2006 AmericanControl onference,(Minneapolis, Minnesota, USA),  pp.  5183–5188, 2006
  4. Shim, N. H. Jo, and Y. I. Son, “A new disturbance observer for NMP  linear systems,” in Proceedings of the 2008         American Control Conference,  (Seattle,  Washington, USA), pp. 3385–3389, 2008.
  5. M.-S. Chen, S.-Y.Lin, M.-L.Tseng, Y.-L.Yeh, and J.-Y Yen,“Robust state-and-disturbance observer design for linear non-minimum-phase systems,” sianJournal of control, vol. 18, no.   4, pp.1–7,2016.

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9.

Authors:

Donthulwar Swarupa Rani, N Dinesh Kumar, P.A Harsha Vardhini

Paper Title:

Post-correction of Analog-To-Digital Converter for Different Input signals

Abstract: A novel post correction method with real-time FPGA implementation is proposed to correct the distortion generated by high-speed Analog ¬to¬ Digital Converters (ADCs). It is achieved by simplifying the dynamic deviation reduction based Volterra series to form an accurate model to effectively compensate both static nonlinearities and memory effects. Both post correction model generation and model extraction modules can be readily implemented in FPGA, which provides great flexibilities in realizing real-time calibration. Experimental results demonstrated that excellent calibration performance can be achieved with very low implementation complexity by employing the proposed method. The fundamental observation, upon which this work is motivated, is that practical analog-to-digital converters are prone to produce errors, i.e., deviations from the ideal operation. The term ‘post correction’ indicates that the correction methods considered in this work are applied after the converter, thus operating on the digital signal provided from the output. One of the constraints for this work is that the internal signals and states of the analog-to-digital converter under consideration are unavailable to us. The goal of the correction is, naturally, to make the corrected output from the converter more true to the ideal output, in some sense; as we will see later on, there are many ways to measure the performance of a converter. Error correction of ADCs has received increasing attention during the last two decades. These methods have in common that the ADC to be corrected is treated as a closed entity, i.e., internal signals and states of the ADC are not available, and the calibration and correction methods must operate outside of the converter. Moreover, the correction is dependent on the output signal x(n) of the ADC to be corrected. That is, the correction is an operation incorporated after the ADC, hence the name post-correction. This paper introduces the present status of analog to digital converter for sine waveform (sine wave). Based on the fundamental principle, the paper then focuses on the different input waveforms such as pulse, triangular type, and square sine for analog to digital converter and compared few ADC parameters like SFDR and SINAD.

Keywords: Post correction, SFDR, SINAD, square wave, sine wave.

References:

  1. Post-Correction of Analog to Digital Converters Gong, Pu Guo, Hua September 2008. Department Of Technology And Built Environment, Master’s Thesis in Electronics /Telecommunications, Examiner: Magnus Isaksson, Supervisor: Niclas Björsell, pp 1-48.
  2. Post-correction of Analog-To-Digital Converter with Real-time FPGA Implementation: A Review by Pradip mane and P.L Paikrao. International Journal of Innovative and Emerging Research in Engineering Volume 3, Special Issue 1, ICSTSD 2016. pp 615-617.
  3. Digital automatic calibration method for time interleaved ADC’s system used in time domain EMI measurement receiver, By Slim, Hassan Hani and Peter Russer, 2011 IEEE International Symposium on Electromagnetic Compatibility (2011), pp 476-479.
  4. Adaptive blind background calibration of polynomial represented frequency response mismatches in a two channel time interleaved ADC by, Shahzad Saleem and Christian Vogel, Ieee Transactions On Circuits And Systems—I: Regular Papers, Vol. 58, No. 6, June 2011, pp 1300-1310.
  5. Yashar Hesamiafshar and sanaz momeni the authors of A new DFT based approach for gain mismatch detection and correction in the time interleaved ADC’s. Published in: NORCHIP 2010, Date of Conference: 15-16 Nov. 2010 Date Added to IEEE Xplore: 17 December 2010, pp: 1- 4.
  6. Schmidt, J. E. Cousseau, J. L.Figueroa, R. Wichman and S. Werner “ADC post-compensation using a Hammerstein model”, Proc. of the Argentine School of Micro-Nanoelectronics Technology and Applications, catalog, pp. 71-76
  7. Comparison between Adaptive filter Algorithms (Lms, Nlms And Rls) Jyoti Dhiman1, Shadab Ahmad2 , Kuldeep Gulia3. ISSN: 2278 – 7798, International Journal of Science, Engineering and Technology Research (IJSETR), Volume 2, Issue 5, May 2013, pp 1100-1103.

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10.

Authors:

Banita , Poonam Tanwar

Paper Title:

Evaluation of Facial Paralysis Using Face Model

Abstract: Facial paralysis is a common cause of uneven face dimensions. It is very challenging to diagnose the exact level of facial paralysis. The entity is less recognized in facial palsy and in literature as well. The aim of the study is to investigate the recovery rate of an individual suffering from facial paralysis. The Material and Methods was an observational, manual study done for a period of two years at PGIMS Rohtak. The cases having initial stroke were studied for Conduction velocity changes showing in the form of waveform for Cranial nerve. All the data were analyzed and studied by using fuzzification and MATLAB 7.Total 100 cases of facial paralysis studied for EMG changes. The average age was from 17 to 68 years. The men age group affected was from 17 to 26 years. The clinical representation was pain behind the ear and uneasiness. Other symptoms were pain behind the ear, nausea, stuffiness, changed sense of taste, dropping of mouth, difficulty to close eye etc. The compound motor action potential were recorded with the use of single pair and two pair electrodes in Pathology laboratory. Fuzzy model was used to analyze the system and to detect the exact recovery rate of facial paralyzed patient. If grading system is used to investigate the model followed by fuzzification will help to detect level of facial paralysis and also to detect the exact recovery rate.

Keywords: Classification of paralysis, Conduction Velocity, Grading system, Fuzzification, 2D and 3D.

References:

  1. Swati Suni Jagtap, KC Wingker, Chandrashekhar Aundhkar, Saswati Boral, Sunil Vitthalrao Jagtap. Role of EEG in diagnosing abdominal epilepsy patients. 2018.
  2. Nikolaos Anastasopoulos, Trifon Totlis, Nikolaos Lazaridis, Konstantinos Natsis. Complete paraplegia due to anterior spinal artery syndrome following total knee arthroplasty under epidural anaesthesia: A Case Report. 2018.
  3. Asgari Abaszadeh, Mohammad Zamani, Askari Noorbaran, Sekieh Kamali Ahangar, mohammad Reza Sheikh Ansari, Novin Nikbakhsh. Is it necessary to drain after thyroid surgeries? A prospective randomized clinical trial. 2017. 11(12).
  4. Pradosh Kumar Sarangi, Pratisruti hui, HS Sagar, Dinesh Kumar kisku, Jayashree mohanty. Combined left recurrent laryngeal nerve and phrenic nerve palsy: A rare presentation of throracic aortic aneurysm. 2017. 11(5).
  5. Chetan Pathak, Salil Pawah, Arpit Sikri, Pushpanjali Rexwal, Prachi Aggarwal. Lip and lower lid supporting prosthetic appliance: A unique approach of treating unilateral facial paralysis. 2017. 11(5).
  6. Anguraj, S. Padma. Evaluation and severity classification of facial paralysis using Salient point selection algorithm. 2015. 123(7).
  7. Aris Garro, Lise E, Nigrovic. Managing peripheral facial palsy. 2017. 71(5).
  8. Surabhi Chopra, Arpan Chaturmohta. Gold weighted eyelid implant in post operative facial nerve palsy. 2017. 6(6). 2277-8160.
  9. Anguraj, S. Padma. Analysis of facial paralysis disease using image processing technique. 2012. 54(11). 0975-8887.
  10. Reginald Baugh, Gregory Basura, Lisa Ishii, Seth R. Schwartz, Caitlin Murray Drumheller,     Rebecca Burkholder. Clinical Practice guideline summary: Bell’s palsy. 2013. AAO-HNS bulletin.
  11. Dhruvashree Somasundara, Frank Sullivan. Management of Bell’s palsy. 2017. 40(3).
  12. Lauren R.M. Eagelston, Frederic Deleyiannis, Richard Appell. 3D Imaging approaches to analyzing outcomes of facial reanimation surgery. Poster.
  13. Jagga, M. Lehri, A.& Verma, S.K. Effect of ageing and anthropometric measurements on nerve conduction properties- A Review. 2011. 7(1). 1-10.
  14. Philipp Meyer-Marcotty, Angelika stellzig-Eisenhauer, Ute Bareis, Jutta Hartmann, Janka Kochel. Three dimensional perception of facial asymmetry. 2011. 33. 647-653.
  15. David F Mayor. Electroaccupuncture: An introduction and its use for peripheral facial paralysis. 2007. 84.
  16. Garrett R.Griffin, Waleed Abuzeid, Jeffrey vainshtein, Jennifer C. Kim. Outcomes following temporalis tendon transfer in Irradiated patients. 2012. 14(6). 395-402.
  17. Oya Celikutan, Sezer Ulukaya, Bulent Sankur. A comparative study of face landmarking techniques. 2013. 1(13).
  18. CALVIN minds in the making. Defining fuzzy sets. mathworks.com.
  19. Arman Savran, Nese Alyuz, Hamdi Dibeklioglu, Oya Celikutan, Brek Gokberk, Bulent Sankur, Lale Akarun. Boshphorus Database for 3D face analysis. 2008. 5372. 47-56.
  20. Fang, X. Zhao, O. Ocegueda, S.K. Shah, I.A. Kakadiaris. 3D Facial expression recoginition: A prespective on promises and challenges.2011. 603-610.
  21. Kevin W. Bowyer, Kyong Chang, Patrick Flynn. A survey of approaches and challenges in 3D and multi-modal 3D+2D face recoginition. 2005. 1-15.
  22. Gerd Fabian Volk, Martin Pohlmann, Mira Finkensieper, Heather J Chalmers, Orlando Guntinas-Lichius. 3D-Ultrasonography for evaluation of facial muscles in patients with chronic facial palsy or defective healing: a pilot study. 2014. 14(4).
  23. Ravi chander rao Annamaneni, Mukunda Reddy D, Srikanth R., Sridhar Moturi, Arpitha Komuravalley, Srinivasa Rao Sadam, Shashi Kanth V., Bhadra Rao V. To evaluate the feasibility of Neurotosation of facial nerve branches with lpsilateral masseteric nerve: An Anatomic study. 2014. 8(4).
  24. Arianna Di Stadio. Another scale for the assessment of facial paralysis? ADS Scale: Our proposition, How to use it. 2015. 9(12). 8-11.
  25. Sowmya GV, Manjunatha BS, Saurabh Goel, Mohit pal singh, madhusudan astekar. Facial pain followed by unilateral facial nerve palsy: A case report with literature review. 2014. 8(8). 34-35.
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11.

Authors:

Mukesh Kumar, Yass Khudheir Salal

Paper Title:

Systematic Review of Predicting Student's Performance in Academics

Abstract: Data mining (DM) gaining popularity due to its advantages in the educational environment. Most of the educational institution, now a day applied these techniques to make improvement in their education system. By using these techniques, academic performance of the student is analyzed and if find anything wrong with the student performance then timely help will be provided to that student. In our education system, we lack in finding those factor which mostly affects the student performance in academics. Therefore, a systematic review of all the authors work done in this field is required to understand the data mining application in education and how it helps to improve and predict the student academic performance. In this article, the main focus moves around two important factors: Firstly, to find the most critical factors which affect the student performance used by the most researcher and secondly to find the algorithm which is mostly used.

Keywords: Academic Performance, Educational Data Mining, Prediction, Classification. 

References:

  1. M. S. Anupama Kumar, “Appraising the significance of self-regulated learning in higher education using neural networks”, International Journal of Engineering Research and Development Volume 1 (Issue 1) (2012) 09–15.
  2. Bunkar, U. K. Singh, B. Pandya, R. Bunkar, Data mining: Prediction for performance improvement of graduate students using classification, in Wireless and Optical Communications Networks (WOCN), 2012 Ninth International Conference on, IEEE, 2012, pp. 1–5.
  3. Parack, Z. Zahid, F. Merchant, “Application of data mining in educational databases for predicting academic trends and patterns”, in Technology Enhanced Education (ICTEE), 2012 IEEE International Conference on, IEEE, 2012, pp. 1–4
  4. Anuwatvisit S, Tungkasthan A, Premchaiswadi W., “Bottleneck mining and Petri net simulation in education situations. In: ICT and Knowledge Engineering (ICT & Knowledge Engineering), 10th International Conference. IEEE; Bangkok, Thailand; 2012, 244–251.
  5. Mohammed M. Abu Tair, Alaa M. El-Halees, Mining Educational Data to Improve Students’ Performance: A Case Study, International Journal of Information and Communication Technology Research, ISSN 2223-4985, Volume 2 No. 2, February 2012.
  6. Edin Osmanbegović and Mirza Suljic, Data Mining Approach For Predicting Student Performance, Economic Review – Journal of Economics and Business, Vol. X, Issue 1, May 2012.
  7. of Education Malaysia, National higher education strategic plan (2015). URL http://www.moe.gov.my/v/pelan-pembangunan-pendidikan-malaysia-2013-2025
  8. bin Mat, N. Buniyamin, P. M. Arsad, R. Kassim, An overview of using academic analytics to predict and improve students’ achievement: A proposed proactive intelligent intervention, in: Engineering Education (ICEED), 2013 IEEE 5th Conference on, IEEE, 2013, pp. 126–130.
  9. M. D. Angeline, Association rule generation for student performance analysis using apriori algorithm, The SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 1 (1) (2013) p12–16.
  10. F. Li, D. Rusk, F. Song, Predicting student academic performance, in Complex, Intelligent, and Software Intensive Systems (CISIS), 2013 Seventh International Conference on, IEEE, 2013, pp. 27–33.
  11. Romero, M.-I. L´opez, J.-M. Luna, S. Ventura, Predicting students’ final performance from participation in on-line discussion forums, Computers & Education 68 (2013) 458–472.
  12. Ramesh, P. Parkavi, K. Ramar, Predicting student performance: a statistical and data mining approach, International Journal of Computer Applications 63 (8) (2013) 35–39.
  13. M. Arsad, N. Buniyamin, J.-l. A. Manan, A neural network students' performance prediction model (nnsppm), in Smart Instrumentation, Measurement and Applications (ICSIMA), 2013 IEEE International Conference on, IEEE, 2013, pp. 1–5.
  14. Elakia, N. J. Aarthi, Application of data mining in the educational database for predicting behavioural patterns of the students, Elakia et al,/(IJCSIT) International Journal of Computer Science and Information Technologies 5 (3) (2014) 4649–4652.
  15. Mishra, D. Kumar, S. Gupta, Mining students' data for prediction performance, in Proceedings of the 2014 Fourth International Conference on Advanced Computing & Communication Technologies, ACCT '14, IEEE Computer Society, Washington, DC, USA, 2014, pp. 255–262. doi:10.1109/ACCT.2014.105. URL http://dx.doi.org/10.1109/ACCT.2014.105
  16. Natek, M. Zwilling, Student data mining solution–knowledge management system related to higher education institutions, Expert systems with applications 41 (14) (2014) 6400–6407.
  17. Raheela Asif, Agathe Merceron, Mahmood K. Pathan, Predicting Student Academic Performance at Degree Level: A Case Study, I.J. Intelligent Systems and Applications, 2015, 01, 49-61 Published Online December 2014 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2015.01.05.
  18. Bogar´ın, C. Romero, R. Cerezo, M. S´anchez-Santill´an Clustering for improving educational process mining, in Proceedings of the Fourth International Conference on Learning Analytics And Knowledge, ACM, 2014, pp. 11–15.
  19. Gray, C. McGuinness, P. Owende, An application of classification models to predict learner progression in tertiary education, in Advance Computing Conference (IACC), 2014 IEEE International, IEEE, 2014, pp. 549–554.
  20. T. Jishan, R. I. Rashu, N. Haque, R. M. Rahman, Improving accuracy of student’s final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique, Decision Analytics 2 (1) (2015) 1–25.
  21. Randa Kh. Hemaid and Alaa M. El-Halees, Improving Teacher Performance using Data Mining, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 2, February 2015.
  22. Fadhilah Ahmad, Nur Hafieza Ismail and Azwa Abdul Aziz, The Prediction of Students’ Academic Performance Using Classification Data Mining Techniques, Applied Mathematical Sciences, Vol. 9, 2015, no. 129, 6415 - 6426HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2015.53289.
  23. Mashael A. Al-Barrak and Mona S. Al-Razgan, predicting students’ performance through classification: a case study, Journal of Theoretical and Applied Information Technology 20th May 2015. Vol.75. No.2.
  24. Kolo David Kolo, Solomon A. Adepoju, John Kolo Alhassan, A Decision Tree Approach for Predicting Students Academic Performance, I.J. Education and Management Engineering, 2015, 5, 12-19 Published Online October 2015 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijeme.2015.05.02.
  25. Mohammed I. Al-Twijri and Amin Y. Noaman, A New Data Mining Model Adopted for Higher Institutions, Procedia Computer Science 65 ( 2015 ) 836 – 844, doi: 10.1016/j.procs.2015.09.037.
  26. Mihai Dascalu and Elvira Popescu et. al., Predicting Academic Performance Based on Students’ Blog and Microblog Posts, Springer International Publishing Switzerland 2016 K. Verbert et al. (Eds.): EC-TEL 2016, LNCS 9891, pp. 370–376, 2016. DOI: 10.1007/978-3-319-45153-4_29.
  27. Mrinal Pandey and S. Taruna, Towards the integration of multiple classifiers pertaining to the Student's performance prediction, http://dx.doi.org/10.1016/j.pisc.2016.04.076 2213-0209/© 2016 Published by Elsevier GmbH. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
  28. Mukesh Kumar, Shankar Shambhu & Punam Aggarwal, “Recognition of Slow Learners Using Classification Data Mining Techniques” Imperial Journal of Interdisciplinary Research (IJIR) Vol-2, Issue-12, 2016 ISSN: 2454-1362, http://www.onlinejournal.in
  29. Doleck T, Jarrell A, Poitras EG, Chaouachi M, Lajoie SP. Examining diagnosis paths: a process mining approach. In: Computational Intelligence & Communication Technology (CICT), Second International Conference. IEEE, Ghaziabad, India, 2016, 663–667.
  30. Vidal JC, Vázquez-Barreiros B, Lama M, Mucientes M. Recompiling learning processes from event logs. Knowledge-Based Syst 2016, 100:160–174.
  31. Julia Rudnitckaia. Process Mining: Data Science in Action. Berlin, Germany: Springer; 2016.
  32. Romero C, Cerezo R, Bogarín A, Sánchez-Santillán M. Educational process mining: a tutorial and case study using moodle data sets. In: Data Mining and Learning Analytics: Applications in Educational Research. Hoboken, NJ: John Wiley & Sons; 2016, 1–28.
  33. Raheela Asif, Saman Hina and Saba, "Predicting Student Academic Performance using Data Mining Methods", International Journal of Computer Science and Network Security (IJCSNS), VOL.17, 2017.
  34. Ricardo Mendes And Joao P. Vilela, “Privacy-Preserving Data Mining: Methods, Metrics, and Applications", IEEE, 2017.
  35. Schulte J, Fernandez de Mendonca P, Martinez- Maldonado R, Buckingham Shum S. Large-scale predictive process mining and analytics of university degree course data. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference. ACM; Vancouver, Canada; 2017, 538–539.
  36. Mukesh Kumar, A.J. Singh, Disha Handa, "Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques", International Journal of Education and Management Engineering(IJEME), Vol.7, No.6, pp.40-49, 2017.DOI: 10.5815/ijeme.2017.06.05
  37. Mukesh Kumar, A.J. Singh, “Evaluation of Data Mining Techniques for Predicting Student’s Performance", International Journal of Modern Education and Computer Science (IJMECS), Vol.9, No.8, pp.25-31, 2017.DOI: 10.5815/ijmecs.2017.08.04
  38. Febrianti Widyahastuti, Viany Utami Tjhin, "Predicting Students Performance in Final Examination using Linear Regression and Multilayer Perceptron", IEEE, 2017.
  39. Sumitha and E.S. Vinoth Kumar, Prediction of Students Outcome Using Data Mining Techniques, International Journal of Scientific Engineering and Applied Science (IJSEAS) – Volume-2, Issue-6, June 2016 ISSN: 2395-3470.
  40. Azwa Abdul Aziz, Nor Hafieza Ismailand Fadhilah Ahmad, First Semester Computer Science Student's Academic Performances Analysis by Using Data Mining Classification Algorithms, Proceeding of the International Conference on Artificial Intelligence and Computer Science(AICS 2014), 15 - 16 September 2014, Bandung, INDONESIA. (e-ISBN978-967-11768-8-7).
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  42. López, M. I., Luna, J. M., Romero, C., & Ventura, S. (2012). Classification via clustering for predicting final marks based on student participation in forums. International Educational Data Mining Society. 148-151.
  43. A. Oloruntoba, J. L. Akinode, "Student Academic Performance Prediction Using Support Vector Machine" International Journal of Engineering Sciences & Research Technology, 2016, DOI: 10.5281/zenodo.1130905, ISSN: 2277-9655.
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  46. B. Eashwar, R. Venkatesan, " Student Performance Prediction Using SVM", International Journal of Mechanical Engineering and Technology (IJMET), Volume 8, Issue 11, November 2017, pp. 649–662, Article ID: IJMET_08_11_066
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12.

Authors:

Elena Yurevna Avksentieva, Yuri Alekseevich Senterev, Vladimir Aleksandrovich Kostezh, Svetlana Mikhailovna Platunova

Paper Title:

Application of Cloud and Fog Computing in Educational Process upon Implementation of Master's Degree Program

Abstract: This article discusses a variant of application of cloud and fog computing for training process upon implementation of educational programs on the basis of e-learning courses (ELC) aiming at replacement of conventional courses presented by human teachers by courses based on innovative technologies. The arguments are adduced that application of cloud services and fog nodes upon the use of ELC in training process provides wider possibilities for delivery of educational content in comparison with conventional learning. Solutions are proposed capable to expand application of approaches to the use of cloud and fog computing in educational process.

Keywords: training process, cloud computing, fog computing, server cluster, private cloud, educational service, hybrid architecture. 

References:

  1. Yu. Avksent'eva, “Infrastruktura oblachnykh vychislenii dlya elektronnogo obucheniya” [Infrastructure of cloud computing for e-learning], Sovremennoe obrazovanie: traditsii i innovatsii, 3, 2016, p. 55-61.
  2. Yu. Avksent'eva, “Proektirovanie setevoi infrastruktury dlya razvertyvaniya oblachnykh servisov vuza” [Designing network infrastructure for deployment of cloud services of higher school], Sovremennoe obrazovanie: traditsii i innovatsii, 4, 2016, p. 158-162.
  3. E. Yu. Avksent'eva, S. Yu. Avksent'ev, “Model' arkhitektury "chastnogo oblaka" vuza” [Model of private cloud architecture], E-LEARNING IN higher AND SECONDARY SCHOOL, Proceedings of online international workshop, 2015, p. 8-10.

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13.

Authors:

Harish Baraithiya, R. K. Pateriya

Paper Title:

An Analysis of Hybrid Authentication and Authorization Model for Web based Application

Abstract: In the recent time many websites performing different task and there is need to every user having separate access credentials to each website. Every user is needed to remember and maintain the every user id and its password that is related website. There is always requirement to secure access the personal information and safe from malicious insiders. So there is always needed to manage only authenticated user can access only authorized data not the other. The present models having some limitations but the proposed model is useful for the analysis of some authentication and authorization for the secure the website in user friendly environment. Here it is follow the Single Sign On mechanism which handle the comparison with many access control models and its features in the proposed hybrid model.

Keywords: Web Usage Mining, Authentication, Authorization, Web Access control, Web-based Applications, Security.

References:

  1. Marc Andre Laverdiere, “Computing Counter-Examples for Privilege Protection Losses using Security Models”, IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), pp. 240-249, 2017.
  2. Weili Han, “Regional Patterns and Vulnerability Analysis of Chinese Web Passwords”, IEEE Transactions on Information Forensics and Security, Vol. 11, No.2, pp. 258-272, 2016.
  3. Amina Bourouis, “On the Verification of Opacity in Web Services and their Composition”, IEEE Transactions on Services Computing, Vol. 10, Issue 1, pp. 66-79, 2017.
  4. Lin Liu, “A Website Security Risk Assessment Method based on the I-BAG Model”, IEEE China Communications, Vol. 13, Issue 5, pp. 172-181, 2016.
  5. Mingqiang Li, “CDStore: Toward Reliable, Secure, and Cost-Efficient Cloud Storage via Convergent Dispersal”, IEEE, Vol. 20, Issue 3, pp. 45-53, May 2016.
  6. M. Rubesh Anand, “Hybrid Authentication and Authorization Model for Web based Applications”, IEEE WiSPNET, pp. 1187-1191, 2016.
  7. Joseph K. Liu, “Fine-grained Two-factor Access Control for Web-based Cloud Computing Services”, IEEE Transactions on Information Forensics and Security, Vol. 11, Issue 3, pp. 484-497, 2016.
  8. Wei She, “Role Based Integrated Access Control and Data Provenance for SOA Based Net Centric Systems”, IEEE Transactions on Services Computing, Vol. 9, Issue 6, pp. 940-953, 2016.
  9. Siam U. Hussain, “A Built-In-Self-Test Scheme for Online Evaluation of Physical Unclonable Functions and True Random Number Generators”, IEEE Transactions on Multi-Scale Computing Systems, Vol. 2, Issue 1, pp. 1-15, 2016.
  10. Asrar Ashraf, “A Heterogeneous Service-Oriented Deep Packet Inspection and Analysis Framework for Traffic-Aware Network Management and Security Systems”, IEEE Access, Vol. 4, pp. 5918-5936, 2016.
  11. Lin Cui, “PLAN-Joint Policy- and Network-Aware VM Management for Cloud Data Centers”, IEEE Transactions on Parallel and Distributed Systems, Vol. 28, Issue 4, pp. 1163-1175, 2017.

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14.

Authors:

Sushma Kakkar, Tanmoy Maity, Rajesh Kumar Ahuja

Paper Title:

Performance Enhancement of Grid connected PWM Rectifier under Grid Disturbances

Abstract: The voltage distribution system may be disturbed in spite of a stiff system. The front end rectifiers should be controlled to provide immunity to such disturbances. In this paper, virtual flux oriented control algorithm for three phase active rectifier under distorted and unbalanced grid conditions is presented. No voltage measurement is required to implement this control scheme. In spite the grid virtual flux is estimated. The control is based on grid virtual flux estimation without ac voltage sensors. The LCL filters have been acknowledged for reduction in harmonics at switching frequency. However the selection of LCL parameters is tedious task. A complete design for the active rectifier and LCL filter has been given in detail. The virtual flux is utilized to get the voltage and instantaneous power estimation. As the flux is less sensitive to grid voltage disturbances so it provided more robust control of the converter. The control algorithm is simulated in MATAB under two conditions: (a) normal grid voltage and (b) unbalanced and distorted grid voltage. The results demonstrate the balanced and sinusoidal grid current even under grid unbalanced and distorted grid voltages.

Keywords: Harmonic distortion, LCL filters, Power quality, PWM Rectifier, VFOC.

References:

  1. P. Stratford. 1980.Harmonic Pollution on Power Systems- A Change in Philosophy. IEEE Transactions on Industry Applications. Vol. IA-16, No.5, pp.43-49.
  2. M. Peeran. and C.W.P. CascaddenApplication, design, and specification of harmonic filters for variable frequency drives. IEEE Conf .APEC’94, pp. 909-916.
  3. R. Rodriguez, J. W. Dixon, J. R. Espinoza, J. Pontt, P. Lezana.2005.PWM regenerative rectifiers: state of the art. IEEE Transactions on Industrial Electronics, Vol. 52, No. 1, pp. 5-22.
  4. Malinowski, M., Kazmierkowski, M. P., &Trzynadlowski, A. Review and comparative study of control techniques for three-phase PWM rectifiers.Mathematics and Computers in Simulation, Vol.63, No.3, pp. 349-361.
  5. Kazmierkowski, M. P. 2002.Direct power control of three-phase PWM rectifier using space vector modulation-simulation study. IEEE International Symposium on Industrial Electronics, Vol. 4, pp. 1114-1118.
  6. Lalili, D., Mellit, A., Lourci, N., Medjahed, B., &Berkouk, E. M. 2011.Input output feedback linearization control and variable step size MPPT algorithm of a grid-connected photovoltaic inverter. Elsevier Journal onRenewable energy,36, No.12, pp.3282-329.
  7. Kazmierkowski M.P. and Malesani L.1998.Current control techniques for three-phase voltage-source PWM converters: a Survey. IEEE Transactions on Industrial Electronics, Vol.45,No.5,pp.691-703.
  8. Mustapha Jamma, Mohamed Barara and Bogdan-Adrian Enache.2016. Voltage oriented control of three-phase PWM  rectifier using space vector modulation and input output feedback linearization theory. Intr Conf 8th Edition  ECAI 2016.
  9. Noguchi, H. Tomiki, S. Kondo, I. Takahashi. 1998.Direct power control of PWM converter without power-source voltage sensors”,IEEE Transactions on Industry Applications, Vol. 34, No. 3, pp. 473- 479.
  10. Habetler, F. Profumo, M. Pastorelli, L. Tolbert.1992.Direct torque control of induction machines using space vector modulation”, IEEE Transactions on Industry Applications, Vol. 28, No. 5, pp. 1045-1052.
  11. Liserre M., Dell’Aquila A., Blaabjerg F.2002.Stability improvements of an LCL-filter based three-phase active rectifier. IEEE 33rd Annual Power Electronics Specialists Conference, vol 3, pp. 1195-1201.
  12. Twining, Holmes D. G.2003.Grid current regulation of a three-phase voltage source inverter with an LCL input filter,” IEEE Transactions on Power Electronics, vol. 18, No.3, pp. 888-895.
  13. Hea-Gwang Jeong, Kyo-Beum Lee, Sewan Choi, and Woojin Choi.2010.Performance Improvement of LCL-Filter-Based Grid-Connected Inverters Using PQR Power Transformation. IEEE Transactions on Power Electronics, 25, No. 5, pp.1320-1330.
  14. Azziddin M. Razali, M. A. Rahman, Glyn George and Nasrudin A. Rahim. 2015.Analysis and Design of New Switching Lookup Table for Virtual Flux Direct Power Control of Grid-Connected Three-Phase PWM AC–DC Converter. IEEE Transactions on Industry Applications, Vol. 51, No. 2, pp. 1189-1199.
  15. P.Kazmierkowski, R. Krishnan and F.Blaabjerg, 2002.Control in power electronics (selected problems), Academic press, USA.
  16. Marco Liserre, Frede Blaabjerg, Steffan Hansen. 2005.Design and Control of an LCL-Filter-Based Three-Phase Active Rectifier. IEEE Transactions on Industry Applications, Vol. 41, No. 5pp.1281-129
  17. Sushma Kakkar, Tanmoy Maity and Rajesh Kumar Ahuja, “Power Quality Improvement of PWM Rectifier Using VFOC and LCL Filter” in proc. ICPCSI-2017,pp. 1036-1040.

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15.

Authors:

Mukesh Kumar, A. J. Singh

Paper Title:

Performance Analysis of Students Using Machine Learning & Data Mining Approach

Abstract: Performance evaluation of students is essential to check the feasibility of improvement. Regular evaluation not only improves the performance of the student but also it helps in understanding where the student is lacking. It takes a lot of manual effort to complete the evaluation process as even one college may contain thousands of students. This paper proposed an automated solution for the performance evaluation of the students using machine learning. A threshold-based segmentation is employed to complete the evaluation procedure over MATLAB simulation tool. The performance of machine learning is evaluated by accuracy and mean square error.

Keywords: Performance Evaluation, Machine Learning, Performance Improvement, MATLAB, Mean Square Error, Estimation Effort

References:

  1. Ramanathan L., Parthasarathy G., Vijayakumar, K., Lakshmanan, L., & Ramani, S. (2018). Cluster-based distributed architecture for prediction of student’s performance in higher education. Cluster Computing, 1-16.
  2. Thomas, C. L., Cassady, J. C., & Heller, M. L. (2017). The influence of emotional intelligence, cognitive test anxiety, and coping strategies on undergraduate academic performance. Learning and Individual Differences, 55, 40-48.
  3. Keyes K., & Dworak E. (2017). Staffing Chat Reference with Undergraduate Student Assistants at an Academic Library: A Standards-Based Assessment. The Journal of Academic Librarianship, 43(6), 469-478.
  4. Yahya A. A. (2017). Swarm intelligence-based approach for educational data classification. Journal of King Saud University-Computer and Information Sciences.
  5. Bharara S., Sabitha S., & Bansal A. (2017). Application of learning analytics using clustering data Mining for Students’ disposition analysis. Education and Information Technologies, 1-28.
  6. Brown S., Bowmar A., White, S., & Power, N. (2017). Evaluation of an instrument to measure undergraduate nursing student engagement in an introductory Human anatomy and physiology course. Collegian, 24(5), 491-497.
  7. Ojha A. K. (2017). Management education in India: avoiding the simulacra effect. In Management Education in India (pp. 55-77). Springer, Singapore.
  8. Asif R., Merceron A., & Pathan M. K. (2015, March). Investigating performance of students: a longitudinal study. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 108-112). ACM.
  9. Pandey M., & Taruna S. (2016). Towards the integration of multiple classifier pertaining to the Student's performance prediction. Perspectives in Science, 8, 364-366.
  10. Almutairi F. M., Sidiropoulos N. D., & Karypis G. (2017). Context-aware recommendation-based learning analytics using tensor and coupled matrix factorization. IEEE Journal of Selected Topics in Signal Processing, 11(5), 729-741.
  11. Oskouei R. J., & Askari M. (2014). Predicting academic performance with applying data mining techniques (Generalizing the results of two different case studies). Computer Engineering and Applications Journal, 3(2), 79.
  12. Wook M., Yusof Z. M., & Nazri M. Z. A. (2017). Educational data mining acceptance among undergraduate students.
  13. Education and Information Technologies, 22(3), 1195-1216.
  14. Hussain M., Al-Mourad M., Mathew S., & Hussein A. (2017). Mining educational data for academic accreditation: Aligning assessment with outcomes. Global Journal of Flexible Systems Management, 18(1), 51-60.
  15. Costa E. B., Fonseca B., Santana M. A., de Araújo F. F., & Rego J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses. Computers in Human Behavior, 73, 247-256.
  16. Polyzou A., & Karypis G. (2016, April). Grade prediction with course and student specific models. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 89-101). Springer, Cham
  17. Wisneski J. E., Ozogul G., & Bichelmeyer B. A. (2017). Investigating the impact of learning environments on undergraduate students' academic performance in a prerequisite and post-requisite course sequence. The Internet and Higher Education, 32, 1-10.
  18. Ghani A. A., & Mohamed R. (2017). The Effect of Entry Requirement for Civil Engineering Student Performance. Journal of Science and Technology, 9(4).
  19. Chakraborty T., Chattopadhyay S., & Chakraborty, A. K. A novel hybridization of classification trees and artificial neural networks for selection of students in a business school. OPSEARCH, 1-13.
  20. Kumar M., & Singh A. J. (2017). Evaluation of Data Mining Techniques for Predicting Student’s Performance. International Journal of Modern Education and Computer Science, 9(8), 25.
  21. Meedech P., Iam-On N., & Boongoen T. (2016). Prediction of student dropout using personal profile and data mining approach. In Intelligent and Evolutionary Systems (pp. 143-155). Springer, Cham.
  22. Yehuala M. A. (2015). Application of Data Mining Techniques For Student Success And Failure Prediction (The Case Of Debre_Markos University). International Journal of Scientific & Technology Research, 4(4), 91-94.
  23. Asif R., Haider N. G., & Ali S. A. (2016). Prediction of Undergraduate Student's Performance using Data Mining Methods. International Journal of Computer Science and Information Security, 14(5), 374.
  24. Tran T. O., Dang H. T., Dinh, V. T., & Phan, X. H. (2017). Performance Prediction for Students: A Multi-Strategy Approach. Cybernetics and Information Technologies, 17(2), 164-182.
  25. Kim K. (2016). A hybrid classification algorithm by subspace partitioning through semi-supervised decision tree. Pattern Recognition, 60, 157-163.
  26. Kumar M., Singh A.J. , Handa D.,"Literature Survey on Student’s Performance Prediction in Education using Data Mining Techniques", International Journal of Education and Management Engineering(IJEME), Vol.7, No.6, pp.40-49, 2017.DOI: 10.5815/ijeme.2017.06.05
  27. Guarín C. E. L., Guzmán E. L., & González, F. A. (2015). A model to predict low academic performance at a specific enrollment using data mining. IEEE Revista Iberoamericana de tecnologias del Aprendizaje, 10(3), 119-125.
  28. Kumar M., Singh A. J., Handa D."Literature Survey on Educational Dropout Prediction", International Journal of Education and Management Engineering (IJEME), Vol.7, No.2, pp.8-19, 2017.DOI: 10.5815/ijeme.2017.02.02.

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16.

Authors:

Muratbai Zhanaidarovich Ryskaliyev, Sabit Muratovich Zharylgapov, Nargul Amanovna Saktaganova, Ulbossyn Zhangabilkyzy Sarabekova, Koktem Akarysovich Yerimbetov

Paper Title:

Physico-Mechanical Properties of Foam Concrete with a Keratin-Based Foaming Agent

Abstract: The work shows that the technology for producing keratin-based foaming agent, in terms of its physicochemical characteristics, is highly competitive with other foam concrete with another foaming agent. The study describes the compositions of foam concrete on the basis of a keratin-based foaming agent and its basic physicomechanical properties. The effect of the keratin-based foaming agent in terms of increasing the timing of the beginning and end of the cement setting in cement systems is less than in traditional foam concrete. This is explained by the presence of the complex additive MB-01 and Aquatron-6, which seal the Plato channels and contribute to retaining more water in the foaming agent films. A complex of technological methods for the use of keratin raw materials and calcium hydroxide for its hydrolysis, as well as the mineral-chemical additives MB-01 and Aquatron-6, made it possible to produce foam concrete with given properties of strength and density.

Keywords: keratin raw material, hydrolysis mode, hydrolyzate neutralization, surface tension, sublimation, foam concentrate. 

References:

  1. Ramamurthy, E.K. Nambiar, G. Kunhanandan Ranjani, “A classification of studies on properties of foam concrete”, Building Technology and Construction Management Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600036, India, Indu Siva Cement & Concrete Composites, 31(6), 2009, p. 388-396.
  2. Jiang, Zh. Lu, Yu. Niua, "Investigation of the properties of high-porosity cement foams based on ternary Portland cement–metakaolin–silica fume blends", Construction and Building Materials, 107, 2016, p. 181-190.
  3. H.M. Amran, A.A. Abang Ali, Raizal S.M. Rashid, et al., "Structural behavior of axially loaded precast foamed concrete sandwich panels", Construction and Building Materials, 107, 2016, 307-320.
  4. S. Shintemirov, S.A. Montaev, M.Zh. Ryskaliyev, A.A. Bakushev, K.A. Narikov, “Investigation into the Properties of Foamed Concrete Modified by Chemical Additives”, Research Journal of Pharmaceutical, Biological and Chemical Sciences (RJPBCS), 7(3), 2016, p. 2065-2072.
  5. V. Korotyshevskiy, “Novaya resursosberegayushchaya tekhnologiya po proizvodstvu vysokoeffektivnykh penobetonov” [New resource-saving technology for the production of highly efficient foam concrete], Stroitelnye materialy, 2, 1999, p. 32-33.
  6. A. Akhundov, “Razvitie individualnogo zhilishchnogo stroitelstva kak rychag podema ekonomiki strany” [The development of individual housing construction as a lever for boosting the country's economy], Stroitelnye materialy, 4, 1998, p. 27-28.
  7. A. Akhundov, “Penobeton – effektivnyy stenovoy i teploizolyatsionnyy material” [Foam concrete – effective walling and heat-insulating material], Stroitelnye materialy, 1, 1998, p. 9-10.
  8. A. Martynenko, “Vliyanie tekhnologicheskikh parametrov na svoystva teploizolyatsionnogo penobetona” [The influence of technological parameters on the properties of insulating foam concrete], Dnepropetrovsk, PGASA, Vestnik pridneprovskoy akademii stroitelstva i arkhitektury, 5, 2002, p. 41-50.
  9. G. Granik, “Teploeffektivnye ograzhdayushchie konstruktsii zhilykh i grazhdanskikh zdaniy” [Heat-efficient enclosing structures of residential and civil buildings], Stroitelnye materialy, 2, 1999, p. 4-6.
  10. P. Sakharov, “Effektivnye materialy s povyshennymi teplozashchitnymi i stroitelno-ekspluatatsionnymi svoystvami” [Effective materials with enhanced heat-shielding and building-operational properties], Moscow, MGUPS, Novoe v stroitelnom materialovedenii, Yubileynyy sbornik kafedry «Stroitelnye materialy i tekhnologii», 1997, p. 74.
  11. A. Gusenkov, “Proizvodstvo penobetona v Rossii” [Production of foam concrete in Russia], Stroitelnye materialy, oborudovanie, tekhnologii XXI veka, 3, 2001, p. 20-21.
  12. I. Kharkhardin, L.S. Vesnin, “Opyt osvoeniya massovogo proizvodstva penobetonnykh izdeliy” [Experience in mastering the mass production of foam concrete products], Stroitelnye materialy, 2, 1999, p. 30-31.
  13. S. Shintemirov, M.Zh. Ryskaliev, “Tekhnologiya keratinovogo penoobrazovatelya dlya proizvodstva penobetonov” [Technology of keratin foaming agent for the production of foam concrete], Penza, PGASU, Collection of works, IX International Conference of Young Scientists "Theory and practice of improving the efficiency of building materials", 2014, p. 84-88.
  14. A. Trapeznikov, “Nekotorye svoystva plenok i pen i voprosy ikh ustoychivosti. Peny. Poluchenie i primenenie” [Some properties of films and foams and issues of their stability. Foams. Receipt and application], Moscow, Materialy Vserossiyskoy nauchno-tekhnicheskoy konferentsii, Part I. Fiziko-khimiya pen, 1974, p. 6-37.
  15. Th. Nemetschek, “Kolloid-Zeitschrift und Zeitschrift fur Poymere”, Springer, 187, 2, 1963, p. 109.

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17.

Authors:

Oksana Olegovna Gorshkova, Vladimir Ivanovich Nekrasov

Paper Title:

Five-Stage Electric Starter

Abstract: This article classifies and analyzes the existing electric starters, their advantages and disadvantages. A five-stage starter is proposed which improves performances of engines of land vehicles. Operation of the five-stage electric starter is discussed, ray path diagrams are presented for its two internal parameters: K = 4 and K = 1.62, variation of the internal parameter K leads to variation of kinematic properties of the electric starter with planetary reducer. The five-stage starter is intended for expansion of performances of land vehicles. The formulated target is achieved by starting cold engine with high viscosity motor oil at low rpm, and by increase in rpm of crankshaft for preliminary oil supply to friction units before engine start which takes place under optimum conditions.

Keywords: five-stage electric starter, planetary mechanism, planetary reducer, engines of land vehicles. 

References:

  1. V. Akimov, Yu.P. Chizhkov, “Elektrooborudovanie avtomobilei” Vehicle electrical system], Moscow, Guidebook: Za rulem, 2001.
  2. N. Vishnyakov, V.K. Vakhlamov, A.N. Narbut, “Avtomobil`: osnovy konstruktsii” [Vehicle: basic design], Moscow, Guidebook: Mashinostroenie, 2-nd revised edition, 1986.
  3. A. Mozharov, O.A. Korobova, V.A. Petrov et al., “Starter for internal combustion engine”, Author's Certificate №1420231, Bulletin, №32, August 30, 1988.
  4. I. Nekrasov, O.O. Gorshkova, “Two-mode electric starter”, Patent RU № 2567847.
  5. I. Nekrasov, O.O. Gorshkova, “Five-stage electric starter”, Patent RU № 2624778.
  6. A. Vinogradov, S.V. Solov'ev, “Starter with planetary reducer”, Russian patent № 2092712.
  7. A.M. Litvinenko, A.V. Zhilkin, “Starter with planetary reducer”, Patent RU № 2560932.

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18.

Authors:

Kahkashan Kouser, Amrita Priyam

Paper Title:

Comparison between Subspace and onventional clustering for High Dimensional Data Analysis

Abstract: Clustering High dimensional data is a propitious research area in current scenario. Now it becomes a crucial task to cluster multi-dimensional dataset as data-objects are largely dispersed in multi-dimensional space. Most of the conventional algorithms for clustering work on all dimensions of the feature space for calculating clusters. Whereas only few attributes are relevant. Thus their performance is not very Precise. A modified subspace clustering is proposed in this research paper, which does not use all attributes of high-dimensional feature space simultaneously rather, it determine a subspace of attributes which are important for each individual cluster. This subspace of attributes may be same or different for the different cluster. The comparison between conventional K-Means and modified subspace K-means clustering algorithms were done based on various validation matrices. Results of the modified subspace clustering is compared with the conventional clustering algorithm. It was analyzed based on different matrices such as SSE(sum of squared error), WGAD-BGD (Within group average distance minus between group distances) and DBI(Davies-Bouldin index) or Validity index. Artificial data set were used for all the experiments. Results represent the better efficiency and feasibility of modified subspace clustering algorithm over conventional clustering methods.

Keywords: clustering, high-dimensional data, Subspace clustering, COSA, clustering on subset of attribute

References:

  1. J.Sun,L.H.Xiong ,Genetic Algorithm based high dimensional data clustering techniques. IEEE 2009 Sixth International Conference on Fuzzy System anf Knowledge Discovery.
  2. Anusha ,J.G.R. Sathiaseelan , Feature Selection using K-Means Genetic Algorithm forMulti-objective Optimization .Procedia Computer Science 57 ( 2015 ) 1074 – 1080.
  3. Z, Deng, K.C.Choi, Y. Jiang, J. Wang, S. Wang, A Survey on Soft Subspace Clustering, Information Sciences: an International Journal, June 2016, pp. 84-106
  4. Surekha, S. Anuradha, B. Jaya Lakshmi, B. Madhuri , A survey on hard subspace clustering algorithms ,International Journal of Science & Engineering Development Research, August 2016.
  5. Jahirabadkar, P. Kulkarni , Clustering for High Dimensional Data: Density Based Subspace Clustering Algorithms, International Journal of Computer Applications, February 2013.
  6. H. Friedman and J. J. Meulman. Clustering objects on subsets of attributes.  Journal of the Royal Statistical
  7. Society: Series B (Statistical Methodology), 66(4):825-849, 2004.
  8. S. Singh, N. C. Chauhan, K-means v/s K-medoids: A Comparative Study,  National conference on Recent Trends in Engineering &Technology, Jan2014
  9. Chaimontree, K. Atkinson, F. Coenen, Best Clustering Configuration Metrics: Towards Multiagent Based Clusterin. , Advanced Data Mining and Applications, Volume 6440 of the series Lecture Notes in Computer Science pp 48-59.
  10. Liu, J.Zhang, J.Xiao, H.Zhu, Q.Zhao A Supervised Feature Selection Algorithm through Minimum Spanning Tree Clustering, 2014 IEEE 26th International Conference on Tools with Artificial Intelligence.
  11. Manolis C. Tsakiris and Ren´e Vidal “Filtrated Spectral Algebraic Subspace Clustering” 2015 IEEE International Conference on Computer Vision Workshops.
  12. C. Tsakiris and R. Vidal. Abstract algebraic subspace clustering. CoRR, ABS/1506.06289, 2015.
  13. Hongchang Gao, Feiping Nie, Xuelong Li, Heng Huang,Multi-View Subspace Clustering. 2015 IEEE International Conference on Computer Vision.
  14. Castro,X.Pu A simple approach to sparse clusterin, Computational Statistics & Data Analysis
  15. Volume 105 Issue C, January 2017 Pages 217-228
  16. P.Kriegel, E.Ntoutsi .Clustering high dimensional data: Examining differences and commonalities between subspace clustering and text clustering A position pape. SIGKDD Explorations Volume 15, Issue 2.
  17. Han and M. Kamber. Data Mining: Concepts and Techniques. Academic Press, 2nd edition, 2006.
  18. https://en.wikipedia.org/wiki/Clustering high-dimensional_data

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19.

Authors:

Boobalan S C, Nikeath D, Pragadesh M

Paper Title:

Experimental Study on Concrete by using Manufactured Sand – A General Review

Abstract: This review paper gives an idea about the various studies on concrete by replacing the natural river sand into varying percentages of manufactured sand. Nowadays due to the environmental considerations and scarcity of natural river sand, we have to find out the suitable alternate material similar to those properties. Various research works have been going on for finding the suitable material, from that one such material is Manufactured Sand. Due to the high angularity of manufactured sand particles, higher water-cement ratio is required for maintaining the workability as compared to the natural river sand concrete. Concrete properties discussed in this paper are mechanical properties, workability and durability. Finally, conclusion has been made by quoting suitable percentage replacement of manufactured sand is feasible in concrete making process. 

Keywords: Manufactured Sand, Mechanical Properties, River sand.

References:

  1. Elavenil and B. Vijaya, “Manufactured Sand, A solution and an alternative to river sand and in concrete manufacturing”, Journal of Engineering, Computers & Applied Sciences, 2(2), 2013, pp. 20-24.
  2. Panimayam, P. Chinnadurai, R. Anuradha, M. Rajalingam, Ajith Raj and Godwin, “Experimental study of Pervious Concrete using M-Sand”, International Journal of Chemtech Research, 10(8), 2017, pp. 186-198.
  3. Proyanka A. Jadhav and Dilip K. Kulkarni, “An Experimental investigation on the properties of Concrete containing Manufactured Sand”, International Journal of Advanced Engineering Technology, 3(2), 2012, pp. 101-104.
  4. Suresh and J. Revathi, “An experimental investigation on effect of High Strength Concrete using Manufactured Sand”, International Journal of Innovative Research in Science, Engineering and Technology, 5(2), 2016, pp. 2135-2140.
  5. Umamaheswaran, C. Sudha, P. T. Ravichandran and P. R. Kannan Rajkumar, “Use of M Sand in High Performance Concrete”, Indian Journal of Science and Technology, 8(28), 2015, pp. 1-8.
  6. ZHOU Mingkai, WANG Jiliang, ZHU Lide and H.E. Tusheng, “Effects of Manufactured sand on Dry shrinkage and Creep of High Strength Concrete”, Journal of Wuhan University of Technology – Mater. Sci. Ed., 23(2), 2008, pp. 249-252.
  7. Adams Joe, A. Maria Rajesh, P. Brightson and M. Prem Anand, “Experimental investigation on the effectd of M-Sand in High Performance Concrete”, American Journal of Engineering Research, 2(12), 2013, pp. 46-51.
  8. Suseela and T. Baskaran, “Stregnth analysis on concrete with M-Sand as a partial replacement of fine aggregate”, International Journal of Civil Engineering and Technology, 8(12), 2017, pp.583-592.
  9. Kiran M. Mane and Dilip K. Kulkarni, “Strength and Workability of Concrete with Manufactured Sand”, International Journal of Engineering and Tecchnology, 10(1), 2017, pp.331-335.
  10. Sagura and R. Jagadeesan, “Experimental Study on mechanical properties of M-Sand concrete by different curing methods”, IOSR Journal of Mechnical and Civil Engineering, pp. 19-25.
  11. Meghashree and Arpitha K. Gowda, “Comparison of Physical Properties between Natural sand and Manufactured sand”, International Journal for Innovative Research in Science and Technology, 3(7), 2016, pp. 92-96.
  12. IS 383-1970, Specification for Coarse and Fine Aggregate from Natural Sources for Concrete, Bureau of Indian Standards, New Delhi, India.
  13. IS: 516-1959, Methods of Tests for Strength of Concrete, Bureau of Indian Standards, New Delhi, India.
  14. IS 10262- 2009, Concrete Mix Proportioning – Guidelines, Bureau of Indian Standards, New Delhi, India.

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20.

Authors:

Danduproul Kiran Kumar and G. Tulasi Ram Das

Paper Title:

Performance of Fuzzy Controller based Three Level Direct Torque Control fed IPMSM

Abstract: The paper presents a direct torque control (DTC) of Interior Permanent Magnet Synchronous Motor (IPMSM) drive that involves a multilevel inverter. The proposed control drive is described as the comparative study between Fuzzy controller and PI controller based three level diode clamped inverter fed DTC strategy of IPMSM. The key factor of the control technique is implemented using three level diode clamped inverter with adoption of space vector pulse width modulation topology. Moreover, the control scheme is achieved to improve the problem of balancing capacitor voltage with SVPWM. The complete proposed system developed in SIMULINK/MATLAB orientation. However, the three level DTC of IPMSM with conventional PI controller is despite the poor transient performances during the step changes of the speed with various load disturbances and reversal speed conditions. In addition, the amplitude of flux and torque oscillations are not accomplished satisfactory with traditional controller. The proposed three level diode clamped inverter drive of PI controller is replaced by intelligent Fuzzy logic controller. Further, the obtained numerical results of fuzzy controller of DTC for IPMSM are validated and compared with the DTC of IPMSM using PI speed controller at different speed and load conditions. In fact, the proposed DTC algorithm with fuzzy controller performances demonstrates the efficacy of drive in view of a dynamic response, considerably low distortions of flux and torque and less THD in stator current and voltages.

Keywords: Direct Torque Control, Fuzzy Controller, IPMSM, Multilevel Inverter, SVPWM.

References:

  1. Niu F, Wang B, Babel AS, Li K, Strangas EG. Comparative evaluation of direct torque control strategies for permanent magnet synchronous machines. Power Electronics. IEEE Transactions on 2016 Feb; 31(2):1408-124.
  2. Ying Pei Liu “Space Vector Modulated Direct Torque Control for PMSM” Advanced computer in computer system intelligent system and environment, pp. 225–230, 2011.
  3. Mohamed Kadjoudj, Soufiane Taibi, Noureddine Golea, Hachemi Benbouzi, “Modified Direct Torque Control of Permanent Magnet Synchronous Motor Drives,” International Journal of Sciences and Techniques of Automatic control & computer engineering, Volume 1, No.2, December 2007, pp. 167−180.
  4. Takahashi I. and Naguchi T., “A new quick response and high-efficiency control strategy of an induction motor,” IEEE Trans. Ind. Applicat., Vol. IA-22, pp. 820–827, /Oct. 1986.
  5. Nitin Kelkar, V.A.Joshi, “Direct Torque Control of Permanent Magnet Synchronous Motor” International Journal of Scientific & Engineering Research Volume 3, Issue 8, August-2012. ISSN 2229-5518.
  6. Yong-chang Zhang, Jian-guo Zhu, Wei Xu, You-guang Guo. A Simple Method to Reduce Torque Ripple in Direct Torque-Controlled Permanent-Magnet Synchronous Motor by Using Vectors with Variable Amplitude and Angle[J]. IEEE Transactions on Industrial Electronics, 2011, 58(7): 2848-2859.
  7. Piotr Gajewski, Krzysztosof Pienkowski “Direct Torque Control and Direct Power Control of wind turbine system with PMSG,” Wroclaw University of Technology, Department of Electrical Machines, Drives and Measurement doi:10.15199/48.2016.10.56.
  8. Khouch F, Lugoun SM, Marouni K, Kheloui A an El Hachemi Benbouzid M. “Hybrid Cascaded H-Bridge Multilevel-Inverter Induction-Motor-Drive Direct Torque Control for Automotive Applications,” IEEE Transactions on Industrial Electronics, vol. 57, no.3, pp. 892-899, 2010.
  9. Qidi Tang, Xinglai Ge, Yong-Chao Liu and Maojun Hou, “Improved switching- table-based DTC strategy for the post-fault three-level NPC inverter-fed induction motor drives,” IET Elect. Power Appl., 12, no.1, pp. 71-80, 2017.
  10. Saifullah Payami, Ranjan Kumar Behera, Se, and Atif Iqbal, “DTC of Three-Level NPC Inverter Fed Five-Phase Induction Motor Drive with Novel Neutral Point Voltage Balancing Scheme” IEEE Transactions on Power Electronics, Vol. 33, No. 2, pp.1487-1500, February 2018.
  11. Deepu Mohan, Xinan Zhang, and Gilbert Hock Beng Foo, “Generalized DTC Strategy for Multilevel Inverter Fed IPMSMs with Constant Inverter Switching Frequency and Reduced Torque Ripples,” IEEE Transactions on Energy Conversion, Vol. 32, No. 3, pp.1031-1041, September 2017.
  12. Feng Niu, Bingsen Wang, Andrew S. Babel and Kui Li, and Elias G. Strangas, “Comparative Evaluation of Direct Torque Control Strategies for Permanent Magnet Synchronous Machines,” IEEE Power Electron., vol.31, no.2, pp. 0885-8993, February 2016.
  13. Ashish B Chaudhari, S. Jebarani Evangeline “A Space Vector Pulse Width Modulation Technique to Reduce the Effects of Voltage Unbalances” International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-3, Issue-4, April 2014.
  14. Wei Chen, Ying-Ying Zhao, Zhan-Qing Zhou, Yan Yan, and Chang-Liang Xia, “Torque Ripple Reduction in Three-Level Inverter-Fed Permanent Magnet Synchronous Motor Drives by Duty-Cycle Direct Torque Control Using an Evaluation Table,” Journal of Power Electronics, Vol. 17, No. 2, pp. 368-379, March 2017
  15. Vinod B. R., Shiny G. “A Multilevel Inverter fed Direct Torque Control Strategy for an Induction Motor using PI Controllers” International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-7 Issue-4, April 2018.
  16. Venkataramana Naik N, Aurobinda Panda, S.P Sing, “A Three-Level Fuzzy-2 DTC of Induction Motor Drive Using SVPWM,” IEEE Transactions on Industrial Electronics, Vol. 63, No. 3, March 2016.
  17. K, Umashankar.S, Sanjeevikumar.P, Paramasivam., “Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Direct Torque Control of PMSM Driven Centrifugal Pump,”   International Journal of Renewable Energy Research (IJRER), vol.7, No.3, 2017.
  18. Kiran Kumar and G. Tulasi Ram Das,” Simulation and analysis of modified DTC of PMSM,” International Journal of Electrical and ComputerScience.Vol.8, no.5,2018.
  19. Qian Liu and Kay Hameyer, “Torque Ripple Minimization for Direct Torque Control of PMSM with Modified FCSMPC,” IEEE transactions on Industry applications, vol. 52, no.6, November/December 2016.
  20. Lixin Tang, Limin Zhong, Muhammed Fazlur Rahman, and Yuwen Hu, “A novel direct torque controlled     Interior Permanent Magnet Synchronous Machine Drive with low ripple in flux and torque and fixed     switching frequency,” IEEE Transactions on Power   Electronics, vol. 19, no. 2, march 2004.
  21. Karel Jezernik, Joze Korelic, and Robert Horvat, “PMSM Sliding Mode FPGA-Based Control for Torque Ripple Reduction,” IEEE Transactions on Power Electronics, Vol. 28, No. 7, pp.3549-3556, 2013.
  22. Xueqing Wang, Zheng Wang, Ming Cheng, and Yihua Hu, “Remedial strategies of T-NPC three- level asymmetric six-phase PMSM Drives based on SVM-DTC,” IEEE Transactions on Industrial Electronics, vol. 64, no. 9, September 2017.
  23. Yasser Abdel-Rady Ibrahim Mohamed, “Adaptive Self-Tuning Speed Control for Permanent-Magnet Synchronous Motor Drive with Dead Time,” IEEE Transactions on Energy Conversion, vol. 21, no. 4,2006.
  24. Saji Chacko, Chandrashekhar N. Bhende, Shailendra Jain and Rajesh Kumar Nema, “Rotor resistance estimation of vector controlled Induction Motor Drive using GA/PSO tuned fuzzy controller,” International Journal on Electrical Engineering and Informatics - volume 8, Number 1, March 2016.P. Sathvik, A. Srinivasa Reddy, B. Sambasiva Rao “Simulation of Shunt Active Power Filter with Pi and Fuzzy Logic Controller” International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-7 Issue-2, December 2017.
  25. Ahmad Asri Abd Samat, M.N. Fazli, N.A. Salim, Abdul Malek Saidina Omar, and Muhammad     Khusairi Osman, “Speed control design of Permanent Magnet Synchronous Motor using Takagi Sugeno Fuzzy Logic Control”, Volume 13, Number 3, pp: 689-695, December 2017
  26. Ritesh Sharma, K.K. Pranjapat and Atual Sood, “Performance Analysis of Direct Torque Control of PMSM Drive Using Two Level Inverter,”. IEEE. Conference on Communication and network Technologies.pp.7695-4692,2012.

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21.

Authors:

Sundaresan S, Anantha kumar M, Madhumathi K

Paper Title:

Optimal Bandwidth Utilization and Energy Efficiency in C-RAN for 5G Wireless Communication

Abstract: Mobile communication play a vital role in human life and a new era is yet to evolve with 5G Technology. Cloud Radio Access Network (C-RAN) with Remote Radio Head (RRH) and Baseband unit (BBU) pool is meant for high spectrum efficiency and considered to be as a dominant realization of mobile networks with open platform supporting green infrastructure and paves way for emerging technology in 5G mobile networks. This blooming technology provides a pioneering approach to encounter the requirements and progress the network to deliver coverage, innovative services and lower support costs. This results in enhancement of network performance, in addition with resource utilization. This paper proposes a BBU and RRH association scheme with opportunistic coordinated selective forwarding using duplicate detection distributed Ex-or to utilize the appropriate bandwidth, reduce the latency of transmitted packets and thereby reducing energy consumption in future wireless communication. Results reveals that opportunistic coordinated selective forwarding method yields better performance in all aspect for 5G communication system.

Keywords: Bandwidth utilization, C-RAN, Latency, Opportunistic coordinated selective forwarding, Throughput. 

References:

  1. Chen S and Zhao J, 2014 “The requirements, challenges, and technologies for 5G of terrestrial mobile telecommunication,” IEEE Commun. Mag., 52, no. 5, pp.36-43.2014.
  2. Chih-Lin, I., et al. 2014 "Recent progress on C-RAN centralization and cloudification." IEEE Access 2, pp. 1030-1039.
  3. Larsson, G. Erik, et al. 2014 "Massive MIMO for next generation wireless systems" IEEE communications magazine 52, no.2, pp. 186-195.
  4. Wang, Xinbo, et al. 2016 "Handover reduction in virtualized cloud radio access networks using TWDM-PON fronthaul." Journal of Optical Communications and Networking8, no.12 pp. B124-B134.
  5. Sun, C, et al., 2014 “A coalitional game scheme for cooperative interference management in cloud radio access networks” Trans Emerging Tel Tech, vol. 25, no. 9, pp.954–964.
  6. Zhai G et al., 2014 “Load diversity based optimal processing resource allocation for super base stations in centralized radio access networks,” China Inf. Sci., vol. 57, no. 4, pp. 1–12.
  7. N, et al., 2016 “Traffic-Aware Cloud RAN: A Key for Green 5G Networks,” IEEE JSAC, vol. 34, no. 4, pp. 1010-1021.
  8. Bosch, Peter, et al. 2007 "Flat cellular (UMTS) networks." Wireless Communications and Networking Conference, WCNC 2007. IEEE.
  9. Jo, Minho, et al. 2015 "Device-to-device-based heterogeneous radio access network architecture for mobile cloud computing." IEEE Wireless Communications22, no.3, pp.50-58.
  10. M, Li. Y, Jiang. J, Li. J, and Wang. C, 2014 Heterogeneous cloud radio access networks: A new perspective for enhancing spectral and energy efficiencies, IEEE Wireless Commun., vol. 21, no. 6, pp. 126-135.
  11. Colombo, Giovanni, and Hans Hegeman, "Network architecture and functionalities in UMTS. 1994 " Personal, Indoor and Mobile Radio Communications, 1994. Wireless Networks-Catching the Mobile Future., 5th IEEE International Symposium on. Vol. 3. IEEE.
  12. Chih-Lin, I., et al. 2014 "Toward green and soft: a 5G perspective."  IEEE Communications Magazine52, no.2, pp.66-73.
  13. B et al., 2013“Radio Base Stations in the Cloud.” Bell Labs Tech. J., vol. 18, no. 1, May 2013, pp. 129–52.
  14. M, et. al., 2015 “Baseband Processing Units Virtualization for Cloud Radio Access Networks,” IEEE Wireless Communications Letters, vol. 4, no. 2, pp. 189-192.
  15. S et al., 2012 “CloudIQ: A framework for processing base stations in a data center,” Proc. ACM MobiCom, pp. 125–136.
  16. D et al., 2016 “Elastic Resource Utilization Framework for High Capacity and Energy Efficiency in Cloud RAN,” IEEECommun. Mag., vol. 54, no. 1, pp. 26–32.
  17. J et al., 2016 “Statistical Multiplexing Gain Analysis of Heterogeneous Virtual Base Station Pools in Cloud Radio Access Networks,” IEEE Trans. Wireless Commun., vol. 15, no. 8, pp. 5681–5694.
  18. P et al., 2011 “Traffic Driven Power Saving in Operational 3G Cellular Networks,” Proc. ACM Mobicom, U.S., pp. 121–132.
  19. zhang, Junguo, et al. 2009 "The NS2-based simulation and research on wireless sensor network route protocol”. Wireless Communications, Networking and Mobile Computing, 5th International Conference on IEEE.

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22

Authors:

Vishal Mishra, Nikhil Bharat, Kalyan Chakraborty

Paper Title:

Comparative assessment on the machinability of EN 24 and EN 36C Steels

Abstract: EN 24 steel is medium carbon steel of nickel-chromium-molybdenum grade generally used for manufacturing heavy-duty axles, shafts, etc., whereas EN 36C steel is low carbon, nickel- chromium case hardened steel used widely in the manufacturing of heavy-duty crane shafts, aeroplane gears, cams, rollers, etc. The paper presents the comparative analysis on machinability of two varieties of steels, i.e. EN24 and EN 36C steels. Machining experiments were done according to 33 factorial design. Cutting speed, feed and depth of cut are the input process parameters and chip reduction coefficient, Von Mises stress, temperature differential and tool wear are the output parameters considered in the analysis. Metal removal of the work material was done in a conventional way by using carbide tool and lathe.

Keywords: Machinability, Von Mises stress, Temperature differential, Tool wear.

References:

  1. Korat, N.Agarwal, “Optimization of Different Machining Parameters of En24 Alloy Steel In CNC Turning by Use of Taguchi Method”, International Journal of engineering research and applications (IJERA),vol. 2, 2012, pp. 160-164.
  2. Krishankant, J. Taneja, M. Bettor, R. Kumar. “Application of Taguchi Method for Optimizing Turning Process by the effects of Machining Parameters”, International Journal of engineering and advanced technology (IJEAT), vol. 2,2012, pp. 9-15.
  3. Raman Kumar, Raman Kumar, Jasper Singh Ray and Avnet Singh Virk“Analysis the effects of process parameters in EN24 alloy steel During CNC turning by using madm”, International Journal of Innovative Research in Science, Engineering and Technology, vol. 2, 2013, 1131-1145.
  4. Eshwara Prasad Koorapati, Mohandas KN, Ramesh C S, Balashanmugam N, “Optimization of Surface Roughness during Hard Machining of Hard Chrome Plated Surfaces on EN24 Base Substrate”, International Journal of Mining, Metallurgy & Mechanical Engineering (IJMMME) ,vol. 1, 2013, pp. 27-38.
  5. Rahul Davis, Jitendra Singh Madhukar, “A parametric analysis and optimization of tool life In dry turning of En24 steel using Taguchi method”, International Journal of Production Technology and Management(IJPTM),vol. 3,2012, pp. 9-15.
  6. Adinarayana, G. Prasanthi, G. Krishnaiah, “Multi-Objective Optimization during Turning of EN24 Alloy Steel”, Journal of Engineering Research and Applications , vol. 3, 2013, pp. 1193-1198.
  7. Umesh Gupta, Amit Kohli, “Experimental Investigation of surface roughness in dry turning of AISI 4340 alloy steel using PVD- and CVD- coated carbide inserts”, International Journal of Innovations in Engineering and Technology, vol. 4, August 2014, ISSN 2319-1058.
  8. AmitAherwar, Deepak Unune, BhargavPathri, Jai Kishan, “Statistical and regression analysis of vibration of carbon steel cutting tool for turning of en24 steel using design of experiments”, International Journal of Recent advances in Mechanical Engineering (IJMECH) Vol.3, August 2014.
  9. Nikhil Bharat, Dr Kalyan Chakraborty, "Machinability of en24 steel (817m40)", International Journal of Latest Trends in Engineering and Technology (IJLTET)Volume 12 Issue 3 - January 2019 , pp.001-007 e-ISSN:2278-621X Print-ISSN : 2319-3778 https://www.ijltet.org/journal_details.php?id=942&j_id=4743
  10. VishalMishra and Dr KalyanChakraborty, “Machinability of Nickel-Chromium Case Hardened Steel (EN36C)”. Global Journal of Researches in Engineering: A Mechanical and Mechanics Engineering Volume 19 Issue 1 Version 1.0 the Year Online ISSN:2249-4596 Print ISSN:0975-5861.
  11. Amritpal Singh, Harjeet Singh. “Effects of Process Parameters on Surface roughness and MRR in hard turning of EN36 Steel”. International Journal of Scientific & Engineering Research, Volume 7,June-2017.
  12. Venkata Vishnu, E.Sanjana, G.Guruvaiah Naidu. “Experimental Investigations on CNC Turning of-36 Material Using Taguchi Method”. Vol-2 Issue-2 2017IJARIIE-ISSN(O)-2395-4396.
  13. MehulGosai, Sanket N. Bhavsar. “Experimental Study on Temperature Measurement in Turning Operation of Hardened Steel (EN36)”. Procedia Technology 23 ( 2016 ) 311 – 318
  14. Rahul Singh, Diplesh Gautam. “Experimental Analysis of the Cutting Forces and Material Removal Rate during Dry Turning of En-36 Steel.” International Journal of scientific research and management. Volume 4, Issue 06, Pages 4351-4354, year2016. ISSN (e): 2321-3418.
  15. Surulimani, A.Karthikraja, V.Sivaganesan, J.Gowthaman, M.Yojiith. “Optimization of CNC Turning Parameters on EN36B Steel Using Taguchi Method.” International Journal of Innovative Research in Science, Engineering and Technology. Vol. 5, Issue 2, February 2016. ISSN(Online): 2319-8753 ISSN (Print) : 2347-6710
  16. Venkata Vishnu, G.Guruvaiah.Naidu, Ch.PranavSrivatsav, A.Srikar.“Optimization of Process Parameters for hardness in Turning of EN36 using Taguchi Robust Design Methodology.”National Conference on Recent Trends & Innovations in Mechanical Engineering, April 2016 ISSN(online): 2321-0613.
  17. Murali Mohan, N.V.S.S. Sagar, M. Lava Kumar, E. VenugopalGoud. “Multi-Objective optimization of machining parameters of EN36 Steel using RSM”. International Journal of Scientific Research in engineering and Technology,Volume 4,April-2015
  18. A.Venkata Vishnu, G.Guruvaiah Naidu, K.B.G.Tilak, J.Ramakrishna. “Application of Taguchi Method in the Optimization of Turning Parameters for Material Removal Rate of En-36 Material”. International Journal of Advance Engineering and Research Development. (Scientific Journal of Impact Factor(SJIF): 3.134 ). Volume 2,Issue 8, August-2015 e-ISSN(O): 2348-4470 p-ISSN(P): 2348-6406.

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23

Authors:

S. K. Malave, A. S. Shirsat, D. M. Yadav

Paper Title:

Performance Assessment of Mobile WiMAX in high Speed Environment

Abstract: Currently, there is an increasing demand for high-speed internet access with reliable performance. Mobile WiMAX, IEEE 802.16m is 4G technology employed for broadband internet applications. It incorporates cyclic prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) as a modulation as well as multiplexing technique. In the high-speed scenario, OFDM experiences intercarrier interference, increasing the number of bits in error. In this paper, we provide the performance assessment of Mobile WiMAX for different vehicular speed upto 350km/h. Simulation is carried out for different values of doppler frequencies upto 1200Hz and bit error rate is computed. Result reveals that Bit Error Rate (BER) performance of mobile WiMAX degrades with an increase in vehicular speed. 16QAM gives satisfactory performance in a high-speed environment upto 250km/h.

Keywords: Mobile WiMAX, OFDM.

References:

  1. Hassan Yagoobi, “Scalable OFDMA physical layer in IEEE 802.16 wireless MAN,” Intel Technology J., vol. 8, Aug. 2004, pp. 201-212.
  2. Sassan Ahmadi, “An overview of next generation mobile WiMAX technology,” IEEE Comm. Magn., Jun. 2009, pp. 84-98. doi: 1109/MCOM.2009.5116805
  3. Rohde Schwarz, “IEEE 802.16m technology introduction,” White Paper, pp. 1-41.
  4. Mostifi, D. Cox, and A. Bahai, “ICI mitigation for pilot aided OFDM mobile system,” Proc. IEEE Trans. on Wireless Comm., vol. 4, no.2, Mar. 2005, pp. 765-774. [Online]. Available: https://www.ece.ucsb.
  5. Stamoulis, S. N. Diggavi and N. Al-Dahir, “Intercarrier interference in MIMO OFDM,” IEEE Trans. Sig. Proc., vol.50, Oct. 2002, pp. 2451-2464.
  6. H-C Wu and Y .Wu, “A new ICI matrix estimation scheme using hadamard sequence for OFDM system,” IEEE Trans. on Broadcast., 51, no.3, Sep. 2005, pp. 305-314.
  7. H-C.Wu, “Analysis and characterization of intercarrier and interblock interference for mobile OFDM system,” IEEE Trans. on Broadcast., vol 52, no.2, Jun. 2006, pp. 203-310.
  8. Xianbin Wang, Yiyan Wu, Jean Yves Chouinard, and Hsiao-Chun Wu, “On the design and performance analysis of multisymbol encapsulated OFDM systems,” IEEE Trans. on Vehicular Tech., vol. 55, No.3, (May 2006) pp. 990-1002.
  9. Biswojit Bose, Iftekar Ahmad and Daryoush Habibi, “Bit error rate analysis in WiMAX communication at vehicular speeds using nakagami–m fading model,” IEEE Vehicular Tech., Conf. VTC Fall, Sept. 2012, pp. 1-5.
  10. M. Fernandez- Carames, M. Gonzalez-Lopez, L. Castedo, “Mobile WiMAX for vehicular applications: performance evaluation and comparison against IEEE 802.11p/a,” Comp. Netw., vol.55, Nov. 2011, pp. 3784-3795. doi.org/10.1016/j.comnet.2011.02.016
  11. Alim, H. Abdallah, and A.Elaskary, “Simulation of WiMAX systems,” 2008 LCW, IEEE Lebanon, May 2008, pp. 11-16.
  12. Hala Bahy Eldeen Nafea, Fayez, W. Zaki, Hossam E. S. Moustafa, “Penetration loss of walls and data rate of IEEE 802.16m WiMAX,” International J. Wirel. Comm. and Mobile Comp., 2014, 2. no.1, pp.1-10. Doi:10.11684/j.wcmc.20140201.11.
  13. Wassan K Saad, Mohammad, Rosdiadee N, Ibraheem Shayea, “Throughput performance of adaptive modulation and coding scheme with link adaption for MIMO-WiMAX downlink transmission,” Journal of Asian Scientific Resear., vol. 2, No.11, pp. 641-650.
  14. Beibei Wang, Indranil Sen, David W. Matolak, “Performance evaluation of 802.16e in vehicle to vehicle channels,” in Vehic. Tech. Conf. IEEE 66th, Oct. 2007, pp 1406-1410. Doi: 1109/VETECF.2007.300
  15. Nargis Begam, Fahim Rahman, Khawza I. Ahmed, “Analysis of propagation model performance in WiMAX (IEEE 802.16e)-based wireless mobile vehicular networks,” 6th International Conf. on Electrical and Computer Engineering, ICECE, 18-20 Dec. 2010, pp. 714-717.
  16. Zaggoulos, A.Nix, and A. Doufexi, “WiMAX system performance in highly mobile scenarios with directional antennas, in IEEE 18th International Symposium on Pers., Indoor and Mobile Radio Comm., PIMRC, Sept.2007, pp. 1-5.
  17. Ahmadzadeh, A.M, “Capacity and cell-range estimation for multitraffic users in mobile WiMAX,” MSc Department of Electrical, Comm. and Signal Processing Engineering, University College of Boras School of Engineering, Sept. 2008.
  18. Hala B. Nafea, Fayez, W. Zaki, Hossam E. S. Moustafa, “Performance and capacity evaluation for mobile WiMAX IEEE 802.16m Standard,” and Mobile Techn., vol. 1, no, 1. 2013, pp. 12-19.
  19. Fazel and S. Kaiser, “Multi-Carrier and Spread Spectrum System,” 2nd ed., New York: John Wiley and Sons Ltd, 2003, pp. 86-92. [Online]. Available: https://books.google.co.in
  20. Qinghua Li, Guangjie Li, Wookbong Lee, Moon-il Lee, David Mazzarese, Bruno Clerckx, Zexian Li, “MIMO techniques in WiMAX and LTE: a feature overview,” IEEE Comm. Magn., May. 2010, pp. 86-92.

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24

Authors:

Shamil Zufarovich Valiev, Olga Anatolevna Fedorova

Paper Title:

Aspects of Modeling a Petrochemical and Petroleum Refinery Lifecycle

Abstract: The article considers the issues related to the method of modeling the lifecycle of petrochemical and oil refineries using a cognitive approach for making management decisions. The work gives the analysis of the current state of proven oil reserves in the Russian Federation and the Republic of Bashkortostan. It describes the assessment results of the technical and economic indicators of the PAO ANK Bashneft enterprises, made based on the schedule executed in the Sigma Plot program. For the purpose of sustainable future operation of petrochemical and oil refining enterprises in the Republic of Bashkortostan, several scenarios are proposed. The most optimal scenario is the production of an expanded range of commercial products based on biomass.

Keywords: biomass, development scenario, lifecycle, petrochemical and oil refinery, management decision making, renewable energy sources.

References:

  1. Adizes, “Managing corporate lifecycles”, The Adizes Institute Publishing, 2004, pp. 465.
  2. E. Greiner, “Evolution and Revolution as Organization Grow”. Harvard Business Review, 50(4), 1972, pp. 37-46.
  3. Pümpin, J. Prange, “Management der Unternehmensentwicklung – phasengerechte Führung und der Umgang mit Krisen”. Frankfurt: Campus-Ver, 1991, pp. 276.
  4. D. Kondratiev, Yu.V. Yakovets, L.I. Abalkin, “Bolshie tsikly konyunktury i teoriya predvideniya”. Izbrannye trudy [Large conjuncture cycles and the theory of foresight. Selected works]. noocivil.esrae.ru/pdf/2012/1/879.pdf
  5. F. Drucker, “Innovation and Entrepreneurship”, New York: Harper Collins Publisher, 1985, pp. 277.
  6. V. Shirokova, “Zhiznennyy tsikl organizatsii: empiricheskie i teoreticheskie podkhody” [Organizational life cycle: empirical and theoretical approaches], Rossiyskiy zhurnal menedzhmenta, 5(3), 2007, pp. 85–90.

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25

Authors:

Aleksey Vladimirovich Popov

Paper Title:

The Impact of Architectural and Space-Planning Design of Student Accommodation (Dormitories, Campuses) on the Time Budget of The Student Youth

Abstract: This study is aimed at determining the time budget of the student youth in the Russian Federation living in dormitories in conjunction with the architectural and space-planning design of these buildings. This study is important to optimize the architectural design of buildings and residential complexes for student accommodation.The research object includes buildings and their complexes for the accommodation of university students.The research subject is the determination of the time budget of the student youth in the system of studies, campus life and rest.The research purpose is to determine the directions of optimization of the architectural design of student accommodation, based on the optimization of the layout of locations of the implementation of functional needs of students living in dormitories and campuses.Research objectives are as follows: determination of the system of functional needs of residents of a student dormitory; determination of time expenditure on movements directly related to the performance of separate functional processes; calculation of the student’s time budget based on the calculated time expenditure; determination of the influence of the space-planning structure of student accommodation on the time budget of the student youth and the directions of improvement of such structure.

Keywords: campus, student accommodation, dormitory, student quarter, higher educational institution, temporary accommodation, university, academy, institute, higher education. 

References:

  1. V. Popov, T. V. Sorokoumova, Eksperimentalnyi raschet zatrat vremeni studencheskoi molodezhi na funktsionalnye protsessy, svyazannye s ucheboi, bytom i otdykhom na primere obshchezhitii studencheskogo gorodka (kampusa) NIU MGSU [Experimental Calculation of the Time Young University Students Spend on Functional Processes related to Studies, Campus Life and Recreation on the Example of Dormitories of the Student Quarter (Campus) of the Moscow State University of Civil Engineering]. Nauka i biznes: puti razvitiya, 10(88), 2018, pp. 65–71.
  2. N. Lobanov, Otdykh i arkhitektura: Budushchee i nastoyashchee [Recreation and Architecture: Future and Present]. Leningrad: Stroyizdat, 1982.
  3. B. Bychin, S. V. Malinin, Normirovanie truda: Uchebnik [Labor Rationing: Textbook]. Moscow: Ekzamen, 2002.
  4. M. Karakhanova, O. A. Bolshakova, Ispolzovanie vremeni gorozhanami: sotsialno-ekonomicheskie i demograficheskie faktory [Time Use by Citizens: Socio-Economic and Demographic Factors]. In Rossiya reformiruyushchayasya: Ezhegodnik [Reforming Russia: Yearbook], Issue 10, M. K. Gorshkov, Ed. Moscow: Institute of Sociology of the Russian Academy of Sciences, 2011, pp. 332-349.
  5. M. Karakhanova, Povsednevnaya deyatelnost gorozhan v pokazatelyakh byudzheta vremeni (1965-2004 gg.) [Daily Activities of Citizens in Indicators of the Time Budget (1965-2004)]. Sotsiologicheskie issledovaniya, 9, 2006, pp. 41–43.
  6. K. Shangaeva, Kharakteristika struktury byta sovremennogo gorodskogo naseleniya [Characterization of the Structure of Mode of Life of Contemporary Urban Population]. Vestnik Buryatskogo gosudarstvennogo universiteta, 6, 2010, pp. 184–188.
  7. V. Chepelik, Arkhitekturno-planirovochnye resheniya domov dlya odinochek: dis. ... kand. arkh. [Architectural and Space-Planning Design of Houses for Singles (Ph.D. Thesis). Kiev, 1964.
  8. I. Zheltikov, Sravnitelnyi analiz byudzheta vremeni studentov 1 i 6 kursa [Comparative Analysis of the Time Budget of students of the 1st and 6th Years of Study]. Vyatskii meditsinskii vestnik, 1, 2009, pp. 85–86.
  9. S. Kondratieva, L. A. Prokopenko, Struktura byudzheta vremeni i formy dosuga studentov spetsialnosti "PGS" TI (f) SVFU [Structure of the Time Budget and Leisure Forms of Students Majoring in "PGS" TI (f) SVFU]. In Materialy raionnoi nauchnoi konferentsii shkolnikov i studentov "Obrazovanie. Dukhovnost. Zdorove detei i molodezhi" [Proceedings of the District Scientific Conference for Pupils and Students "Education. Spirituality. Health of Children and Youth]. Neryungri: TI (f) SVFU, 2011, pp. 54–59.
  10. V. Nebesnaya, N. A. Gridina, Issledovanie byudzheta rabochego vremeni studentov vysshikh uchebnykh zavedenii [Research of the Budget of Working Time of Students of Higher Educational Institutions]. Pedagogika, psikhologiya i mediko-biologicheskie problemy fizicheskogo vospitaniya i sporta, 6, 2008, pp. 57–59.
  11. L. Pershina, Tendentsii formirovaniya investitsii v studencheskie obshchezhitiya kak v vid dokhodnogo zhilya [Trends in the Formation of Investments in Student Dormitories as a Type of Profitable Accommodation]. In Naukoemkie tekhnologii i innovatsii Yubileinaya Mezhdunarodnaya nauchno-prakticheskaya konferentsiya, posvyashchennaya 60-letiyu BGTU im. V.G. Shukhova, XXI nauchnye chteniya [High Technologies and Innovations. Jubilee International Scientific and Practical Conference dedicated to the 60th anniversary of BSTU named after V.G. Shukhov, XXI Scientific Readings]. Belgorod: Belgorod State Technological University named after V.G. Shukhov, 2014, pp. 92-96.
  12. V. Popov, R. A. Kazaryan, Sotsiologicheskie aspekty arkhitekturnogo formirovaniya zhilishcha studencheskoi molodezhi, sotsializatsiya lichnosti [Sociological Aspects of the Architectural Formation of the Student Youth Accommodation, Individual Socialization]. Perspektivy nauki, 4(103), 2018, pp. 46–52.
  13. V. Popov, R. A. Kazaryan, Ekonomicheskie aspekty arkhitekturnogo formirovaniya zhilishcha studencheskoi molodezhi [Economic Aspects of the Architectural Formation of the Student Youth Accommodation]. Nauka i biznes: puti razvitiya, 5(83), 2018, pp. 53–56.
  14. S. Rodionovskaya, Innovatsionnye napravleniya razvitiya sistemy arkhitekturno-stroitelnogo obrazovaniya na sovremennom etape [Innovative Directions of Architectural and Engineering System Development at the Present Stage]. Ekologiya urbanizirovannykh territorii, 3, 2009, pp. 102–104.
  15. V. Romanchikov, A. V. Kalugina, I. A. Puchkin, D. A. Panfilov, Sozdanie komfortnoi sredy i uslovii, stimuliruyushchikh razvitie nauchno-tekhnicheskogo potentsiala samarskogo regiona, i perspektivy sozdaniya kampusov [Creating Comfortable Environment and Conditions Stimulating the Development of the Scientific-Technical Potential of the Samara Region and Prospects for the Creation of Campuses]. Nauchnoe obozrenie, 17, 2016, pp. 81–89.
  16. V. Sorokoumova, A. N. Akimova, Vliyanie urbosredy na obshchee sostoyanie zdorovya cheloveka [The Impact of Urban Environment on the General State of Health of a Person]. In Stroitelstvo – formirovanie sredy zhiznedeyatelnosti [Construction – Formation of the Living Environment]. Moscow State University of Civil Engineering, 2016, pp. 178–190.
  17. P. Tolpinskaya, I. O. Zavgorodnyaya, Organizatsiya i razvitie rekreatsionnogo prostranstva studencheskikh kompleksov kak odnogo iz napravlenii renovatsionnogo protsessa [Organization and Development of the Recreational Space of Student Complexes as One of the Areas of the Renovation Process]. In D.P. Anufriev (Ed.), Potentsial intellektualno odarennoi molodezhi – razvitiyu nauki i obrazovaniya: Materialy VII Mezhdunarodnogo nauchnogo foruma molodykh uchenykh, innovatorov, studentov i shkolnikov [The Potential of Intellectually Gifted Youth – for the Development of Science and Education. Proceedings of the 7th International Scientific Forum of Young Scientists, Innovators, Students and Pupils]. Astrakhan, 2018, pp. 207–217.
  18. V. Popov, Historical Development Stages of the Student Youth Accommodation Architecture from Dormitories Prototypes to Post-industrial University Campuses. International Journal of Civil Engineering and Technology (IJCIET), 9(11), 2018, pp. 2526-2536.
  19. V. Popov, Ecological Optimization of the Architectural Environment of Higher Education Institutions in Moscow – The Use of Phyto-Metal Structures. Advanced Materials Research, 869-870, 2014, pp. 162–166.
  20. V. Popov, Printsipy formirovaniya arkhitektury studencheskogo zhilishcha vysshikh uchebnykh zavedenii: dis. ... kand. arkh. [Principles of the Formation of Architecture of Student Accommodation of Higher Educational Institutions (Ph.D. Thesis)]. Moscow, 2014.
  21. V. Popov, Osobennosti arkhitekturnoi organizatsii i kharakternye parametry zdanii obshchezhitii i domov studenta po rezultatam arkhitekturnogo obsledovaniya 297 obektov v Rossii i SNG (obshchezhitii, studencheskikh gorodkov, kampusov vuzov) [Features of the Architectural Organization and Characteristic Parameters of Dormitory Buildings and Student Houses as a Result of the Architectural Survey of 297 Student Accommodation Facilities in Russia and the CIS (dormitories, Student Quarters, University Campuses)]. Perspektivy nauki, 8(107), 2018, pp. 39–45.
  22. V. Popov, Osobennosti arkhitekturnoi organizatsii kompleksov studencheskogo zhilishcha – studencheskikh gorodkov po rezultatam arkhitekturnogo obsledovaniya 297 obektov studencheskogo zhilishcha v Rossii i SNG (obshchezhitii, studencheskikh gorodkov, kampusov vuzov). Chast 4 [Features of the Architectural Organization of Complexes of Student Accommodation – Student Quarters According to the Results of the Architectural Survey of 297 Student Accommodation Facilities in Russia and the CIS (Dormitories, Student Quarters, University Campuses). Part 4]. Perspektivy nauki, 12(111), 2018.
  23. V. Popov, Primery naibolee kharakternykh proektnykh reshenii zdanii studencheskogo zhilishcha po rezultatam arkhitekturnogo obsledovaniya 297 obektov studencheskogo zhilishcha v Rossii i SNG (obshchezhitii, studencheskikh gorodkov, kampusov vuzov). Chast 2 [Examples of the Most Characteristic Design Options of Buildings of Student Accommodation based on the Results of the Architectural Survey of 297 Student Accommodation Facilities in Russia and the CIS (Dormitories, Student Quarters, University Campuses). Part 2]. Perspektivy nauki, 10(109), 2018, pp. 37–43.
  24. A. V. Popov, Unikalnye i eksperimentalnye proekty zdanii i kompleksov studencheskogo zhilishcha po rezultatam arkhitekturnogo obsledovaniya 297 obektov studencheskogo zhilishcha v Rossii i SNG (obshchezhitii, studencheskikh gorodkov, kampusov vuzov). Chast 3 [Unique and Experimental Projects of Buildings and Complexes of Student Accommodation According to the Results of the Architectural Survey of 297 Student Accommodation Facilities in Russia and the CIS (Dormitories, Student Quarters, University Campuses). Part 3]. Perspektivy nauki, 11(110), 2018, pp. 34–40.

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Authors:

John J. Tucker Yépez, Benjamín A. Pusay Villarroel, Paulo A. Samaniego Rojas

Paper Title:

Analysis of Digital Diplomacy and E-Society in the Context of Internet Governance

Abstract: Nowadays, societies are in constant evolution, adapting to changes and constantly learning due to the progress of Information and Communication Technology (ICT). Internet, considered as the “public super highway” of information through which millions of terabytes travel every day, contains information of many different sources from Facebook comments to diplomatic cables having delicate information. In the international relations and in diplomacy, the ICT have gained an important space, providing a new paradigm through the new computer tools which link the knowledge society with the diplomats work, establishing a significant space for public opinion which is very important for our communities in the present. This paper aims to analyze the digital diplomacy and the new civil society (also known as e-society) for proving the importance of ICT in the scenario of digital diplomacy.

Keywords: Digital diplomacy, e-society, ICT, Internet governance, social media . 

References:

  1. C. Cervera, Dinámica de la sociedad internacional. Editorial Centro de Estudios Ramón Areces, 1993.
  2. Vaca, “Crisis Management on Twitter : Detecting Emerging Leaders,” pp. 140–147, 2016.
  3. Jason and M. Felim, “GWI Social,” GWI Soc., 2017.
  4. Flassan, Historia de la diplomacia francesa. 1811.
  5. M. Satow, A Guide to Diplomatic Practice, no. v. 1. Longmans, Green, 1917.
  6. Nicolson, Diplomacy. Oxford University Press, 1939.
  7. Valdés and E. L. Tovar, Derecho diplomático y tratados. Secretaría de Relaciones Exteriores, 1993.
  8. de asuntos exteriores y cooperación España, “Diplomacia del siglo XXI,” 2015. [Online]. Available: http://www.exteriores.gob.es/Portal/es/PoliticaExteriorCooperacion/DiplomaciasigloXXI/Paginas/Diplomaciapublica.aspx.
  9. Tucker, “Análisis de la diplomacia digital y la nueva sociedad civil en el ámbito de la gobernanza del internet,” Universidad de Guayaquil, 2016.
  10. Redlich, P. Krenz, S. Buxbaum-conradi, J. Wulfsberg, and F. Bruhns, “Impact of ICTs on the importance of membership and participation.”
  11. NANDA and A. MOWSHOWITZ, “Increasing Internet Acces and Freedoms with IGF Participation,” 2008.
  12. Álvarez-Ossorio et al., “Informe sobre las revueltas árabes,” Ediciones del Oriente y del Mediterraneo, 2012.
  13. INEC, “Tecnologías de la Información y Comunicaciones (TIC´S) 2016,” 2016. [Online]. Available: http://www.ecuadorencifras.gob.ec/documentos/web-inec/Estadisticas_Sociales/TIC/2016/170125.Presentacion_Tics_2016.pdf.

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Authors:

Saroj Kumar Mishra, Jagan Mohan Sahu, Subhranshu Sekhar Pati

Paper Title:

Implementation of Microcontroller based Fault Detection and Protection System for DC Motor

Abstract: The acute aim of this paper is to identify the nascent faults that may cause due to various reasons like over current/voltage, under voltage, high temperature, overload, and single phasing and to protect the motor from such faults. These type of faults mainly occur due to variation of parameters i.e. voltage, current, and temperature. Due to this fault, the motor winding gets heated which causes insulation failure, thus the life of the motor reduces. This project uses AT89S51 microcontroller for detection of electrical faults. The same microcontroller was developed by Intel and based on 8-bit microcontroller family. The circuit was designed successfully in Proteus simulation software as well as in real time environment and various testing conditions was applied to check the system robustness. The improved performance of the microcontroller and associate system network is established against system constraints through its efficient mechanism.

Keywords: Microcontroller AT89S51, LM35 temperature sensor, ACS712 current sensor, LM339 quad voltage comparator

References:

  1. Mazidi and Mazidi, “The 8051 Microcontroller and Embedded Systems Assembly and C", Pearson Education India, 2012.
  2. Technical Research Paper, “Microcontroller Based Fault Detector”, International Journal of Advancements in Research & Technology, vol. 1, no. 5, 2012.
  3. Rupali M. Shivpuje, Swapnil D. Patil, "Microcontroller Based Fault Detection and Protection System for Induction Motor", International Conference on Intelligent Computing and Control Systems (ICICCS), 2017.
  4. Ponnle, M.  Omojoyegbe, “Development of a Low-Cost Microcontroller Based Under and Over Voltage Protection Device”, International Journal of Scientific Engineering and Technology, vol. 3, no. 9, pp: 1225‐1229, 2014.
  5. Dharanya, M. Priyanka, R. Rubini and A. Umamakeswari, “Real Time Monitoring and Controlling of Transformers”, Journal of Artificial Intelligence vol. 6, no. 1, pp: 33‐42, 2013.
  6. Wellem and B. Setiawan, “A Microcontroller Based Room Temperature Monitoring System”, International Journal of Computer Applications, vol. 53, no. 1, 2012.
  7. Moseler and R. Isermann, “Application of Model Based Fault Detection to a Brushless DC Motor”, IEEE Transactions on Industrial Electronics, vol. 47, no. 5, pp: 1015-1020, 2000.
  8. Oscar Poncelas, Javiar A. Rosero, JordiCusido Jaun Antonio Ortega and Luis Romeral, “Motor Fault Detection Using Rogowaski Sensor without an Integrator”, IEEE Transaction on Industrial Electronics, vol. 56, no. 10, pp: 4062-4070, 2009.
  9. Siddique, G. S. Yadava, and B. Singh, “A Review of Stator Fault Monitoring Techniques of Induction Motor”, IEEE Transconvers, vol. 20, no. 1, pp: 106-114, 2005.
  10. R. Mondal, A. Mukhopadhyay and D. Basak, “Embedded System of DC Motor Closed Loop Speed Control Based on 8051 Microcontroller”, Procedia Technology, vol. 10, pp: 840-848, 2013.

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Authors:

P. Saleem Akram, T.V. Ramana

Paper Title:

Stacked Electromagnetic Band Gap Ground Optimization for Low profile Patch Antenna Design

Abstract: Current paper concentrates on the design and analysis of novel stacked Electromagnetic Band Gap structures. The surface properties of both the novel designs, for instance, High surface impedance (HSI), Artificial Magnetic Conductor (AMC) and Forbidden band gap (FBG) are overseen by utilizing Finite element method (FEM) based 3D electromagnetic (EM) simulator. The acquired outcomes are contrasted with the outcomes of classical mushroom EBG structure. Proposed novel structures are named here as Progressive Stacked Electromagnetic Band Gap (PSEBG) and Stacked Electromagnetic Band Gap (SEBG). The unit cell of SEBG and PSEBG are analogue to MEBG structure, incorporates two layers over the principle plane. Top layer is a planar MEBG, middle layer contains cluster of small square MEBGs. Both proposed and reference structures are applied as ground plane to microstrip patch antenna (MPA). Radiation characteristics return loss, Front to back radiation, compact and low profile properties are studied and presented to optimize the best EBG. 

Keywords: Electromagnetic (EM), Mushroom Electromagnetic Band Gap (MEBG), Artificial Magnetic Conductor (AMC), forbidden band gap (FBG).

References:

  1. Hansen, R. C., Electrically Small, Super directive, and Superconducting Antennas, 82-89, New Jersey, 2006.
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  3. Praveen Kumar, Dr. Habibulla Khan "Optimization of EBG structure for mutual coupling reduction in antenna arrays; a comparitive study" International Journal of engineering and technology, Vol-7, No-3.6, Special issue-06, 2018. page 13- 20.
  4. Praveen Kumar, Dr. Habibulla Khan "Active PSEBG structure design for low profile steerable antenna applications" Journal of advanced research in dynamical and control systems, Vol-10, Special issue-03, 2018.
  5. Praveen Kumar, Dr. Habibulla Khan, "Design and characterization of Optimized stacked electromagnetic band gap ground plane for low profile patch antennas" International journal of pure and applied mathematics, Vol 118, No. 20, 2018, 4765-4776.
  6. Praveen Kumar, Dr Habibulla Khan " Surface wave suppression band, In phase reflection band and High Impedance region of 3DEBG Characterization" International journal of applied engineering research (IJAER), Vol 10, No 11, 2015.
  7. Olaode, O. O., "Characterization of meander dipole antennas with a geometry based, frequency-independent lumped element model," IEEE Antennas and Wireless Propagation Letters, Vol. 11, 346-349, 2012.
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  11. Scamarcio, G., F. Bilotti, A. Toscano, and L. Vegni, "Broad band U-slot patch antenna loaded by chiral material," Journal of Electromagnetic Waves and Applications, Vol. 15, No. 10, 1303-1317, 2001.
  12. Bilotti, F. and L. Vegni, "Chiral cover effects on microstrip antennas," IEEE Trans. Antennas Propagat., Vol. 51, 2891-2898, 2003.
  13. Vegni, L., A. Toscano, and F. Bilotti, "Shielding and radiation characteristics of planar layered inhomogeneous composites," IEEE Trans. Antennas Propagat., Vol. 51, 2869-2877, 2003.
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Authors:

Damilya Konysbaeva, Viktoriya Gorbulya, Kurmet Baibussenov, Adilkhan Abildinov, Zhamshid Faizakhmatov

Paper Title:

Urban flora of Astana (Kazakhstan): A Technology for Creating a Comfortable Ecosystem

Abstract: This paper describes the results of studying the state of tree plantations in the harsh climatic conditions of Astana. In this paper, the authors describe the assessment of the living state of the main tree varieties, their resistance to pests and diseases as elements of the emerging urban flora. The methods generally adopted in botany were used for assessing the sustainability of tree species and their living state. Total estimation of the living state of trees of each species in the green plantations was performed by 10 – 25 model trees. At seven objects (SP1 – SP7) of the research, widely spread tree crops (Populus balsamifera L., Populus pyramidalis Salisb., Populus alba L. and Populus bolleana Lauche, Picea pungens Engelm., Pinus sylvestris L., Ulmus parvifolia Jacq. and Ulmus glabra “Pendula”), Betula pendula Roth., Salix fragilis L.) show various degrees of resistance to ecotopic conditions, and have various positions in amenity planting of the city and the urban flora formed.

Keywords: flora, urbanized territories, tree plantings, living conditions, recreation, ecosystem services. 

References:

  1. Pauchard, M. Aguayo,  E. Rena, R. Urrutia, “Multiple effects of urbanization on the biodiversity of developing countries: The case of a fast-growing metropolitan area” (Concepción, Chile), Biological Conservation, 127(3), 2006, pp. 272-281.
  2. G. Ilminskikh, “Analiz gorodskoi flory” (na primere goroda Kazani) [Analysis of urban flora (on the example of Kazan)]: Abstract. dis. ... Cand. of Biol. Sciences.: 03.00.05. L: USSR AS BIS, 1982, pp. 23.
  3. G. Ilminskikh, “Florogenez v usloviiakh urbanizirovannoi sredy”: (Na primere gorodov Viatsko-Kamskogo kraia) [Florogenesis in the conditions of the urbanized environment (On the example the cities in the Vyatka-Kama region)]: abstract dis. ... Doctor of Biological Sciences: 03.00.05. Saint-Petersburg, St. Petersburg. State Univ., 1993, 36.
  4. I. Burda, “Antropogennaia transformatsiia flory” [Anthropogenic transformation of flora], Kiev: Naukova Dumka, 1991, pp. 168.
  5. S. Antipina, “Urbanoflora Karelii” [Urban flora of Karelia], Petrozavodsk: Publishing house of PetrSU, 2002, pp. 200.
  6. Y. Grigorievskaya, “Flora goroda Voronezha” [The flora of the city of Voronezh], Voronezh: Voronezh State University, 2000, pp. 200.
  7. A. Berezutsky, A. V. Panin, “Flora gorodov: struktura i tendentsii antropogennoi dinamiki” [Flora of the cities: the structure and the tendencies of anthropogenic dynamics], Botanical magazine, 92(10), 2007, pp. 1481 – 1489.
  8. Strategiia “Kazakhstan-2050”: novyi politicheskii kurs sostoiavshegosia gosudarstva [Strategy "Kazakhstan-2050": new political course of an established state]. http://egov.kz/cms/ru/law/list/K1200002050
  9. Pysek, “Alien and native species in Central European urban floras: a quantitative comparison”, Jornal of Biogeography, 25(1), 1998, pp. 155–163.
  10. Clemants, G. Moore, “Patterns of Species Richness in Eight Northeastern United States Cities”, Urban Habitats, 1(1), 2003, pp. 4– 16. http://www.urbanhabitats.org.
  11. V. Veselkin, A.S. Tretyakova, S.A. Senator, S.V. Saksonov, V.A. Mukhin, G.S. Rozenberg, “Geographical Factors of the Abundance of Flora in Russian Cities”, Doklady Earth Sciences, 476(1), 2017, pp. 1113–1115.
  12. Stajerova, P. Smilauer, J. Bruna, P. Pysek, “Distribution of invasive plants in urban environment is strongly spatially structured”,  Landscape ecology, 32(3), 2017, pp. 681-692.
  13. Borysiak, A. Mizgajski, A. Speak, “Floral biodiversity of allotment gardens and its contribution to urban green infrastructure”, Urban ecosystems, 20(2), 2017, pp. 323-335.
  14. J. Potgieter, M. Gaertner, С. Kueffer, B.M. Larson, H. Stuart, W. Livingstone, P.G. O’Farrell, D.M. Richardson, “Alien plants as mediators of ecosystem services and disservices in urban systems: a global review”, Biological Invasions, 19(12), 2017, pp. 3571–3588.
  15. Limny, B. Sikora, “The effects of pollution on the quality of agriculture and forest products”, Papers presented to the Symposium on the effects of air born pollution on vegetation, Poland, Warsaw, 1980, pp. 160–162.
  16. F. J. Aronson, S. N. Handel, S. E. Clemants, “Fruit type, life form and origin determine the success of woody plant invaders in an urban landscape”, Biological Invasions, 9(4), 2007, pp. 465–475.
  17. Zelenye nasazhdeniia [Amenity stands]. http://astana.gov.kz/ru/modules/material/11213
  18. A. Alekseev, “Diagnostika zhiznennogo sostoianiia derevev i drevostoev” [Diagnostics of the living state of trees and tree stands], Agroforestry, 4, 1989, pp. 51 – 57.
  19. I. Tolmachev, “Vvedenie v geografiiu rastenii” [Introduction into the geography of plants], L. :Publishing house of LSU, 1974, pp. 244.
  20. A. Yurtsev, R. V. Kamelin, “Osnovnye poniatiia i terminy floristiki” [Basic concepts and terminology of floristry], Perm: PSU, 1991, pp. 80.
  21. S. Nikolaevsky, “Ekologicheskaia otsenka zagriazneniia sredy i sostoianiia nazemnykh ekosistem metodami fitoindikatsii” [Environmental assessment of environment pollution and the state of terrestrial ecosystems by the methods of phytoindication], M: MGLUM, 1999, pp. 193.
  22. V. Smirnova, A. A. Chistyakova, R. V. Popatyuk, “Populiatsionnaia organizatsiia rastitelnogo pokrova lesnykh territorii” (na primere shirokolistvennykh lesov Evropeiskoi chasti Rossii) [Populational organization of the vegetation cover of forest territories (on the example of broad-leaved forests of the European part of Russia)]. Pushchino, 1990, pp. 92.
  23. V. Karmanova, “Matematicheskie metody izucheniia rosta i produktivnosti rastenii” [Mathematical methods of studying plants’ growth and productivity]. M.: Nauka, pp. 1976. - 221.
  24.   V. S. Nikolaevsky, “Vliianie promyshlennykh gazov na rastitelnost” [The effect of industrial gases on the vegetation], Regional environmental monitoring. M.: Nauka, 1983, pp. 202 – 222.

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Authors:

Oleg Yakovlevich Kravets, Evgeny Efimovich Krasnovskiy, Irina Nikolaevna Kryuchkova, Evgeniya Vitalievna Bolnokina, Vladimir Dmitriyevich Sekerin

Paper Title:

Mathematical Simulation of Dynamics on the Basis of Analysis of Multidimensional Time Series with Consideration for Lagged Influence of Factors Using Neural Networks

Abstract: This article investigates into the models and methods of neural simulation of dynamics on the basis of analysis of multidimensional time series with consideration for lagged influence of significant factors. Mathematical formulation of the neural network construction for nonzero lag is presented, peculiarities of lag optimization are described for one independent variable (input), simulation and forecast database is specified as well as neural algorithms of data processing, algorithmization of multivariate regression analysis is carried out with optimization of lag vector for significant factors.

Keywords: mathematical simulation, neural networks, lag. 

References:

  1. M. Avdeeva, O.Ya. Kravets, “Teoreticheskie osnovy prognozirovaniya nalogovykh postuplenii na osnove krosskorrelyatsionnogo analiza mnogomernykh vremennykh ryadov” [Theoretical foundations of forecast of tax revenue on the basis of cross correlation analysis of multidimensional time series], Sistemy upravleniya i informatsionnye tekhnologii, 1.2(23), 2006, p. 212-216.
  2. M. Avdeeva, O.Ya. Kravets, I.N. Kryuchkova, “Territorial'noe prognozirovanie nalogovykh postuplenii s primeneniem mnogomernykh krosskorrelyatsionnykh tekhnologii” [Territorial forecast of tax revenue using multidimensional cross correlation technology], Innovatsionnyi Vestnik Region, 3(9), 2007, p. 31-36.
  3. M. Avdeeva, I.N. Kryuchkova, “Issledovanie tekhnologii neirosetevogo prognozirovaniya nalogovykh postuplenii territorii s primeneniem tekhniki mnogomernogo krosskorrelyatsionnogo analiza” [Studying neural network forecast of territorial tax revenue using multidimensional cross correlation technology], Territoriya nauki, 4(5), 2007, p. 428-436.
  4. , Hecht-Nielsen, “Theory of the Backpropagation Neural Network”, Neural Networks, 1(1), 1989, p. 593 - 605.
  5. N. Gibbs, “Variational Gaussian process classifiers” IEEE Transactions on Neural Networks, 11(6), 2000, p. 1458–1464.
  6. I. Goodfellow, Y. Bengio, A. Courville, “Deep Learning”, MIT Press, 2016, p. 196.

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Authors:

Annapantula Sudhakar, Telagarapu Prabhakar

Paper Title:

Design of a Frequency Notch Band Stepped Rectangular Microstrip Antenna

Abstract: The intention of this paper is to propose and explore a frequency notch band stepped rectangular microstrip antenna ranging from 4 to 11 GHz. First design is about UWB antenna with a frequency range from 4.3 to 10.9 GHz and second design deals with frequency notched antenna ranging from 4 to 11 GHz with a stop band from 4.4 to 4.8 GHz. It is required to stop the frequency band from 4.4 to 4.8 GHz used for receiving the frequencies of INSAT as it is overlapping the UWB range. The notch band is due to the introduction of U shaped slot on patch of FR4 substrate. By changing the dimensions of the slot and ground height, the frequency response can be altered. Simulation of the suggested antenna is done by HFSS software. Graphs for the return loss, VSWR, Radiation patterns are illustrated to support the behavior of the antenna.

Keywords: Microstrip antenna, Notched band, Return loss, frequency response.

References:

  1. Garg, R., Bahl, I.J., Bhartia, P. & Ittipiboon, A. Microstrip antenna design hand book. Dedham, MA: Artech House, 2000.
  2. Singh, B. & Singh, N., “Design of a corner cut rectangular microstrip antenna having T- slot for Wi-fi, radar and satellite applications”, International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 2, pp. 1287-1291, 2013.
  3. Keshri, P.K., Kaur, R. & Yadav, D.P., “Effect of notch/slot in the radiating patch of a planar antenna configuration”, Indian Journal of Science and Technology, vol. 9, no. 47, pp. 1-7, 2016.
  4. Sudhakar, A., Satyanarayana, M., Sunil Prakash, M. & Sharma, S. K., “Compact UWB planar antenna with WLAN band rejection”, International Journal of Microwave and Optical Technology, vol. 11, no. 2, pp. 123-130, 2016.
  5. Sekhararao, K.Ch., Babu, Ch., “Design of C-shape slot microstrip patch antenna with line feed for WLAN technology”, IOSR Journal of Electronics and Communication Engineering, vol. 11, no. 6, pp. 72-78, 2016.
  6. Ahmed, Z., Ahmed, M.M., & Ihsan, M.B., “A novel differential fed high gain patch antenna using Resonant Slot Loading”, Radio Engineering, 27, no. 3, pp. 662-670, 2018.
  7. Oraizi, H. & Shahmirzadi, N.V., “Frequency- and time-domain analysis of a novel UWB reconfigurable microstrip slot antenna with switchable notched bands”, IET Microwaves, Antennas & Propagation, 11, no. 8, pp. 1127–1132, 2017.
  8. Ellis, M. S., Zhao, Z., Wu, J., Nie, Z., & Liu, Q. H., “A novel miniature band-notched wing-shaped monopole ultrawideband antenna”, IEEE Antennas and Wireless Propagation Letters, vol. 12, pp. 1614–1617, 2013.
  9. Yoon, C., Lee, W.-J. , Kim, W.-S., Lee, H.-C, Park, H.-D., “Compact band-notched ultrawideband printed antenna using inverted L-slit”,  Microwave and Optical Technology Letters, 54 (1), pp. 143–144, 2012.
  10. Balanis, C. A. Microstrip antennas. In Antenna Theory: Analysis and Design (pp. 811-820). Wiley-Interscience, 2005.

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32.

Authors:

B.OMNAMASIVAYA, MSV.Prasad, B.sandhya sri

Paper Title:

Does Age of the Companies Influence Really Environmental Accounting Disclosure Practices In India

Abstract: The developing countries like India are facing the twin problems of protecting the environment and promoting economic development. a tradeoff between environmental protection and development is required . A careful assessment of the benefits and costs of environmental damage is necessary to find the safe limits of environmental degradation and the required level of development. The main aim of this paper is to know the environmental accounting disclosure practices, for the purpose of the study 23companies were selected selectively. A questionnaire was used for collection of information. A questionnaire was sent to 23 companies finally 23 companies were responded to the questionnaire .there is no statistical tools applied only the data analyzed through graphs and environmental accounting disclosure index calculated. The main findings of the study that the environmental accounting disclosure index is very high for the environmental sensitive industries and the environmental accounting disclosure score is low for non sensitive environmental industries and for small companies . the highest disclosure is 98.25and the lowest score is 55.25 and age of the companies influence positively environmental accounting and disclosure practices

Keywords: environmental accounting, environmental accounting disclosure index, environmental reporting.

References:

  1. O. Young, “Synthetic structure of industrial plastics (Book style with paper title and editor),” in Plastics, 2nd ed. vol. 3, J. Peters, Ed.  New York: McGraw-Hill, 1964, pp. 15–64.
  2. -K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.
  3. Poor, An Introduction to Signal Detection and Estimation.   New York: Springer-Verlag, 1985, ch. 4.
  4. Smith, “An approach to graphs of linear forms (Unpublished work style),” unpublished.
  5. H. Miller, “A note on reflector arrays (Periodical style—Accepted for publication),” IEEE Trans. Antennas Propagat., to be published.
  6. Wang, “Fundamentals of erbium-doped fiber amplifiers arrays (Periodical style—Submitted for publication),” IEEE J. Quantum Electron., submitted for publication.
  7. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.
  8. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interfaces(Translation Journals style),” IEEE Transl. J. Magn.Jpn., vol. 2, Aug. 1987, pp. 740–741 [Dig. 9th Annu. Conf. Magnetics Japan, 1982, p. 301].
  9. Young, The Techincal Writers Handbook. Mill Valley, CA: University Science, 1989.
  10. (Basic Book/Monograph Online Sources) J. K. Author. (year, month, day). Title (edition) [Type of medium]. Volume(issue).       Available: http://www.(URL)
  11. Jones. (1991, May 10). Networks (2nd ed.) [Online]. Available: http://www.atm.com
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   [Type of medium]. Volume(issue), paging if given.          Available:   

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  1.    J. Vidmar. (1992, August). On the use of atmospheric plasmas as
  2. electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3).   
  3. 876—880. Available:

172-178

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33.

Authors:

Divij H. Patel

Paper Title:

Real-time Interactive and Artificial Intelligence

Abstract: In this modern era, artificial intelligence becomes the most essential and useful system. It is not only limited to computers, but smartphone industries, robotics, machine development, and many more areas are affected. Usually, all places have different usage, and people can feel that change in its working ability and the background process. This research is based on Artificial intelligence related to computer systems. In standard, computer-based artificial intelligence comes with the primary and similar functionality like when a person asks the question it gives an appropriate answer based on that. Also, a user must work with given interfaces like graphical or command-line. However, the present system is independent of an interface. That means a user does not need to work with limited boundaries.

Keywords: console window, interactive, keystroke capturing, Real time, send-keys, transparent interface.

References:

  1. Patel, "Copy-Paste Commands with the Additional Facilities", International Journal of Computer Applications, vol. 159, no. 4, pp. 9-13, 2017.
  2. Patel, "Spyware triggering system by particular string value" published at International Journal of Engineering Research and Development, vol. 11, Issue. 09, pp. 32-36, 2015.
  3. Patel, "Assigning shortcut keys", 3726/MUM/2014, May. 27, 2016.
  4. International Business Machines Corp, "Keystroke queuing system",    US4410957A, 1983.
  5. Paquette and E. Schlegel, "Enabling and disabling hotkeys", US7757185B2, 2010.
  6. Edwards and H. Allen, "Programming software for usability evaluation", 1997. L. Orcutt, "Voiced programming system and method", US8315864B2, 2012.
  7. Bachaalany, "SendKeys in C++ -CodeProject", Codeproject.com, 2004. [Online]. Available: https://www.codeproject.com/Articles/6819/SendKeys-in-C. [Accessed: 14- Jun- 2004].
  8.   Singley and J. Anderson, "A Keystroke Analysis of Learning      and Transfer in Text Editing", ACM SIGCHI Bulletin, vol. 20, no. 1, p. 81, 1988.
  9. Schramm, "Artificial intelligence system", US4670848A, 1987.

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34.

Authors:

Htay Aung Pyae, Win Win Aye, Chatpet Yossapol

Paper Title:

Investigation and Characterization of Iron Powders for Zero-Valent Iron (Fe0) in Synchrotron Radiations

Abstract: This study employs four differential synchrotron radiation techniques to characterize the composition of underlying Zero Valent Iron (ZVI) source in three readily commercially available iron particles (code name - M100, R12, and Scrap Iron) and highlights the importance of engaging multiple investigation methods in sourcing ZVI. With ZVI or Fe0 having reducing properties to convert harmful chemicals to harmless substances found, its widespread functional application in the environmental remediation purposes is on the rise. Consequently, attempts were being made in choosing iron powders as ZVI source in interdisciplinary researches for exploring catalytic chemical reactions of ZVI. XAS-XANES and XPS spectra revealed scrap iron could not be regarded as hopeful ZVI sources since its edges and occurrences were detected entirely in contrast against standard iron foil having noticeable valency zero, and rather resembling to iron oxides. M100 and R12 were found consisting more percentage of zero valence properties than iron foil. Homogeneity and phase identification were further investigated by mean of XRD, and discovered R12 and M100 were comparable to reference iron standards. In addition, µ-XRF uncovered possible cross contaminants existed in the samples. Finally, SEM analysis disclosed distinctive metallic morphology, formation and texture of selected iron particles. This study resolved the controversial assumption that all iron source consist of credible ZVI source for its catalytic reaction to take place. And contradictory iron oxides reactions could be highly possible on conditions when irons are not taken comparative characterization methods prior to source ZVI for requisite purpose.

Keywords: SEM, µ-XRF, XAS, XPS, XRD, ZVI.  

References:

  1. USGS, 2008, Iron ore statistics and information, US Geological Survey Minerals Information, US Department of Interior (2008) Available at: http://minerals.usgs.gov/minerals/pubs/commodity/iron_ore/ [accessed on June 8, 2018]
  2. Yellishetty, M., Ranjith, P. G., & Tharumarajah, A. (2010). Iron ore and steel production trends and material flows in the world: Is this really sustainable? Resources, conservation and recycling, 54(12), 1084-1094.
  3. Pollack, S., Kaufman, R., Crosby, W. H., & Butkiewicz, J. E. (1963). Reducing agents and absorption of iron. Nature, 199(4891), 384.
  4. Wilke, M., Farges, F., Petit, P. E., Brown Jr, G. E., & Martin, F. (2001). Oxidation state and coordination of Fe in minerals: An Fe K-XANES spectroscopic study. American Mineralogist, 86(5-6), 714-730.
  5. Desage‐El Murr, M., Fensterbank, L., & Ollivier, C. (2017). Iron and Single Electron Transfer: All is in the Ligand. Israel Journal of Chemistry, 57(12), 1160-1169.
  6. Travis, T. (1993). The Haber-Bosch process: exemplar of Twentieth century chemical industry. Chemistry and Industry, (15), 581-5.
  7. Kandemir, T., Schuster, M. E., Senyshyn, A., Behrens, M., & Schlögl, R. (2013). The Haber–Bosch process revisited: on the real structure and stability of “ammonia iron” under working conditions. Angewandte Chemie International Edition, 52(48), 12723-12726.
  8. Kozuch, S., & Shaik, S. (2008). Kinetic-Quantum Chemical Model for Catalytic Cycles: The Haber− Bosch Process and the Effect of Reagent Concentration. The Journal of Physical Chemistry A, 112(26), 6032-6041.
  9. Schulz, H. (1999). Short history and present trends of Fischer–Tropsch synthesis. Applied Catalysis A: General, 186(1-2), 3-12.
  10. Dry, M. E. (2002). The fischer–tropsch process: 1950–2000. Catalysis today, 71(3-4), 227-241.
  11. Van Der Laan, G. P., & Beenackers, A. A. C. M. (1999). Kinetics and selectivity of the Fischer–Tropsch synthesis: a literature review. Catalysis Reviews, 41(3-4), 255-318.
  12. Khin, M. M., Nair, A. S., Babu, V. J., Murugan, R., & Ramakrishna, S. (2012). A review on nanomaterials for environmental remediation. Energy & Environmental Science, 5(8), 8075-8109.
  13. Joo, S. H., & Cheng, F. (2006). Nanotechnology for environmental remediation. Springer Science & Business Media.
  14. Li, X. Q., Elliott, D. W., & Zhang, W. X. (2006). Zero-valent iron nanoparticles for abatement of environmental pollutants: materials and engineering aspects. Critical reviews in solid state and materials sciences, 31(4), 111-122.
  15. Zou, Y., Wang, X., Khan, A., Wang, P., Liu, Y., Alsaedi, A., ... & Wang, X. (2016). Environmental remediation and application of nanoscale zero-valent iron and its composites for the removal of heavy metal ions: a review. Environmental science & technology, 50(14), 7290-7304.
  16. Noubactep, C., Caré, S., & Crane, R. (2012). Nanoscale metallic iron for environmental remediation: prospects and limitations. Water, Air, & Soil Pollution, 223(3), 1363-1382.
  17. Zhang, W. X., & Elliott, D. W. (2006). Applications of iron nanoparticles for groundwater remediation. Remediation Journal, 16(2), 7-21.
  18. Ansaf, K. V. K., Ambika, S., & Nambi, I. M. (2016). Performance enhancement of zero valent iron based systems using depassivators: optimization and kinetic mechanisms. Water research, 102, 436-444.
  19. Boparai, H. K., Joseph, M., & O’Carroll, D. M. (2011). Kinetics and thermodynamics of cadmium ion removal by adsorption onto nano zerovalent iron particles. Journal of hazardous materials, 186(1), 458-465.
  20. Fu, F., Dionysiou, D. D., & Liu, H. (2014). The use of zero-valent iron for groundwater remediation and wastewater treatment: a review. Journal of hazardous materials, 267, 194-205.
  21. Cundy, A. B., Hopkinson, L., & Whitby, R. L. (2008). Use of iron-based technologies in contaminated land and groundwater remediation: A review. Science of the total environment, 400(1-3), 42-51.
  22. Zou, Y., Wang, X., Khan, A., Wang, P., Liu, Y., Alsaedi, A., ... & Wang, X. (2016). Environmental remediation and application of nanoscale zero-valent iron and its composites for the removal of heavy metal ions: a review. Environmental science & technology, 50(14), 7290-7304.
  23. Feng, Y., Zhang, Y., Quan, X., & Chen, S. (2014). Enhanced anaerobic digestion of waste activated sludge digestion by the addition of zero valent iron. Water research, 52, 242-250.
  24. Liu, Y., Wang, Q., Zhang, Y., & Ni, B. J. (2015). Zero valent iron significantly enhances methane production from waste activated sludge by improving biochemical methane potential rather than hydrolysis rate. Scientific reports, 5, 8263.
  25. Zhang, Y., Feng, Y., & Quan, X. (2015). Zero-valent iron enhanced methanogenic activity in anaerobic digestion of waste activated sludge after heat and alkali pretreatment. Waste management, 38, 297-302.
  26. Liu, Y., Zhang, Y., & Ni, B. J. (2015). Zero valent iron simultaneously enhances methane production and sulfate reduction in anaerobic granular sludge reactors. Water research, 75, 292-300.
  27. Shi, Z., Nurmi, J. T., & Tratnyek, P. G. (2011). Effects of nano zero-valent iron on oxidation− reduction potential. Environmental science & technology, 45(4), 1586-1592.
  28. Bae, S., & Hanna, K. (2015). Reactivity of nanoscale zero-valent iron in unbuffered systems: effect of pH and Fe (II) dissolution. Environmental science & technology, 49(17), 10536-10543.
  29. Carpenter, A. W., Laughton, S. N., & Wiesner, M. R. (2015). Enhanced biogas production from nanoscale zero valent iron-amended anaerobic bioreactors. Environmental engineering science, 32(8), 647-655.
  30. Chekli, L., Bayatsarmadi, B., Sekine, R., Sarkar, B., Shen, A. M., Scheckel, K. G., ... & Donner, E. Analytical characterisation of nanoscale zero-valent iron: An illustrated methodological review.
  31. Chang, Y. S. Synthesis of Fe-nano Particles Obtained by Borohydride Reduction with Solvent.
  32. Stefaniuk, M., Oleszczuk, P., & Ok, Y. S. (2016). Review on nano zerovalent iron (nZVI): from synthesis to environmental    Chemical Engineering Journal, 287, 618-632.
  33. Yang, Y., Guo, J., & Hu, Z. (2013). Impact of nano zero valent iron (NZVI) on methanogenic activity and population dynamics in anaerobic digestion. Water research, 47(17), 6790-6800.
  34. Ibrahim, S. H., & Abdulaziz, M. (2016). The Effect of Different Zero-Valent Iron Sources on Biogas Production from Waste Sludge Anaerobic Digestion. Journal of Biotechnology Research, 2(8), 59-67.
  35. Ignace, A. C., Fidèle, S., Dimon, B., Franck, Y., Lyde, T. A., Daouda, M., & Eni, A. C. Biogas Recovery from Sewage Sludge during Anaerobic Digestion Process: Effect of Iron powder on Methane yield.
  36. Zhen, G., Lu, X., Li, Y. Y., Liu, Y., & Zhao, Y. (2015). Influence of zero valent scrap iron (ZVSI) supply on methane production from waste activated sludge. Chemical Engineering Journal, 263, 461-470.
  37. Sreekanth, K. M., & Sahu, D. (2015). Effect of iron oxide nanoparticle in bio digestion of a portable food-waste digester. Journal of Chemical and Pharmaceutical Research, 7(9), 353-359.
  38. Kidkhunthod, P. (2017). Structural studies of advanced functional materials by synchrotron-based x-ray absorption spectroscopy: BL5. 2 at SLRI, Thailand. Advances in Natural Sciences: Nanoscience and Nanotechnology, 8(3), 035007.
  39. NIST, X. (2018). ray Photoelectron Spectroscopy (XPS) Database, Version 3.5. NIST X-ray Photoelectron Spectroscopy (XPS) Database, Version, 3.
  40. Scientific, H. (2014). webpage on the Internet. Oxygen.[Accessed 20 April 2015]. Available from http://xpssimplified. com/elements/oxygen. php.

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35.

Authors:

G. C. Mekalke , S. R. Basavaraddi

Paper Title:

Vibration Mitigation of Engine by Using Composite Materials

Abstract: Now a days, Vibration and composite materials are two main growing research areas in industry. Almost all the structural components subjected to dynamic loading in their working life. Vibration affects the working life of the structure, so it is very important in designing a structure. To control the structural vibration and its amplitude, it is very important to know in advance the response of the system. Composite materials give chances to designers and engineers to increase material efficiency, therefore resulting in cost reduction and better utilization of resources. Composites materials applications are wide in aerospace industries, automobile sector, manufacturing industries etc. The present study involves different types of composite materials and extensive experimental works to investigate mitigation in the vibration, generated by the engine. Composite materials are manufactured by the hand-lay-up technique. Different manufacturing techniques are there, out of which hand-lay-up method is used for current research work. From this research, it has proved that sample 1 has more vibration isolation property than traditional rubber pads.

Keywords: Composites, Mitigation, Vibrations.

References:

  1. Chavan S. S., (2014),” Study on Vibration Analysis of Composite Plate‖,” International Conference on Multidisciplinary Research & Practice, 1, 8, 407-410.
  2. Snowden J.C., (1965), “Rubber like materials, their internal damping and role in vibration,” Journal of Sound and Vibration, 2(2), 175–193.
  3. Ratnaparkhi S. U., (2013), “Vibration Analysis of Composite Plate‖,” International Journal of Multidisciplinary Educational Research,3(1),377-380.
  4. Senthil Kumar P.S., (2012), “Vibration Damping Characteristics of Hybrid Polymer Matrix Composite,” International Journal of Mechanical & Mechatronics Engineering-IJENS, 15(01), 1338-. 6549
  5. Mogal Pratap, “Design and Development of Passive Damper,” International Engineering Research Journal. 606-613
  6. Shreve D.H., (1995) “Signal Processing for Effective Vibration Analysis”, IRD Mechanalysis, Inc. Columbus, Ohio.
  7. Gosh R, (2014) “Non-woven fabric and the difference between Bonded and Needle,” Journal of Polymer and Textile Engineering (IOSR-JPTE),
  8. User manual Dewe FRF V7.0.3, © 2011 DEWESOFT

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36.

Authors:

Norhafizah Dasuki, Fairuz I Romli

Paper Title:

Malaysian Public Perception on Passenger Standing Cabin for Commercial Transport Aircraft in Domestic Flights

Abstract: With the increase in market competition among the airlines today, many of them are looking for new alternative ways to reduce the cost for their flight operations in order to offer more affordable flight services. One of the proposals that have been put forward is passenger standing cabin concept, which is expected to increase cabin capacity and reduce flight cost per passenger per flight. The perception and also reception of the Malaysian public regarding the potential implementation of this new standing cabin concept by the domestic airlines are explored through conducted public survey. The survey has been carried out at two key domestic hub airports for local low-cost airlines in Malaysia: Sultan Abdul Aziz Shah Airport and Kuala Lumpur International Airport 2. From the collected data, the Malaysian public responses are taken as encouraging and this finding highlights the existence of market demands for this standing cabin concept. The support for this new cabin idea appears to be dependent on the social demographic background of the public, with age, gender and income level are among some of the main indicators. Several factors that could improve the market acceptance of this new cabin concept are also established from the survey data such as ticket price, comfort and safety aspects.

Keywords: low-cost airlines, market perception, passenger cabin design, public survey, standing cabin

References:

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  22. Chen C-M, Liu H-M. (2017) Exploring the impact of airlines service quality on customer loyalty: Evidence from Taiwan. International Journal of Business and Management, 12(5): 36-50.
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37.

Authors:

Sri Murhayati, Hartono, Hertina, Rado Yendra, Ari Pani Desvina, Ahmad Fudholi

Paper Title:

Spatial analysis for detect gender influence on score test English language and mathematics subjects junior high school in Pekanbaru

Abstract: This paper focus on investigate the influence of gender on score test of English Language and Mathematics subjects Junior High School on Pekanbaru region. The study specifically sought to determine gender differences in students academic performances in English Language and Mathematics based on comparison spatial analysis between gender and subjects. From the mapping number of junior high school male and female students and the average of scores English and mathematic on Pekanbaru region, indicate that there were some region on Pekanbaru, namely west, north and small area in south the number of gender has influence a score test Mathematic subject. On other hand females are less mathematically capable than male. This result contrast with east region area on Pekanbaru region, the different of number of gender not influence the score test mathematics. While, almost all area of the north and a few small areas in south region, which were found that the general views are that boys and girls are suited differently to particular academic subjects. Research findings revealed that girls perform better than boys in English Language score tests, on other hand, the different of number of gender has influence the score test English Language. The difference result can be found in east region, the number of gender has not influence the ability understanding in English Languages Subject.

Keywords: Influence of gender on subject, comparison spatial analysis, mapping of number of gender, test score some subject. 

References:

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  2. D. Hu, The theory of english learning. Guangxi Education Press, 1996.
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  4. J. Li, A study on gender differences and influencing factors of high school students english learning. Fujian Normal University Press, 2005.
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  7.        N. Githua, and J.G. Mwangi, Students’ mathematics self-concept and motivation to learn mathematics: relationship and gender differences among Kenya’s secondary-schools students in Nairobi and Rift Valley provinces, International Journal of Educational Development, 23, 2003, 487–499.
  8.        W. Marsh, A.J. Martin, and J.H. Cheng, A multilevel perspective on gender in classroom motivation and environment: Potential benefits of male teachers for boys?, Journal of Educational Psychology, 100, 2008, 78–95.
  9.        V. Mullis, M.O. Martin, E.J. Gonzales, and S.J. Chrostowski, TIMSS 2003 international maths report: Findings from IEA’S trends in international mathematics and science study of the fourth and eight grades. Chestnut Hill: Boston College, 2004.
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  11. Fotheringham, M. Charlton, and C. Brunsdon, Spatial variations in school performance: a local analysis using geographically weighted regres-sion, Geographical & Environmental Modeling, 5(1), 2001, 43-66.
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  14.        Sahdan, N. Ali, and S Idrus, Topophobia wanita dan persekitaran bandar: satu pendekatan reruang   (Women’s Topophobia and Urban Environments: A Spatial Approach), Akademika, 83(1), 2013, 35–43. 
  15.        N.M. Rasidi, M. Sahani, H. Othman, R. Hod, S. Idrus, Z.M. Ali, E.A. Choy, and M.H. Rosli, Aplikasi sistem maklumat geografi untuk pemetaan reruang-masa: suatu kajian kes denggi di daerah Seremban, Negeri Sembilan, Malaysia (Application of geographical information system for spatial-temporal mapping: a case study of dengue cases in Seremban, Negeri Sembilan, Malaysia), Sains Malaysiana, 42(8), 2013, 1073–1080.
  16.        A. Shah, Neoh, Hui-min, S.S.S.A. Rahim, Z.I. Azhar, M.R. Hassan, N. Sasian, and, R. Jamal, Spatial analysis of colorectal cancer cases in Kuala Lumpur, Asian Pacific Journal of Cancer Prevention, 15, 2014, 1149–1154.
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  19. Yendra, Anofrizen, W.Z.W. Zin, A.A. Jemain, and A. Fudholi. Spatial analysis of storm behavior in Peninsular Malaysia during monsoon seasons. International Journal of Applied Engineering Research, 12(10), 2017, 2559-2566.
  20. Yendra, Anofrizen, W.Z.W. Zin, A.A. Jemain, and A. Fudholi, Neymanscott rectangular pulse (NSRP) modeling and spatial analysis of strom behavior in Penisular Malaysia, Journal of Engineering and Applied Sciences, 12(24), 2017, 7604-7611.
  21. Yendra, Anofrizen, W.Z.W. Zin, A.A. Jemain, and A. Fudholi, Long-term daily rainfall pattern in Penisular Malaysia, Journal of Engineering and Applied Sciences, 12(24), 2017, 7640-7648.
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38.

Authors:

Sangita Choudhary, Manisha Agarwal, Manisha Jailia

Paper Title:

Design Framework for Facial Gender Recognition Using MCNN

Abstract: Facial Gender Recognition that allows automatic identification of gender from facial images, plays an important role in various applications. Even though it’s a challenging task, it has gained immense popularity recently, especially with the development and popularity of social platforms and social media. The main aim of this paper is to use the proposed framework to classify the facial images based on their gender. The proposed framework uses a modified form deep convolution neural network (CNN), to obtain greater performance and accuracy. This frame can be used even for processing huge quantity of data. Hence by combining both modified deep convolution neural network and KNN-classifier we have created an application that can classify gender accurately. The rate of accuracy can be increased by increasing the number of layers and simultaneously training the images using back propagation. The parallel processing concept can be enhanced using this framework.

Keywords: convolution neural network (CNN), deep convolutional neural network, deep learning. 

References:

  1. , Kwak, Y., Kim, Y., Choi, C., & Kim, J. (2018). Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion. IEEE Signal Processing Letters, 25(6), 808–812. doi:10.1109/lsp.2018.2822241.
  2. Zhang, K., Tan, L., Li, Z., & Qiao, Y. (2016). Gender and Smile Classification Using Deep Convolutional Neural Networks. 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). doi:10.1109/cvprw.2016.97
  3. Haseena, S., Bharathi, S., Padmapriya, I., & Lekhaa, R. (2018). Deep Learning Based Approach for Gender Classification. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA). doi:10.1109/iceca.2018.8474919.
  4. Liu, X., Li, J., Hu, C., & Pan, J.-S. (2017). Deep convolutional neural networks-based age and gender classification with facial images. 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS). doi:10.1109/eiis.2017.8298719.
  5. Gupta, N., Gupta, A., Joshi, V., Subramaniam, L. V., & Mehta, S. (2017). Deep Attribute Driven Image Similarity Learning Using Limited Data. 2017 IEEE International Symposium on Multimedia (ISM). doi:10.1109/ism.2017.28.
  6. Singh, M., Nagpal, S., Vatsa, M., Singh, R., Noore, A., & Majumdar, A. (2017). Gender and ethnicity classification of Iris images using deep class-encoder. 2017 IEEE International Joint Conference on Biometrics (IJCB). doi:10.1109/btas.2017.8272755
  7. Hyun, C., & Park, H. (2017). Recognition of Facial Attributes Using Multi-Task Learning of Deep Networks. Proceedings of the 9th International Conference on Machine Learning and Computing - ICMLC 2017. doi:10.1145/3055635.3056618.
  8. Zibrek, K., Hoyet, L., Ruhland, K., & McDonnell, R. (2013). Evaluating the effect of emotion on gender recognition in virtual humans. Proceedings of the ACM Symposium on Applied Perception - SAP ’13. doi:10.1145/2492494.2492510
  9. Jeon, J., Park, J.-C., Jo, Y., Nam, C., Bae, K.-H., Hwang, Y., & Kim, D.-S. (2016). A Real-time Facial Expression Recognizer using Deep Neural Network. Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication - IMCOM ’16. doi:10.1145/2857546.2857642.
  10. Azzakhnini, S., Ballihi, L., & Aboutajdine, D. (2018). Combining Facial Parts For Learning Gender, Ethnicity, and Emotional State Based on RGB-D Information. ACM Transactions on Multimedia Computing, Communications, and Applications, 14(1s), 1–14. doi:10.1145/3152125.
  11. Vinay, Gupta, S., & Mehra, A. (2014). Gender specific emotion recognition through speech signals. 2014 International Conference on Signal Processing and Integrated Networks (SPIN). doi:10.1109/spin.2014.6777050.
  12. Zvarevashe, K., & Olugbara, O. O. (2018). Gender Voice Recognition Using Random Forest Recursive Feature Elimination with Gradient Boosting Machines. 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). doi:10.1109/icabcd.2018.8465466
  13. Wang, Z.-Q., & Tashev, I. (2017). Learning utterance-level      representations for speech emotion and age/gender recognition using deep neural networks. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp.2017.7953138.
  14. Azimi, M., & Pacut, A. (2018). The effect of gender-specific facial expressions on face recognition system’s reliability. 2018 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR). doi:10.1109/aqtr.2018.8402705.
  15. Yoon, W.-J., & Park, K.-S. (2011). Building robust emotion recognition system on heterogeneous speech databases. IEEE Transactions on Consumer Electronics, 57(2), 747–750. doi:10.1109/tce.2011.5955217.
  16. Narain, B., Shah, P., & Nayak, M. (2017). Impact of emotions to analyze gender through speech. 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC). doi:10.1109/ispcc.2017.8269645.

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39.

Authors:

David Paul.D, Vijayan.S.N, Navish Kumar

Paper Title:

Investigation on thermal effects of Al2O3 Nano particles mixed with water in forced convection micro channel using Computational fluid dynamics

Abstract: Micro channels are of current interest for use in compact heat exchangers, micro reactors where very high heat transfer performance is desired. These electronic equipments are virtually synonyms with modern life applications such as appliances, instruments and computers. The dissipation of heat is necessary for the proper functioning of these instruments Micro channels provide very high heat transfer coefficients because of their small hydraulic diameters. Here, an investigation of fluid flow and heat transfer in micro channels is conducted. The computational fluid dynamics (CFD) model equations will be solved to predict the hydrodynamic and thermal behaviour of Micro channel. This study will be aimed at investigation of the forced convection heat transfer and flow characteristics of water-based Al2O3nanofluids inside a horizontal circular tube in the laminar flow regime under the constant wall temperature boundary condition. The analysis will be carried out for concentrations 0.05% , and the diameter of nano particle is 40 nm. The simulation will carried out for inlet velocities range from 1.3 -6.5 m/s. analysis will be validated with experimental results provided in the literature as a part of validation. To carry out this study twodimesnional circular duct of will be taken as micro channel. The geometry of the problem and meshing of it will be made in ANSYS ICEM CFD. The models have to be solved by ANSYS Fluent 14.0 solver. The results will be shown that the use of the Al2O3nano particles leads to an enhancement in the heat transfer.

Keywords: Nanofluids, Micro Channel, Heat Transfer, Heat Exchanger, CFD.

References:

  1. Heat Transfer – A Basic Approach – Ozisik M.N., McGraw- Hill Publications, 1985
  2. C. Maxwell, Electricity and Magnetism, Clarendon, Oxford, UK, 1873.
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  7. D. Wen, Y. Ding, Experimental Investigation into convective heat transfer of nanofluids at the entrance region under laminar flow conditions, International Journal of Heat Mass Transfer 47 (2004)5181–5188.

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40.

Authors:

Rochdi Kerkeni, Anis Mhalla, Kais Bouzrara

Paper Title:

Contribution to the Monitoring of an Automated Production System by Hybrid Interpreted Petri Nets

Abstract: This article proposes the establishment of monitoring and diagnosis tools in order to implement an automated system. The purpose of monitoring task is to preserve the production equipments quality and personnel safety. The study proposed in this paper consists in developing algorithms, based on Hybrid Signal Interpreted Petri Nets (HSIPN) for monitoring the quality of bobbins obtained by a winding machine. This monitoring approach is integrated into maintenance decision process.

Keywords: monitoring, interpreted Petri Nets, wires quality, PLC, coils shape

References:

  1. Vatani, M., & Doustmohammadi, A. (2015). decomposition of first-order hybrid petri nets for hierarchical control of manufacturing systems. journal of Manufacturing Systems, 35, 206-214..
  2. Li, H., Yang, H., Yang, B., Zhu, C., & Yin, S. (2018). Modelling And Simulation Of Energy Consumption Of Ceramic Production Chains With Mixed Flows Using Hybrid Petri Nets. International Journal Of Production Research, 56(8), 3007-3024.
  3. Lu, X., Zhou, M., Ammari, A. C., & Ji, J. (2016). Hybrid Petri Nets For Modeling And Analysis Of Microgrid Systems. IEEE/CAA Journal Of Automatica Sinica, 3(4), 349-356
  4. Hu, H., Liu, Y., & Zhou, M. (2015). Maximally Permissive Distributed Control Of Large Scale Automated Manufacturing Systems Modeled With Petri Nets. IEEE Transactions On Control Systems Technology, 23(5), 2026-2034
  5. Vieting, P., De Lamare, R. C., Martin, L., Dartmann, G., & Schmeink, A. (2018, June). An Adaptive Learning Approach To Parameter Estimation For Hybrid Petri Nets In Systems Biology. In 2018 IEEE Statistical Signal Processing Workshop (SSP) (Pp. 543-547)
  6. Dotoli, M., Epicoco, N., Falagario, M., & Cavone, G. (2016). A Timed Petri Nets Model For Performance Evaluation Of Intermodal Freight Transport Terminals. IEEE Transactions On Automation Science And Engineering, 13(2), 842-857.
  7. Borges, M. U., & Lima, E. J. (2018). Conversion Methodologies From Signal Interpreted Petri Nets To Ladder Diagram And C Language In Arduino. International Journal Of Mechanical Engineering Education, 0306419018759921.
  8. Vázquez, C. R., Gómez-Gutiérrez, D., Ramírez-Teviño, A., & Navarro, M. (2015). Eventual Generic Observability In Linear Hybrid Systems With Discrete Dynamic Modeled By Petri Nets. IFAC-Papersonline, 48(27), 47-53.

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41.

Authors:

Dheerendra Vikram Singh ,Tikendra N Verma,

Paper Title:

Energy Analysis of Water-Lithium Chloride (LiCl-H2O) operated Absorption Refrigeration System using ANN Approach.

Abstract: The objective of this work is to exploit artificial intelligence for performing energy analysis of absorption refrigeration system (ARS) with water-lithium chloride as working fluid. Energy analysis of water-lithium chloride operated vapour absorption refrigeration system is a very intricate process due to limited thermodynamic property data points and analytical functions required for estimating the thermodynamic properties of untraditional working fluid pairs. These equations usually involve the higher order complex partial differential equations and cannot be solved using simple mathematics and are time consuming too. With the help of ANN, authors have formulated new mathematical equations for estimating thermodynamic properties of each salient state of the ARS cycle. Heat load in major component of the ARS and first law based performance indexes are calculated in this analysis. The maximum difference between the predicted results and experimental data of thermodynamic properties are less than 1%. Value of the coefficient of multiple determinations is 1 for test data set and can be considered satisfactorily for using ANN in vapour absorption refrigeration system. 

Keywords: Energy Analysis, ANN, ARS, Water-Lithium Chloride, COP.

References:

  1. Maryami, A.A. Dehghan,” An exergy based comparative study between LiBr/water absorption refrigeration systems from half effect to triple effect”, Applied Thermal Engineering, 124 (2017) 103–123.
  2. D. Misra, P.K. Sahoo, S. Sahoo, A. Gupta, Thermoeconomic optimization of a single effect               water /LiBr vapour absorption refrigeration system, Int.J.Refrig  26(2)(2003)158-169.
  3. T.Chua, H.K.Toh, K.C.Ng, Thermodynamic modeling of an ammonia/water absorption
  4. Int.J.Refrig 25(1) (2002) 896-906.
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  6. system with geothermal energy: an experimental study, Energs Convers Manage 41(1)(2000)
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  9. system, Applied Thermal Engineering 17(3)(1997) 211-22.
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  14. Arzu Sencan, Kemal A. Yakut, Soteris A. Kalogirou, Thermodynamic analysis of absorption systems using artificial neural network, Renewable Energy 31 (2006) 29–43.
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42.

Authors:

Frederick Egyin Appiah-Twum, Jerry John Kponyo, Isaac Acquah.

Paper Title:

Intelligent Traffic Management System Based on Historical Data Analysis

Abstract: The problem of vehicular traffic congestion is ubiquitous yet non-trivial. It is increasingly worsening by the day all around the world with severe vehicular traffic taking its toll on all road users. With the upsurge in urban traffic jams, innovative control strategies are therefore essential to allow efficient flow of vehicular movement. It is thus not surprising that a myriad of novel control strategies has been developed over the past years to manage the ever-growing urban gridlock. Many of the currently used traffic control strategies are based on the relatively inefficient fixed-time traffic systems, like in the case of Ghana, or on a central traffic-responsive control system, which is challenging to implement and even much more difficult to maintain. As a consequence of inefficiencies in traffic control, road users are saddled with inconveniently longer waiting times in queues. To mitigate this problem, we proposed a distributed artificial intelligence and multi-agent system as a viable approach to manage the traffic menace. The proposed system uses historical data for traffic management and was designed and implemented using Simulation of Urban Mobility (SUMO) software. The result obtained in the comparison of the current fixed time-controlled system and designed system clearly indicated that the proposed system outperformed the fixed-time cycle controllers in every key performance index selected for evaluation.

Keywords: Traffic management system, intelligent transportation systems, fixed timed controllers, iterative tuning strategy, multi connect architecture associative memory.

References:

  1. Wang, Y., Wang, D., Jin, S., Xiao, N., Li, Y., & Frazzoli, E. (2016). Iterative tuning strategy for setting phase splits with anticipation of traffic demand in urban traffic network. IET Control Theory & Applications, 10(12), 1469-1479.
  2. Chrobok, O. Kaumann, J. Wahle, and M. Schreckenberg, “Different methods of traffic forecast based on real data,” European Journal of Operational Research, vol. 155, no. 3, pp. 558–568, 2004.
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  5. Taale, M. Preuß, T. Back, A. Eiben, J. de Graaf and C. Schippers, "Optimizing traffic light controllers by means of evolutionary algorithms.", EUFIT, 1999
  6. Lee, K. Lee, K. Seong, C. Kim, and H. Lee-Kwang, "Traffic control of intersection group based on fuzzy logic.", Proceedings of the 6th International Fuzzy Systems Association World Congress, pp. 465–468., 2018.
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43.

Authors:

Pooja M R, Pushpalatha M P.

Paper Title:

A Predictive framework for the assessment of Asthma control level

Abstract: Asthma is a chronic respiratory disease that is reversible in nature and hence identification of the level of control on the disease can be an important intervention to reduce the morbidity and mortality of the disease. We propose a predictive framework that efficiently predicts the asthma control levels in patients by identifying cells and cytokines in bronchoalveolar lavage (BAL) that contribute significantly to the differences in the controls. We apply various regularized regression techniques to infer the best performing technique on the dataset under consideration. Further, a two class classification problem to distinguish controlled and uncontrolled asthma subjects was handled by deploying binary classifiers and the best performing classifier was adopted. The framework involved the application of feature scoring techniques to identify the risk factors. The work is validated on the data containing subjects including healthy, controlled and uncontrolled subjects, acquired from the Department of Asthma, Allergy and Lung Biology, King’s College London School of Medicine, U.K. which was available on the Dryad repository.

Keywords: Regularization, Dryad, Feature scoring, Binary classifiers, Performance metrics.

References:

  1. Zou, Hui, and Trevor Hastie. "Regularization and variable selection via the elastic net." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67.2 (2005): 301-320.
  2. Yuan, Ming, and Yi Lin. "Model selection and estimation in regression with grouped variables." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 68.1 (2006): 49-67.
  3. Kim, Seyoung, Kyung-Ah Sohn, and Eric P. Xing. "A multivariate regression approach to association analysis of a quantitative trait network." Bioinformatics 25.12 (2009): i204-i212.
  4. Brasier, Allan R., et al. "Predicting intermediate phenotypes in asthma using bronchoalveolar lavage‐derived cytokines." Clinical and translational science 3.4 (2010): 147-157.
  5. Schmid, Matthias, et al. "Estimation and regularization techniques for regression models with multidimensional prediction functions." Statistics and Computing 20.2 (2010): 139-150.
  6. Xu, Zongben, et al. "L 1/2 regularization." Science China Information Sciences 53.6 (2010): 1159-1169.
  7. MartíNez-MartíNez, José M., et al. "Regularized extreme learning machine for regression problems." Neurocomputing 74.17 (2011): 3716-3721.
  8. Savenije, Olga EM, et al. "Predicting who will have asthma at school age among preschool children." Journal of Allergy and Clinical Immunology 130.2 (2012): 325-331.
  9. Chatzimichail, Eleni, et al. "An intelligent system approach for asthma prediction in symptomatic preschool children." Computational and mathematical methods in medicine 2013 (2013).
  10. Yu, Qi, et al. "Regularized extreme learning machine for regression with missing data." Neurocomputing 102 (2013): 45-51.
  11. Pescatore, Anina M., et al. "A simple asthma prediction tool for preschool children with wheeze or cough." Journal of allergy and clinical immunology 133.1 (2014): 111-118.
  12. Mozaffari, Ahmad, and Nasser L. Azad. "Optimally pruned extreme learning machine with ensemble of regularization techniques and negative correlation penalty applied to automotive engine coldstart hydrocarbon emission identification." Neurocomputing 131 (2014): 143-156.
  13. Hosoki, Koa, et al. "Analysis of a panel of 48 cytokines in BAL fluids specifically identifies IL-8 levels as the only cytokine that distinguishes controlled asthma from uncontrolled asthma, and correlates inversely with FEV1." PloS one 10.5 (2015): e0126035.
  14. Chen, Robert, et al. "Cloud-based predictive modeling system and its application to asthma readmission prediction." AMIA Annual Symposium Proceedings. Vol. 2015. American Medical Informatics Association, 2015.
  15. Goldstein, Benjamin A., Ann Marie Navar, and Rickey E. Carter. "Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges." European heart journal 38.23 (2016): 1805-1814.
  16. Finkelstein, Joseph. "Machine learning approaches to personalize early prediction of asthma exacerbations." Annals of the New York Academy of Sciences 1387.1 (2017): 153-165
  17. Hosoki, Koa, et al. "Analysis of a panel of 48 cytokines in BAL fluids specifically identifies IL-8 levels as the only cytokine that distinguishes controlled asthma from uncontrolled asthma, and correlates inversely with FEV1." PloS one 10.5 (2015): e0126035.
  18. Pooja, M. R., and M. P. Pushpalatha. "An Empirical Analysis of Machine Learning Classifiers for Clinical Decision Making in Asthma." International Conference on Cognitive Computing and Information Processing. Springer, Singapore, 2017.

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44.

Authors:

Dillwyn s , s elizabeth amudhini stephen, c. Bazil wilfred.

Paper Title:

Use of a Commercial Enzyme for the Clarification of Banana Juice – Optimization Conditions.

Abstract: For the banana juice the enzymatic clarification process are to be optimized as the raw banana juice are highly viscous, turbid and are grey in colour. Response Surface Methodology was employed for the optimization in which Box-Benken method is used. For the treatment of banana juice, pectinase was used having different treatment variables of concentration of enzyme (0.01 - 0.1%), temperature (30 - 50ºC), time (30 – 120 min) and pH (4 – 5.5). The effect of these treatments is studied using the Box – Benken method which has a great impact on the clarity, filterability, turbidity, viscosity and color. To describe the physical characteristic changes in order to the independent variables, the significant regression model was established having R2 that is the coefficient of determination. For the enzymatic treatment of banana juice the optimum conditions for clarification are 0.055% concentration of enzyme at 40ºC for 75 mins having 4.7pH, as per the response surface methodology and the contour plots.

Keywords: Enzyme Concentration, incubation time, incubation temperature, pH, Response surface methodology.

References:

  1. Alvarez, S., Alvarez, R., Riera, F. A., & Coca, J. (1998). Influence of depectinizationon apple juice ultrafiltration. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 138, 377–382.
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  4. Jagtiani, J., Chang, H. T., & Sakai, W. S. (1988). Guava. In Tropical fruit processing. New York: Academic Press.
  5. Kilara, A. (1982). Enzymes and their uses in the processed apple industry: a review.Process Biochemistry, 23, 35–41.
  6. Little, T. M., & Hills, F. J. (1978). Agricultural experimentation design and analysis. New York: John Wiley, p. 170.
  7. Mendenhall, W. (1975). Introduction to Probability and Statistics (4th ed.). North Settuate, MA: Duxbury Press, p. 273.
  8. Viquez, F., Laetreto, C., & Cooke, R. D. (1981). A study of the production of clarified banana juice using pectinolytic enzymes. Journal of Food Technology, 16, 115–125.
  9. Yusof, S., & Ibrahim, N. (1994). Quality of soursop juice after pectinase enzyme treatment. Food Chemistry, 51, 83–88

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45.

Authors:

R.Emilin Renita, S.Elizabeth Amudhini Stephen.

Paper Title:

Optimization of Freeze Drying of Oyster Mushroom and Composition of Extruded Snack Incorporated with Oyster Mushroom Flour Using Response Surface Methodology.

Abstract: Mushroom is a highly perishable product, so various methods were developed for the preservation and storage of it. In our study the optimizing conditions for the freeze drying of oyster mushroom and incorporating this oyster mushroom flour in addition with either the rice flour or corn flour to develop an extruded product. For obtaining the optimizing conditions Central Composite Design is employed that comes under the Response Surface Methodology. With coefficient of determination R², the significant regression models are describing the changes on the physical characters, with respect to the independent variables that were established. For the freeze drying the parameters of the processing conditions are varied like for temperature of -30 ºC to -50ºC and time from 27 to 35 hrs. The independent variables that had an effect on the processing conditions are colour, particle density and porosity. The optimum processing conditions obtained according to Response surface methodology for freeze drying are -40ºC at 31 hrs. In the development of extruded product incorporated with the oyster mushroom flour to that of the corn flour or rice flour the compositions are varied from 5 to 20%. The response variables that had an impact due to the varying composition in raw material where speed, pressure and temperature. The optimum percent of oyster mushroom flour to that of the corn flour or rice flour are 10 and 12.5 respectively.

Keywords: Oyster mushroom, freeze drying, extruded product, colour, particle density, porosity, speed, temperature and pressure.

References:

  1. Aida F M N A., Shuhaimi M., Yazid M., and Maarf A G. (2009). Mushroom as a potential source of prebiotics: a review. Trends on Food Science and Technology, 20, 567 – 575.
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  6. Choi Y., Lee S M., Chun J., Lee H B., Lee J. (2006). Influence of heat treatment on the antioxidant activities and polyphenolic compounds of shiitake (Lentimus edodes) mushroom. Food Chemistry, 99, 381 – 387.
  7. Choudhury M H., Chakrabarty R and Raychaudhuri U. (2011). Optimization of rice flour (Oryza Sativa L) and Lali (Metapenacopsis Stridulans) Extrusion by Response Surface Methodology. International Journal if Advanced Engineering Technology IJAET/ Vol. II/ Issue I/ January – March: 1- 11.
  1. Coutinho L S., Batista J E R., Caliari M., and Junior M S S. (2013). Optimization of extrusion variables for the production of snacks from by-products of rice and soybean. Food Technology (Campinas) Vol 33 no 4: 705 – 712.
  2. Czapski J., and Szudyga K. (2000). Frozen mushroom quality as affected by strain, flesh, treatment before freezing and time of storage. Journal of Food Science, 65(4): 722 – 725.
  3. Guha M., Ali Z S., and Bhattacharya S., (2003). Screening of variables for extrusion of rice flour employing a Placket Burman Design. Journal of Food Engineering 57: 135 – 144.
  4. Gothendapani L., Parvati K., Kennedy Z J. (1997). Evaluation of different methods of drying on the quality of oyster mushroom (Pleurotus Species). Drying Technology. 15(618): 1995 – 2004.
  5. Haritha D., Vijayalakshmi V., and Gulla S. (2014). Development and evaluation of garlic incorporated ready to eat extruded snacks. Journal of Food Science Technology. 51(11): 3425 – 3431.
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  7. Krokida, M.K., Karathanos, V.T. & Maroulis, Z.B. (1998b). Effect of freeze-drying conditions on shrinkage and porosity of dehydrated agricultural products. Journal of Food Engineering, 35, 369–380.
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  9. Martinez-Soto, G., Ocanna-Camacho, R. & Paredes-Lopez, O. (2001). Effect of pretreatment and drying on the quality of oyster mushrooms (Pleurotus ostreatus). Drying Technology, 19, 661–672.
  10. Mehta B K., Jain S K., Sharma G P., Doshi A., Jain H K. (2011). Cultication of button mushroom and its processing: an techno-economic feasibility. International Journal of Advanced Biotechnology and Research. 2(1): 201 – 207.
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46.

Authors:

Sonam Gupta, Vishwachi.

Paper Title:

Identification of File and Directory Level Near-Miss Clones For Higher Level Cloning.

Abstract: The presence of code cloning, which speaks to duplicate segments of code, has been archived to happen much of the time in programming frameworks. The principle reason for cloning is reusing the part of the code that plays out some usefulness by duplicating and rewriting it to another area in the source code. The concentration here is to detect duplication in software program, which is a noteworthy reason for poor structure in programs. The key, novel part of our duplication- detection approach is detection of near-miss clones at higher level of granularity i.e, the directory and file level. Our work is a progress over past work around there as earlier the granularity of clone detection was method level. The technique adopted incorporates a novel hybrid approach using Abstract Syntax Tree and metrics to achieve precision. The results indicate the ability to detect Type-1, 2, 3 clones at directory and folder level.

Keywords: Levenshtein distance, re-ordered clones, syntactic clones.

References:

  1. Baxter, Ira D., et al. "Clone detection using abstract syntax trees." Software Maintenance, 1998. Proceedings., International Conference on. IEEE, 1998.
  2. Bellon, Stefan, et al. "Comparison and evaluation of clone detection tools." IEEE Transactions on software engineering 33.9 (2007).
  3. Chatterji, Debarshi, Jeffrey C. Carver, and Nicholas A. Kraft. "Code clones and developer behavior: results of two surveys of the clone research community." Empirical Software Engineering 21.4 (2016): 1476-1508.
  4. Choudhary V and Gupta S (2017). A novel approach in detecting code clones in Java using DFS. International Journal of Advanced and Applied Sciences, 4(5): 26-29
  5. Cordy, James R., and Chanchal K. Roy. "The NiCad clone detector." Program Comprehension (ICPC), 2011 IEEE 19th International Conference on.IEEE, 2011.
  6. Cuomo, Antonio, Antonella Santone, and Umberto Villano. "CD-Form: A clone detector based on formal methods." Science of Computer Programming 95 (2014): 390-405.
  7. Gupta, Sonam, and P. C. Gupta. "A Novel Approach to Detect Duplicate Code Blocks to Reduce Maintenance Effort." International Journal of Advanced Computer Science & Applications 1.7 (2016): 311-314.
  8. Kodhai, Egambaram, and Selvadurai Kanmani. "Method-level code clone detection through LWH (Light Weight Hybrid) approach." Journal of Software Engineering Research and Development 2.1 (2014):
  9. Kontogiannis, Kostas. "Evaluation experiments on the detection of programming patterns using software metrics." Reverse Engineering, 1997. Proceedings of the Fourth Working Conference on. IEEE, 1997.
  10. Mondal, Manishankar, et al. "An empirical study of the impacts of clones in software maintenance." Program Comprehension (ICPC), 2011 IEEE 19th International Conference on. IEEE, 2011.
  11. Rieger, Matthias, Stéphane Ducasse, and Michele Lanza. "Insights into system-wide code duplication." Reverse Engineering, Proceedings. 11th Working Conference on. IEEE, 2004.
  12. Roy, Chanchal K. "Detection and analysis of near-miss software clones." Software Maintenance, 2009. ICSM 2009. IEEE International Conference on. IEEE, 2009.
  13. Roy, Chanchal Kumar, and James R. Cordy. "A survey on software clone detection " Queen’s School of Computing TR 541.115 (2007): 64-68.
  14. Selim, Gehan MK, et al. "Studying the impact of clones on software defects." Reverse Engineering (WCRE), 2010 17th Working Conference on. IEEE, 2010.
  15. Vishwachi and Sonam Gupta. Literature Survey of Software Clones. International Journal of Computer Applications 153(4):1-7, November.
  16. Wettel, Richard, and RaduMarinescu. "Archeology of code duplication: Recovering duplication chains from small duplication " Seventh International Symposiumon Symbolic and Numeric Algorithms for Scientific Computing (SYNASC'05). IEEE,2005.
  17. Li, Z. O., & Sun, J. (2010, April). A metric space based software clone detection approach. Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on (pp. 393-397).
  18. Roy, Chanchal K., and James R. Cordy. "NICAD: Accurate detection of near-miss intentional clones using flexible pretty-printing and code normalization." Program Comprehension, 2008. ICPC 2008. The 16th IEEE International Conference on. IEEE, 2008.
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  22. Kanagalakshmi, K., and R. Suguna. "Software Refactoring Technique for Code Clone Detection of Static and Dynamic Website."
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  28. Pate, Jeremy R., Robert Tairas, and Nicholas A. Kraft. "Clone evolution: a systematic review." Journal of software: Evolution and Process 3 (2013): 261-283.
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47.

Authors:

Upendra Rajak , Prerana Nashine, Tikendra Nath Verma.

Paper Title:

Effect of Fuel Injection Pressure in a Diesel Engine Using Microalgae-Diesel Emulsion.

Abstract: In this paper to investigate the effect of fuel injection pressure in a diesel engine running on microalgae in naturally aspirated, direct injection diesel engines, parameters of pure diesel (D100), 80% diesel + 20% microalgae spirulina (B20), 60% diesel + 40% microalgae spirulina (B40) and pure microalgae spirulina biodiesel (B100). The fuel injection pressures (FIP) varies from 18 to 26 MPa and stationary injection timings. The result shows that optimum effect can be obtained in 22 MPa FIP, with B20 bio-diesel and increasing the percentage of spirulina biodiesel, there is a decrease in exhaust gas temperature, while there is a marginal increase in SFC. The remarkable emissions lower 22.9% of particulate matter and there is an increase in CO2 emission by 4% in the blend ratio B20% at full load operating condition.

Keywords: Upendra Rajak , Prerana Nashine, Tikendra Nath Verma.

References:

  1. Wang, H. Liu, C. F. Lee, Particulate matter emission characteristics of diesel engines with biodiesel or biodiesel blending : A review. Renewable and Sustainable energy Reviews, 2016, 64, 569–581.
  2. Alptekin, Emission, injection and combustion characteristics of biodiesel and oxygenated fuel blends in a common rail diesel engine. Energy, 2017, 119, 44–52.
  3. An, W. Yang, J. Li, A. Maghbouli, K. J. Chua, S. K. Chou, A numerical modeling on the emission characteristics of a diesel engine fueled by diesel and biodiesel blend fuels. Applied Energy, 2014, 130, 458-465.
  4. Mohan, W. Yang, W. Yu, K. L. Tay, S. K. Chou, Numerical investigation on the effects of injection rate shaping on combustion and emission characteristics of biodiesel fueled CI engine. Applied Energy, 2015, 160, 737-745.
  5. Lin, W. Yang, J. Li, D. Zhou, W. Yu, F. Zhao, S. Kiang, Numerical investigation on the combustion and emissions of a kerosene-diesel fueled compression ignition engine assisted by ammonia fumigation. Applied Energy, 2017, 204, 1476-1488.
  6. Zhao, W. Li, W. Zhuge, Y. Zhang, Y. Yin, Numerical study on steam injection in a turbo compound diesel engine for waste heat recovery. Applied Energy, 2016, 185, 506-518.
  7. Rajak, P. Nashine, T. S. Singh, T. N. Verma, Numerical investigation of performance, combustion and emission characteristics of various biofuels. Energy Conversion and Management, 2018, 156, 235-252.
  8. F. Al-dawody, S. K. Bhatti, Optimization strategies to reduce the biodiesel NOX effect in diesel engine with experimental verification. Energy Conversion Management, 2013, 68, 96–104.
  9. Banerjee, S. Kumar, Numerical investigation of stratified air / fuel preparation in a GDI engine. Applied Thermal Engineering, 2016, 104, 414–428.
  10. Contino,J. B. Masurier, F. Foucher, T. Lucchini, G. D. Errico, P. D, CFD simulations using the TDAC method to model iso-octane combustion for a large range of ozone seeding and temperature conditions in a single cylinder HCCI engine. Fuel, 2014, 137, 179–184.
  11. Guo, H. Li, J. Zhao, X. Li, J. Yan, Numerical simulation study on optimizing charging process of the direct contact mobilized thermal energy storage. Applied Energy, 2013, 112, 1416-1423.
  12. Petranovic, M. Vujanovi, N. Dui. Towards a more sustainable transport sector by numerically simulating fuel spray and pollutant formation in diesel engines. Journal of Cleaner Production, 2015, 88, 272–279.
  13. Salam, T. N. Verma, Appending empirical modelling to numerical solution for behavior characterization of microalgae biodiesel. Energy Conversion and Management, 2019, 180, 496-510.
  14. Mohan, W. Yang, V. Raman, V. Sivasankaralingam, S. K. Chou, Optimization of biodiesel fueled engine to meet emission standards through varying nozzle opening pressure and static injection timing. Applied Energy, 2014, 130, 450-457.
  15. Han, K. Li, Y. Duan, H, Lin, Z. Huang, Numerical study on fuel physical effects on the split injection processes on a common rail injection system. Energy Conversion and Management, 2017, 134, 47–58.
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  19. Lin, W. Yang, J. Li, D. Zhou, W. Yu, F. Zhao, S. Kiang Numerical investigation on the combustion and emissions of a kerosene-diesel fueled compression ignition engine assisted by ammonia fumigation. Applied Energy, 2017, 204, 1476-1488.
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  26. Singh, T S & Verma T N., (2019). Taguchi design approach for extraction of methyl ester from waste cooking oil using synthesized CaO as heterogeneous catalyst: Response surface methodology optimization. Energy Conversion and Management, 182, 383-397.
  27. Rajak, U., & Verma, T. N. (2019). A comparative analysis of engine characteristics from various biodiesels: Numerical study. Energy Conversion and Management, 180, 904-923.

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48.

Authors:

J. Aswini, N. Malarvizhi, T. Kumanan.

Paper Title:

A Dynamic Resource Allocation Framework Based On Workload Prediction Algorithm For Cloud Computing.

Abstract: The conventional load balancing algorithms feature severe limitations and drawbacks in cloud environments. In order to address these challenges, researchers have proposed prediction algorithms using genetic algorithms and genetic programming. These algorithms aim to simplify task scheduling in cloud platforms characterized by a large volume of users. The proposed scheme meets the requirements for inter-nodes load balancing. Simulations to compare the performance of the proposed scheme and the AGA demonstrated the effectiveness and validity of the proposed method in cloud computing.

Keywords: Cloud computing, Resource allocation, Workload prediction, CloudSim

References:

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  3. Calheiros, R. N., Ranjan, R., De Rose, C. A. F., & Buyya, R. (2009). CloudSim: A novel framework for modeling and simulation of cloud computing infrastructure and services. Technical Report GRIDS-TR-2009-1, Grid Computing and Distributed Systems Laboratory.
  4. Chase, J. S., Anderson, D. C., Thakar, P. N., Vahdat, A. M., & Doyle, R. P. (2001,October). Managing energy and server resources in hosting centers. In ACM SIGOPS Operating Systems Review, 35(5), 103-116.
  5. Devi, C., & Uthariaraj, R. (2016). Load balancing in cloud computing environment using improved weighted Round Robin Algorithm for non-preemptive dependent tasks. http://dx.doi.org/10.1155/2016/3896065.
  6. Doyle, B., & Lopes, C. V. (2005). Survey of technologies for Web application development. ACM Journal, 2(3), 1-43.
  7. Duggan, J., Cetintemel, U., Papaemmanouil, O. & Upfal, E. (2011). Performance prediction for concurrent database workloads. SIGMOD ’11. June 12-16, 2011, Athens, Greece. 978 (1): 337-348.
  8. Issawi, S. F., Halees, A. A., & Radi, M. (2015). An efficient adaptive load-balancing algorithm for cloud computing under bursty workloads. Engineering, Technology, & Applied Science Research, 5(3), 795-800.
  9. Jena, S. R., & Ahmad, Z. (2013). Response time minimization of different load balancing algorithms in cloud computing environment. International Journal of Computer Applications, 69(17), 22-27.
  10. LaCurts, K. L. (2014, June). Application workload prediction and placement in cloud computing systems (Unpublished doctoral dissertation). Massachusetts Institute of Technology, Cambridge Massachusetts.
  11. Lee, R., & Jeng, B. (2011). Load-balancing tactics in cloud. In Proceedings of the International Conference on Cyber-Enabled Distributed Computing and Knowledge CyberC Discovery, pp. 447-454.
  12. Mahmood, Z. (2011). Cloud computing: characteristics and deployment approaches. In the 11th IEEE International Conference on Computer and Information Technology, pp. 121-126.
  13. Mathur, S., Larji, A. A., & Goyal, A. (2017). Static load balancing using SA Max-Min algorithm. International Journal for Research in Applied Science & Engineering Technology, 5(4), 1886-1893.
  14. Nae, V., Prodan, R., & Fahringer, T. (2010, October). Cost-efficient hosting and load balancing of massively multiplayer online games. In the 11th IEEE/ACM International Conference on Grid Computing (GRID), Brussels, Belgium, pp. 9-16.
  15. Nema, R., & Edwin, S. T. (2016). A new efficient virtual machine load balancing algorithm for a cloud computing environment. International Journal of Latest Research in Engineering and Technology, 2(2), 69-75.

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49.

Authors:

Vishal Prem, Mark Sheridan Nonghuloo, Nagaraja Rao A.

Paper Title:

Optimization of Siamese Neural Networks Using Genetic Algorithm.

Abstract:The power of Deep Learning Networks has allowed us to build applications far beyond what was thought possible during our time. But the basic forms of these architectures still have their limitations in terms of the data needed and difficulty in understanding and tuning the parameters of these networks. Our project aims to deal with these limitations as effectively as possible. For this reason we have chosen to implement a Siamese Neural Network in order to overcome the requirements of classical Deep Learning based image classification. And in order to further increase the efficiency of the network, we will process it using a genetic algorithm and harness it to improve the architecture and training efficiency by letting the algorithm fine-tune the parameters to get the best possible configuration for the neural network.

Keywords: Siamese Neural Network, Genetic Algorithm, Neural Network Optimization.

References:

  1. Gyanesh Sinha, "Study of Indian Logistics Industry in Changing Global Scenario", ResearchGate, February 2016
  2. Yann LeCun, Leon Bottou, Yoshua Bengio, Patrick Haffner, "Gradient-Based Learning Applied to Document Recognition", IEEE, November 1998
  3. Dan Claudiu Ciresan, Ueli Meier, Luca Maria Gambardella, Juergen Schmidhuber, "Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition", Cornell University Library, Volume 22, Number 12, December 2010
  4. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks", Neural Information Processing Systems Conference, 2012.
  5. Eugenio Culurciello, "The History of Neural Networks", Dataconomy, April 2017. Available: Dataconomy, https://dataconomy.com
  6. Simonyan, A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” International Conference on Machine Learning, 2015.
  7. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, “Going deeper with convolutions”, IEEE Conference on ComputerVision and Pattern Recognition, 2015
  8. He, X. Zhang, S. Ren, J. Sun, “Deep residual learning for image recognition”, IEEE Conference on Computer Vision and Pattern Recognition, 2016
  9. Gregory Koch, Richard Zemel, Ruslan Salakhutdinov, "Siamese Neural Networks for One-shot Image Recognition",
  10. Sumit Chopra, Raia Hadsell, Yann LeCun, "Learning a Similarity Metric Discriminatively, with Application to Face Verification", Yann LeCun's Publications, 2005
  11. F. Man, K.S. Tang, S. Kwong, "Genetic algorithms: concepts and applications",IEEE, Volume 43, Issue 5, October 1996.
  12. J. Umbarkar, P.D. Sheth, "Crossover Operators in Genetic algorithms: A review", ICTACT Journal on Soft Computing, Volume 6, Issue 1, October 2015.

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50.

Authors:

Siti Safwana Abd Razak, Mohd Azlishah Othman, Ahmad Fauzan Kadmin.

Paper Title:

The effect of Adaptive Weighted Bilateral Filter on Stereo Matching Algorithm.

Abstract: Stereo matching process is attracted numbers of study in recent years. The process is unique and difficult due to visual discomfort occurred which contributed to effect of accuracy of disparity maps. By using multistage technique implemented most of Stereo Matching Algorithm; taxonomy by D. Scharstein and R. Szeliski, in this paper proposed new improvement algorithm of stereo matching by using the effect of Adaptive Weighted Bilateral Filter as main filter in cost aggregation stage which able contribute edge-preserving factor and robust against plain colour region. With some improvement parameters in matching cost computation stage where windows size of sum of absolute different (SAD) and thresholds adjustment was applied and Median Filter as main filter in refinement disparity map’s stage may overcome the limitation of disparity map accuracy. Evaluation on indoor datasets, latest (2014) Middlebury dataset were used to prove that Adaptive Weighted Bilateral Filter effect applied on proposed algorithm resulted smooth disparity maps and achieved good processing time.

Keywords: Bilateral Filter, Disparity map, SAD, Stereo matching.

References:

  1. Scharstein and R. Szeliski, “A Taxonomy and Evaluation of Dense Two-Frame Stereo,” Int. J. Comput. Vis. 47, vol. 47, no. 1–3, pp. 7–42, 2002.
  2. Bagnell, D. Bradley, D. Silver, B. Sofman, and A. Stentz, “Learning for autonomous navigation,” IEEE Robot. Autom. Mag., vol. 17, no. 2, pp. 74–84, 2010.
  3. Humenberger, T. Engelke, and W. Kubinger, “A Census-based stereo vision algorithm using modified Semi-Global Matching and plane fitting to improve matching quality,” in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010, 2010, pp. 77–84.
  4. Scharstein, R. Szeliski, and R. Zabih, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” in Proceedings - IEEE Workshop on Stereo and Multi-Baseline Vision, SMBV 2001, 2001, pp. 131–140.
  5. Tao, H. S. Sawhney, and R. Kumar, “A global matching framework for stereo computation,” Proc. Eighth IEEE Int. Conf. Comput. Vision. ICCV 2001, vol. 1, no. 2, pp. 532–539, 2001.
  6. Zhou and C. Hou, “Stereo matching based on guided filter and segmentation,” Optik (Stuttg)., vol. 126, no. 9–10, pp. 1052–1056, 2015.
  7. Kanade and M. Okutomi, “A stereo matching algorithm with an adaptive window: theory and experiment,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 9, pp. 920–932, 1994.
  8. Affendi, H. Ibrahim, and A. H. A. Hassan, “Stereo matching algorithm based on per pixel difference adjustment , iterative guided filter and graph segmentation,” J. Vis. Commun. Image Represent., vol. 42, pp. 145–160, 2017.
  9. Klaus, M. Sormann, and K. Karner, “Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure,” in Proceedings - International Conference on Pattern Recognition, 2006, vol. 3, pp. 15–18.
  10. Shi, H. Zhu, J. Wang, S. Y. Yu, and Z. F. Fu, “Segment-based adaptive window and multi-feature fusion for stereo matching,” J. Algorithms Comput. Technol., vol. 10, no. 1, pp. 3–11, 2016.
  11. Heiko Hirschmüller (Inst. of Robotics & Mechatronics Oberpfaffenhofen, German Aerosp. Center, Wessling, “Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information,” Comput. Vis. Pattern Recognition, 2005. CVPR 2005. IEEE Comput. Soc. Conf. (Volume 2), pp. 807–814, 2005.
  12. Weiss, “Fast median and bilateral filtering,” in ACM SIGGRAPH 2006 Papers on - SIGGRAPH ’06, 2006, p. 519.
  13. Mattoccia, F. Tombari, and L. Di Stefano, “Stereo vision enabling precise border localization within a scanline optimization framework,” in IEEE Asian Conference on Computer Vision, 2007, pp. 517–527.
  14. Mei, X. Sun, W. Dong, H. Wang, and X. Zhang, “Segment-tree based cost aggregation for stereo matching,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, pp. 313–320.
  15. Z. Brown, D. Burschka, and G. D. Hager, “Advances in computational stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 8. pp. 993–1008, 2003.
  16. A. Hamzah and H. Ibrahim, “Literature Survey on Stereo Vision Disparity Map Algorithms,” vol. 2016, 2016.

 

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51.

Authors:

V. Veera Nagi Reddy, D.V. Ashok Kumar Venkata Reddy kota.

Paper Title:

IRP Theory based UPQC for Power Quality Profile Enhancement in Distribution System

Abstract: Power system network will never operate under ideal conditions but contains power quality problems. FACTS based controllers like DVR, DSTATCOM and APF (active power filter) are some of the compensating devices used to improve power quality. UPQC (unified power quality conditioner) is a back-to-back converter topology with common DC-link voltage employed to improve power quality. This paper presents multi-level (three-level) UPQC as power quality conditioner in distribution system. The pulses from hysteresis control trigger UPQC where reference currents are generated using IRP (instantaneous reactive power) control theory for shunt controller and SRF theory for series controller. Analysis is presented with the conditions like UPQC as series compensator conditioning source voltage, UPQC as shunt compensator compensating harmonics in source current and UPQC as (series and shunt) compensator conditioning source voltage and harmonics. System proposed is build and analysis is presented using MATLAB/SIMULINK software.

Keywords: Current compensation, Harmonics compensation, IRP theory, Power quality, UPQC, Voltage compensation.

References:

  1. Hirofumi Akagi, “New trends in active filters for power conditioning,” IEEE Trans. Industry Applications, vol. 32, no. 6, Nov/Dec 1996.
  2. Mauricio Aredes, Klemens Heumann, Edson H. Watanabe, “An Universal Active Power Line Conditioner,” IEEE Trans. Power Delivery, vol. 13, no. 2, April 1998.
  3. Hideaki Fujita, Hirofumi Akagi, “The Unified Power Line Conditioner: The Integration of Series- and Shunt-Active Filters,” IEEE Trans. Power Elelctronics, vol. 13, no. 2, March 1998.
  4. Zainal Salam, Tan Perng Cheng and Awang Jusoh, “Harmonics Mitigation Using Active Power Filter: A Technological Review”, ELEKTRIKA, VOL. 8, NO. 2, 200.6, 17‐26.
  5. Khalid, B.Dwivedi, “Comparative Critical Analysis of Advanced Controllers used for Active Power Filter,” National Conference on Power Electronics and Renewable Energy Systems, PEARES, Kalavakkam, 2009.
  6. S. Shen, H. Zheng, Y. Lin and W. Zhao, "UPQC Harmonic Detection Algorithm Based on Improved p-q Theory and Design of Low-Pass Filter," 2018 Chinese Control And Decision Conference (CCDC), Shenyang, China, 2018, pp. 5156-5160
  7. A. O. d. Silva, L. B. Garcia Campanhol and V. d. Souza, "Dynamic Performance Evaluation of a dual UPQC Operating Under Power Quality Disturbances," PCIM Europe 2018; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management, Nuremberg, Germany, 2018, pp. 1-8.
  8. Muneer, J. Sukumaran and A. Bhattacharya, "Investigation on reduced DC link voltage based UPQC for harmonic compensation under unbalanced load," 2017 International Conference on Technological Advancements in Power and Energy ( TAP Energy), Kollam, India, 2017, pp. 1-6
  9. Ye, H. B. Gooi and F. Wu, "Optimal Design and Control Implementation of UPQC Based on Variable Phase Angle Control Method," in IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3109-3123, July 2018.

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52.

Authors:

Mohan Kantharia, Pankaj Mishra, M.K. Trivedi

Paper Title:

Strength of Cement Mortar Using Nano Oxides: An Experimental Study

Abstract: Cement mortar is one of the important materials which plays key role in maximising the durability of a building. Strength, permeability, thermal insulation, freeze and thaw resistance, Chloride-ion penetration, carbonation, hydration, weathering are the factors affecting the overall performance of the cement mortar. So many new ingredients are being used as admixture have been investigated for making the mortar of high performance. Various nano materials are constituting the advance engineering products with smart working. Some industrial waste like blast furnace slag, fly ash, silica fume, also incorporated in cement concrete for making mortar economical. In this Experimental study nano oxide of Zinc, aluminium, and silicon have been investigated in cement mortar and compared. It is found that all nano materials are not having the similar effect.

Keywords: Nano Zinc oxide, compressive strength, cement mortar.

References:

  1. R. Arefi, S. R. Zarchi, “Synthesis of Zinc Oxide Nanoparticles and Their Effect on the Compressive Strength and Setting Time of Self-Compacted Concrete Paste as Cementitious Composites”, 2012, International Journal of Molecular Sciences ISSN 1422-0067, pp 4340-4350.
  2. Behfarnia, A. Keivan, A. Keivan, “The Effects of Tio2 and ZnO Nanoparticles on Physical and Mechanical Properties of Normal Concrete”, 2013, Asian Journal of Civil Engineering (BHRC) Vol. 14, No. 4, pp 517-531.
  3. Riahi, A. Nazari, “Physical, mechanical and thermal properties of concrete in different curing media containing ZnO2 nanoparticles”, 2011, Energy and Buildings 43, 1977–1984.
  4. Nochaiya, Y. Sekine, S. Choopun, A. Chaipanich, “Microstructure, characterizations, functionality and compressive strength of cement-based materials using zinc oxide nanoparticles as an additive”, 2015, Journal of Alloys and Compounds 630, pp 1–10.
  5. Nivethitha, S Dharmar, “Influence of Zinc Oxide Nanoparticle on Strength and Durability of Cement Mortar”, 2016, International Journal of Earth Sciences and Engineering ISSN 0974-5904, Vol. 09, No. 03, pp. 175-181.
  6. Ghafari, S.A. Ghahari, Y. Feng, F. Severgnini, N. Lu,” Effect of Zinc oxide and Al-Zinc oxide nanoparticles on the rheological properties of cement paste”, 2016, Composites Part B 105, pp 160-166.E.
  7. Nivethitha, S. Dharmar, “Effect of Zinc Oxide Nanoparticle on Strength of Cement Mortar”, 2016, International Journal of Science Technology & Engineering, Volume 3, Issue 05.
  8. Nazari, S. Riahi, S. Riahi, S. F. Shamekhi, A. Khademno, “Assessment of the effects of the cement paste composite in presence TiO2 nanoparticles”, 2010, Journal of American Science;6(4), nanoparticles in Concrete.
  9. Nazari, S. Riahi, S. Riahi, S. F. Shamekhi, A. Khademno, “Improvement the mechanical properties of the cementitious composite by using TiO2 nanoparticles”, 2010, Journal of American Science;6(4), Nazari, et al, nanoparticles in Concrete.
  10. Boshehrian and P. Hosseini, “Effect of nano-SiO2 particles on properties of cement mortar applicable for ferrocement elements”, 2011, CRL Letters Vol. 2(1).
  11. Soleymani, “Abrasion resistance of concrete containing SiO2 nanoparticles in different curing media”, 2012, Journal of American Science;8(8).
  12. Amini, G.D. Najafpour and M.A. Beygi, “Evaluation of Mechanical Strength of Epoxy Polymer Concrete with Silica Powder as Filler”, 2010, World Applied Sciences Journal 9 (2): 216-220, ISSN 1818-4952.
  13. K. Mohsen, T. H. Sepehri, M. Sepehri, “Influence of Nano-Silica Particles on Mechanical Properties and Permeability of Concrete”, 2010, Second international Conference on sustainable construction materials and technologies.
  14. Yang, “Strength and Shrinkage Property of Nano Silica Powder Concrete”, 2012, 2nd International Conference on Electronic & Mechanical Engineering and Information Technology.
  15. Nazari, S. Riahi, S. Riahi, S. F. Shamekhi and A. Khademno, “Influence of Al2O3 nanoparticles on the compressive strength and workability of blended concrete”, 2010, Journal of American Science;6(5), Nazari, et al, nanoparticles in Concrete.
  16. Nazari, S. Riahi, S. Riahi, S. F. Shamekhi, A. Khademno, “Mechanical properties of cement mortar with Al2O3 nanoparticles”, 2010, Journal of American Science;6(4).
  17. M. R. Arefi, M. R. Javeri, E. Mollaahmadi, “To study the effect of adding Al2O3 nanoparticles on the mechanical properties and microstructure of cement mortar”, 2011, Life Science Journal;8(4)

294-299

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53.

Authors:

Deepak Sharma, Atul Pandey

Paper Title:

Study of Mechanical Properties and Change in Microstructure of Alloy Steel EN24 under various Heat Treatment Process.

Abstract: The alloy steel EN24 as a base metal before heat treatment has limited applications because of its low values of tensile properties. In order to enhanced its mechanical properties, hardness, its machinability up to such an extent that it can be used as: gears, connecting rods, swivel arms, transmission parts, machine tool parts, dies, cylinders, cropping blades , aero planes & aerospace systems the alloy steel EN24 have to be heat treated with different processes. The above problems of alloy steel En24 is improved in this project by applying three heat treatment processes i.e. stress relieving, normalizing and hardened & tempered for each specimens and testing each specimens for their tensile properties, chemical composition, hardness & microstructures. The graphs and their comparative analysis are done in the end.

Keywords: Chemical composition, EN24, Hardness Testing, Heat Treatment, Mechanical Properties & Microstructure study.

References:

  1. ASM Metals Handbook, Volume 4, Heat Treating (1991) ASM International, Materials Park, Ohio.
  2. Camel Cather M, Bayram Ali, sala Baushi Material science and engineering vol 407, Oct 2005.
  3. Chang W. Microstructure and mechanical properties of 780 MPa high
  4. strength steels produced by direct-quenching and tempering process. J Mater Sci 2002;37: 1973-9.
  5. Y Lai, W. E Wood, R.A Clark, V.F Zackay & E.R Parker, Metallurgical transactions volume-5, july 1974-1663.
  6. Hafiz Mahmud Mat. Series and Engg,Vol 340. 15, Jan (2003).
  7. Heat Treatment: Principles and Techniques-By T.V Rajan, C.P Sharma, Ashok Sharma.
  8. Lee WS, Su TT. Mechanical properties and microstructural features of AISI 4340 high-strength alloy steel under quenched and tempered Mater Process Technol 1999;87:198-206. Meysami AH, Ghasemzadeh R, Seyedian SH, Aboutalebi MR. An investigation on the microstructure and mechanical properties of direct-quenched and tempered AISI 4140 steel. Mater Des 2009;31(3):1570-5.
  9. Nilson Fedrick Karl and Blagovea D.; An experimental and numerical analysis to correlate the variation in ductiltity and defects of microstructure in ductile cast iron components; Engineering Fracture Mechanics, Volume 73, Issue 9, June 2005, Page 1133-1147.
  10. Principles and application of heat treatment of CI, Isfahan University Iran, 1987.
  11. A Grange, C.R Hribal and L.F.Porter, Metallurgical transactions volume-8A, November 1977-1775.
  12. Ray et al A.K., Materials Science and Engineering A, 454-455 (2007):pp. 679–684.
  13. Saboury-Sichany, T.J Baker, D.R.F west Department of
  14. metallurgy and material science, Imperial college of science & technology , London , Material science letters 1 ( 1982) 316-317.
  15. Saikumar S, Shunmugam MS (2011) Development of a feed rate adaption control system for high-speed rough and finish end milling of hardened EN24 steel. Int J Adv Manufacturing Technology.
  16. Shankar Vani, Valsan M., Kannan R., Bhanu K. Rao Sankara, Mannan S.L.and Pathak S.D. International Symposium of Research Students on Materials Science and Engineering (2004).
  17. I Spivakov, I.G Uzlov, L.A Moiseeva, E.A Orlov & L.S Tikhonyuk; ‘ Effective process of heat treatment of thick- sheet rolled stock under the condition of a 3600 mill, ’ Stal no-7, 62-67 ( 1993 ).
  18. Zamba J, Sumandi M, Materials and Design Vol 25 august 2004.

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54.

Authors:

Chandrabhanu Malla, Manisha Maurya, Jatin Sadarang, Isham Panigrahi

Paper Title:

Deep Groove Ball Bearing Fault Diagnosis and Classification Using Wavelet Analysis and Artificial Neural Network

Abstract: Now a days deep groove ball bearings are widely used to support the load of the shaft and to reduce friction in industrial machinery and domestic appliances. The major issue that arises in deep groove ball bearings is catastrophic failure which arises due to fatigue loading, electrical erosion, corrosion or spalls on various bearing components. Thus to ensure steadiness and continuous running of the machine, condition monitoring and defect detection of deep groove ball bearings are very essential. This research paper emphasizes on fault detection of deep groove ball bearings having specific defects present on various bearing elements using Debauchies Wavelet (DB-02) up to fourth level of decomposition. The vibration signals were recorded from a customized ball bearing test rig. The accelerometer and FFT analyzer is used to collect time and frequency domain vibration data and signature. Finally Artificial Neural Network (ANN) based Pattern recognition classifier is used for automatic bearing fault detection. The training of the network is done based on the collected data and the testing is done based on random data set. The highest classification rate was found to be 94%. This paper represents the implementation of Artificial Neural Network as a functional artificial intelligence tool for automatic bearing fault detection and classification without any human involvement.

Keywords: Artificial Neural Network, Condition Monitoring, Deep groove ball bearing, Defects, Debauchies Wavelet, Vibration Signature.

References:

  1. B. Ali, N. Fnaiech, L. Saidi, B. Chebel-Morello, F. Fnaiech, “Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals,” Applied Acoustics, 89 (2015), pp. 16-27.
  2. K. Kankar, S. C. Sharma, S. P. Harsha, “Rolling element bearing fault diagnosis using wavelet transform,” Neurocomputing, 74 (2011), pp. 1638-1645.
  3. Hemmati, W. Orfali, M. S. Gadala, “Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation,” Applied Acoustics, 104 (2016), pp.101-118.
  4. K. Kankar, S. C. Sharma, S. P. Harsha, “Fault diagnosis of rolling element bearing using cyclic autocorrelation and wavelet transform,” Neurocomputing, 110 (2013), pp. 9-17.
  5. N. Babu, H. S. Himamshu, N. P. Kumar, “Journal Bearing Fault Detection Based on Daubechies Wavelet,” Archives of Acoustics, 42-3 (2017), pp. 401-414.
  6. S. Kumar, P. S. Pai, N. S. Sriram, G. S. Vijay, “ANN based evaluation of performance of wavelet transform for condition monitoring of rolling element bearing,” Proceedings of the International Conference On Design and Manufacturing, IConDM-2013 64 (2013), pp. 805-814.
  7. M. Nistane, S. P. Harsha, “Failure Evaluation of Ball Bearing for Prognostics,” Proceedings of the 3rd International Conference on Innovations in Automation and Mechatronics Engineering, ICIAME-2016 23 (2016), pp. 179-186.
  8. Mishra, A. K. Samantray, G. Chakraborty, “Rolling Element Bearing Fault Diagnosis under Slow Speed Operation using Wavelet De-noising,” Measurement (2017), doi: http://dx.doi.org/10.1016/j.measurement.2017.02.033.
  9. Wang, Z. He, Y. Zi, “Enhancement of Signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform,” Mechanical Systems and Signal Processing, 24 (2010), pp. 119-137.
  10. K. E. Nizwan, S. A. Ong, M. F. M. Yusof, M. Z. Baharom, “A wavelet decomposition analysis of vibration signal for bearing fault detection,” Proceedings of the 2nd International Conference on Mechanical Engineering Research, ICMER-2013, IOP Conf. Series: Material Science and Engineering, 50 (2013), pp. 1-9.
  11. V. N. Patel, N. Tandon, R. K. Pandey, “Defect detection in deep groove ball bearing in presence of external vibration using envelope analysis and Duffing oscillator,” Measurement, 45 (2012), pp. 960-970.

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55.

Authors:

K. Chandrasekaran

Paper Title:

Improved Sine Cosine Algorithm for Solving Dynamic Economic Dispatch Problem

Abstract: This paper presents an Improved Sine Cosine Algorithm (ISCA) for solving economic dispatch problem (EPD) and dynamic economic dispatch (DED) problem. The DED problem exhibits non-convex nature due to the inclusion of valve-point loading effects and ramp rate limits. In Sine Cosine algorithm (SCA), the searcher agents which interact with each other based on the trigonometric function. This paper proposes a novel ISCA that track the best solution to improve the convergence and solution quality of SCA. The results obtained by the ISCA are tabulated, graphed and compared with that obtained by the SCA and other algorithm available in the literature. The outcomes show that the implementation of ISCA provides a feasible solution with significant savings.

Keywords: Economic Dispatch Problem, Dynamic Economic Dispatch Problem, Sine Cosine algorithm and Improved Sine Cosine Algorithm.

References:

  1. Zwe-Lee Gaing, Particle Swarm Optimization to Solving the Economic Dispatch Considering the Generator Constraints, IEEE Transactions on Power Systems, 18, 1187- 1196(2003).
  2. K. Panigrahi, P.K. Chattopadhyay, R.N. Chakrabarti and M. Basu, Simulated annealing technique for dynamic economic dispatch, Electric Power Components and Systems, 34, pp. 577–586(2006).
  3. Balamurugan and S. Subramanian, Differential Evolution-based Dynamic Economic Dispatch of Generating Units with Valve-point Effects, Electric Power Components and Systems, 36, pp. 828-843(2006).
  4. K. Panigrahi, V. Ravikumar Pandi and S. Das, Adaptive particle swarm optimization approach for static and dynamic economic load dispatch, Energy Conversion and Management, 49, pp. 1407–1415(2008).
  5. RavikumarPandiand BijayaKetanPanigrahi, Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm,Expert Systems with Applications , 38, pp..8509–8514(2011).
  6. Vaisakh and P. Praveena, S. Rama Mohana Rao and KalaMeah, Solving dynamic economic dispatch problem with security constraints using bacterial foraging PSO-DE algorithm, Elect power and Energy system , 39, pp. 56-67(2012).
  7. Attaviriyanupap, H. Kita, E. Tanaka and J.Hasegawa, A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function, IEEE Transactions on Power Systems, 22, pp. 77(2002).
  8. A. Victoire and A.E. Jeyakumar, A modified hybrid EP-SQP approach for dynamic dispatch with valve- point effect, Int. J. Electr. Power Energy System, 27, pp.594–601(2005).
  9. A. Victoire and A.E. Jeyakumar, Deterministically guided PSO for dynamic dispatch considering valve-point effect, Electr. Power Syst. Res., 73, pp.313–322(2005).
  10. Ying Wang, Jianzhong Zhou, Youlin Lu, Hui Qin and Yongqiang Wang, Chaotic self-adaptive particle swarm optimization algorithm for dynamic economic dispatch problem with valve-point effect, Expert Systems with Applications , 38, 14231–14237(2011).
  11. Hemamaliniand S.P Simon. Dynamic economic dispatch using artificial immune system for units with valve-point effect, Int. J. Electr. Power Energy System, 33, pp. 868–874(2011).
  12. Immanuel Selvakumar, Enhanced cross-entropy method for dynamic economic dispatch with valve-point effects, Int. J. Electr. Power Energy System33 , pp. 783–790(2011).
  13. Hemamalini and S.P Simon, Dynamic economic dispatch using artificial bee colony algorithm for units with valve-point effect, Eur. Trans. Electr. Power , 21, pp.70–81(2011).
  14. Mohammadi-ivatloo, A.Rabiee and M. Ehsan, Time-varying acceleration coefficients IPSO for solving dynamic economic dispatch with non-smooth cost function, Energy Convers. Manage.,56 ,pp. 175–183(2012).
  15. Niknam, R. Azizipanah-Abarghooee and A. Roosta, Reserve constrained dynamic economic dispatch: A new fast self-adaptive modified firefly algorithm, IEEE Syst. J , 6, pp.635–646 (2012).
  16. Niknamand F.Golestaneh, Enhanced bee swarm optimization algorithm for dynamic economic dispatch, IEEE Syst . J ,7, pp.754–762 (2013).
  17. Niknam T, R. Azizipanah-Abarghooee R and J. Aghaei, A new modified teaching-learning algorithm for reserve constrained dynamic economic dispatch, IEEE Transactions on Power Systems, 28 (2013) 749–763.
  18. Q Wang, H.B. Gooi, S.X.Chen and S. Lu, A mixed integer quadratic programming for dynamic economic dispatch with valve point effect, IEEE Transactions on Power Systems,99, pp.1–10(2014).
  19. SeyedaliMirjalili, “SCA: A Sine Cosine Algorithm for Solving Optimization Problems”, Knowledge-Based Systems (2016), DOI: 10.1016/j.knosys.2015.12.022.
  20. F. Zaman, M. SaberElsayed, Tapabrata Ray and Ruhul ASarker, Evolutionary Algorithms for Dynamic Economic Dispatch Problems, IEEE Transactions on Power Systems 31 (2016) 1486-1495.
  21. Modiri-Delshad and N.A Rahim, Multi-objective backtracking search algorithm for economic emission dispatch problem, Appl Soft Comput., 40 , pp. 479–94(2016).
  22. K Roy and S. Bhui, Multi-objective quasi-oppositional teaching learning based optimization for economic emission load dispatch problem, Int J Electr Power Energy Syst., 53, pp. 937–48(2013).
  23. Basu, Economic environmental dispatch using multi-objective differential evolution, Appl Soft Comput., 11, pp.2845–53(2011).
  24. DexuanZoua, Steven Lib, ZongyanLicand XiangyongKonga, A new global particle swarm optimization for the economic emission dispatch with or without transmission losses, Energy Conversion and Management, 139 , pp. 45–70 (2017).

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56.

Authors:

Nitin Kukreja, Prakhar Vatsa

Paper Title:

Advanced Energy Storage Technique and its Conversion

Abstract: Energy storage is an important aspect of the conservation of energy such as the energy releases from solar radiations using different energy storage in an efficacious way of renewable energy. Hydrogen is considered as a precious model for energy storage and energy carrier for the future scope and it can acquire by low-temperature water electrolysis, high-temperature water electrolysis, and carbon assisted hydrogen production. Electrochemical energy storage is a favourite zone of the modern era because of its adorable properties such as site versatility, static structure and modularity. Redox flow battery and lithium-ion batteries are most popular in energy storage because they have the most appreciable feature as the depth of discharge, which decides the lifespan of the batteries and signifies the maximum number of the cycle for which the capacity of batteries does not undergo the nominal capacity. Lithium-ion batteries enrolled all the aspects such as size range, capital cost, efficiency and found that the Redox flow battery and lithium-ion batteries have major potential to store energy for an off-grid renewable source.

Keywords: Redox flow batteries (RFB), Hydrogen Cell, depth of discharge (DOD).

References:

  1. Rahman, S. Rehman, M.A. Abdul-Majeed. Overview of energy storage systems for storing electricity from renewable energy sources in Saudi Arabia. Renewable and Sustainable Energy Reviews. 16 (2012) 274-83.
  2. Janoschka, A. Teichler, B. Häupler, T. Jähnert, M.D. Hager, U.S. Schubert. Reactive inkjet printing of cathodes for organic radical batteries. Advanced Energy Materials. 3 (2013) 1025-8.
  3. Alotto, M. Guarnieri, F. Moro. Redox flow batteries for the storage of renewable energy: A review. Renewable and Sustainable Energy Reviews. 29 (2014) 325-35.
  4. Á. Cunha, J. Martins, N. Rodrigues, F. Brito. Vanadium redox flow batteries: a technology review. International Journal of Energy Research. 39 (2015) 889-918.
  5. Wang, Q. Luo, B. Li, X. Wei, L. Li, Z. Yang. Recent progress in redox flow battery research and development. Advanced Functional Materials. 23 (2013) 970-86.
  6. J. Kim, M.-S. Park, Y.-J. Kim, J.H. Kim, S.X. Dou, M. Skyllas-Kazacos. A technology review of electrodes and reaction mechanisms in vanadium redox flow batteries. Journal of Materials Chemistry A. 3 (2015) 16913-33.
  7. Wang. Hydrogen production from a chemical cycle of H2S splitting. International Journal of Hydrogen Energy. 32 (2007) 3907-14.
  8. L. Norgaard. Integration and control of a battery balancing system. NAVAL POSTGRADUATE SCHOOL MONTEREY CA2013.
  9. Larcher, J.-M. Tarascon. Towards greener and more sustainable batteries for electrical energy storage. Nature chemistry. 7 (2015) 19.
  10. Diouf, R. Pode. Potential of lithium-ion batteries in renewable energy. Renewable Energy. 76 (2015) 375-80.
  11. Lu, X. Han, J. Li, J. Hua, M. Ouyang. A review of the key issues for lithium-ion battery management in electric vehicles. Journal of power sources. 226 (2013) 272-88.
  12. Raccichini, A. Varzi, S. Passerini, B. Scrosati. The role of graphene for electrochemical energy storage. Nature materials. 14 (2015) 271.
  13. Parasuraman, T.M. Lim, C. Menictas, M. Skyllas-Kazacos. Review of material research and development for vanadium redox flow battery applications. Electrochimica Acta. 101 (2013) 27-40.
  14. Janoschka, N. Martin, U. Martin, C. Friebe, S. Morgenstern, H. Hiller, et al. An aqueous, polymer-based redox-flow battery using non-corrosive, safe, and low-cost materials. Nature. 527 (2015) 78.
  15. Turker, S.A. Klein, E.-M. Hammer, B. Lenz, L. Komsiyska. Modeling a vanadium redox flow battery system for large scale applications. Energy Conversion and Management. 66 (2013) 26-32.

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57.

Authors:

LellaKranthi Kumar, ParvataneniGeethika, Sriram Konduru,

Paper Title:

Implementation of KSM Model to Reduce Software Risks

Abstract: This paper deals with the risk management with the help of a certain case study and we propose a KSM model approach for our paper to be implemented for better results and lessen the risk of the system as much as possible. This also helps us in the estimating of the cost effective measures and less uncertain situations in the scenario. This is quite helpful for the risk calculation and estimation along with the avoiding of the risks factored to occur in the process.

Keywords: Risk management, KSM model approach, estimations, risk analysis, risk calculation.

References:

  1. Flanders, W., On the relationship of sufficient component cause models with potential outcome (counterfactual) models. European journal of Epidemiology, 2006, 21(12): p. 847-853.
  2. Fenz, S., Pruchner, T. and Manutscheri, A. (2009) Ontological Mapping of Information Security Best-Practice Guidelines. BIS 2009, LNBIP 21, pp. 49-60.
  3. FIPS PUB 65, National Bureau of Standards (1997). Guidelines of Automatic Data Processing Risk Analysis. USA: Washington D.C., General Printing Office.
  4. Fulford, H. and Doherty, N. (2003) The application of information security policies in large UK-based organizations: an exploratory investigation. Information Management & Computer Security, 11(3), pp. 106-114.
  5. Fung, A. R., Farn, K. and Lin, A. (2003) A study on the certification of the information security management systems. Computer Standards & Interfaces, 25(5), pp. 447-461.
  6. Summit in Okinawa, Japan (July 2000) [online]. Available from: http://www.g7.utoronto.ca/summit/2000okinawa/gis.htm [Accessed: 25 May 2007].
  7. Devanbu, P.; Fong, P.W.-L.;Stubblebine, S.G. (1998) Techniques for trusted software engineering. Proceedings of the 1998 International Conference on Software Engineering, pp. 126–135
  8. Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, 77 (2), 257-286.
  9. Chittister, C. and Haimes, Y.Y., Assessment and Management of Software Technical Risk, IEEE Transaction on Systems, Man, and Cybernetics, vol. 24, no. 2, Feb., 1994.
  10. Greer, D., Report on SERUM trial at NEC Corp., University of Ulster, 1998.
  11. Greer, D. and Bustard, D.W., SERUM-Software Engineering Risk: Understanding and Management, The International Journal of Project & Business Risk, vol. 1, Issue 4, winter, pp. 373-388, Project Manager Today Publications, 1997(2).

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58.

Authors:

Panga Narasimha Reddy, Javed Ahmed Naqash

Paper Title:

Strength Prediction of High Early Strength Concrete by Artificial Intelligence

Abstract: The evaluation of the combined effect of alccofine, chemical admixture and curing age to compressive strength prediction of High early strength concrete (HESC) in view of its increasing application in construction industries, is a novelty. Concrete is generally a mixture of different materials and it is a difficult task to predict the strength of HESC. However, it seems that a soft computing could save time and money. In this study, fuzzy logic (FL) and artificial neural network (ANN) models were developed to predict the strength of High Early Strength Concrete. This research paper presents the effect on strength of the concrete with alccofine (i.e. 25%) as a constant replacement of cement for all concrete mixes and several non-chloride hardening accelerator ratios (0-1.8) for different water to binder contents (i.e. 0.38, 0.4 and 0.45). The compressive strength was evaluated at 3, 7 and 28 days resulting in a total of 36 data sets that were used in FL and ANN. The results of the measured compressive strength were compared to values predicted from FL and ANNs. The results showed that ANN can be used successfully to strength prediction of high early strength concrete wherein the ANN model performed better than the FL model. The extrapolation capacity of FL and ANN was satisfactory.

Keywords: high early strength concrete, artificial neural network, fuzzy logic, compressive strength, non-chloride hardening accelerator, prediction

References:

  1. Meagher, T., Shanahan, N., Buidens, D., Riding, K. A., and Zayed, A. (2015). “Effects of chloride and chloride-free accelerators combined with typical admixtures on the early-age cracking risk of concrete repair slabs.” Construction and Building Materials, 94, 270–279.
  2. Aïtcin, P. C., and Flatt, R. J. (2015). Science and technology of concrete admixtures. Science and Technology of Concrete Admixtures.
  3. Akkurt, S., Tayfur, G., and Can, S. (2004). “Fuzzy logic model for the prediction of cement compressive strength.” Cement and Concrete Research, 34(8), 1429–1433.
  4. Gupta, S. (2013). “Using Artificial Neural Network to Predict the Compressive Strength of Concrete containing Nano-silica.” Civil Engineering and Architecture, 1(3), 96–102.
  5. Standard, IS: 8112-1989. 43 Grade Ordinary Portland Ceemnt- Specification, (1989).
  6. For, S., From, F. A., Sources, N., and Concrete, F. O. R. (1970). “BIS: 383-1970, Specifications for Coarse and Fine Aggregates from Natural Sources for Concrete, Bureau of Indian Standards, New Delhi, India.”
  7. Ghanbari, M., Fardani, M. H., and Ghanbari, M. (2018). “Evaluating the potential of fuzzy logic to predict compressive strength of lightweight concrete, SCC, and fly ash.” Indian Concrete Journal, 92(1), 82–90.
  8. Nataraja, M. C., Jayaram, M. a, and Ravikumar, C. N. (2006). “Prediction of Early Strength of Concrete : A Fuzzy Inference System Model.” International Journal of Physical Sciences, 1(2), 47–56.
  9. Mehdi Neshat. (2012). “Comparative study on fuzzy inference systems for prediction of concrete compressive strength.” International Journal of the Physical Sciences, 7(3), 440–455.
  10. Neshat, M., and Adeli, A. (2011). “Designing a fuzzy expert system to predict the concrete mix design.” 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings, 1–6.
  11. James, B. J., Mcneilly, T., and Oren, P. (1979). “Predicting the Compressive Strength of Brickwork.” (2007), 10.
  12. Chen, H., Qian, C., Liang, C., and Kang, W. (2018). “An approach for predicting the compressive strength of cement-based materials exposed to sulfate attack.” PLoS ONE, 13(1), 1–11.
  13. Alquzweeni, S. S. (2015). “Artificial Neural Network Model for Predicting Compressive Strength of High Strength Concrete after Burning.” International Journal of Civil & Environmental Engineering IJCEE-IJENS, (December).
  14. Muthupriya, P., Subramanian, K., and Vishnuram, B. G. (2011). “Prediction of Compressive Strength and Durability of High Performance Concrete By Artificial Neural Networks.” INTERNATIONAL JOURNAL OF OPTIMIZATION IN CIVIL ENGINEERING Int. J. Optim. Civil Eng, 1, 189–209.
  15. Kalra, G. (2016). “Research Review and Modeling of Concrete Compressive Strength Using Artificial Neural Networks.” IJISET -International Journal of Innovative Science, Engineering & Technology, 3(2), 672–677.
  16. Neeraja, D., and Swaroop, G. (2017). “Prediction of compressive strength of concrete using artificial neural networks.” Research Journal of Pharmacy and Technology, 10(1), 35–40.

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59.

Authors:

M.GOPI SRIDHAR, K V SATYANARAYANA

Paper Title:

Interference suppression in wireless communications by adaptive beam forming algorithm and windows

Abstract: Smart antennas plays major role for improving the capacity and range, conventional methods like LMS (least mean square), Normalized LMS (NLMS) are not suitable for reducing the inferring signals, and the basic drawback of NLMS is variable step size at each iterations because of stable step size. In VSSNLMS algorithm method step size is variable and MSE should be reduced up to the desired level but it takes more processing time for the updation of tap weights. So in order to reduce the above short comings, For LMS algorithm,a new technique is developed for wireless systems. For steering the antenna beam electronically LMS algorithm is used .Sample-by- Sample adaptive implementation and Block-data of the MBER solution is done by using the Rectangular, Hamming, Kaiser, Chebyshev windows. Antenna Half power beam width is to the extent of using Matlab simulation by making use of window techniques. The system gain will increase the CDMA performance of the system. They offer a wide range of ways to increase performance of the wireless system.

Keywords: CDMA, (NLMS), VSSNLMS, NLMS, Rectangular, Hamming, LMS

References:

  1. etc.al ‘low profile smartantennas achieving highergain,’IEEE Trans. on AWP vol. 61, no. 1, pp. 162-168, Jan, 2013.
  2. TSRappaport, WirelessCommunications: PrinciplesandPractice, NJ:Prentice-Hall,1996.
  3. JHWinters, "Smart antennaand applicationto wireless ad-hocnetworks," on WirelessCommunications,vol. 13, no. 4, pp. 77-83, 2006.
  4. MRYerena, "spatial signalprocessing for Adaptive widebandbeamforming insmart antennas," in 15th International Symposium on Antenna Technology and Applied Electromagnetics, Toulouse, France, June, 2012.
  5. AHakam, etc., " MVDR and MUSIC Algorithm for Enhanced DOAEstimation " in IEEE InternationalConference on Current Trends in InformationTechnology (CTIT'2013), Dubai, UAE,2013.
  6. HTakekawa, etc., " NLMS algorithmbased on An efficient andeffective variablestep size," in 42nd Asilomar Conferenceon Signals, SystemsandComputers, October, 2008.
  7. Sal "CLMS filtering with noisy data matrix," IEEE Trans. SignalProcess, vol. 53, no. 6, p. 2112–2123, June,2005.
  8. SHaykin, AdaptiveFilterTheory, Prentice-Hall,2002.
  9. TArnantapunpong, etc.al “Normalized NLMSAlgorithm based VariableStep Size for," in Int. Workshop onNonlinear CircuitsCommunications and SignalProcessing, Hawaii, USA, March, 2010.

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60.

Authors:

G.Aloy Anuja Mary, Sheeba Santhosh

Paper Title:

An optimal Transmitted Power reduction based Rate Improvement Technique in cognitive radio networks

Abstract: This research analyzes an instantaneous transmission rate strategies for secondary users in cognitive radio networks by analyzing their effective capacity performance with HOERA and transmission block sizes. Describing a network model with secondary transmitter and secondary receiver with the potential presence of primary users, we present an interference power constraint that limits the transmission power of secondary users not only when a channel is sensed as busy but also when a channel is sensed as idle. The proposal model provides the tradeoff between reduced power with improved rate constraints. Further the proposed optimization scheme also reduces the complexity of the secondary networks.

Keywords: HOERA ,Primary user, interference

References:

  1. HZhang, CJiang, NBeaulieu, XChu, XWen and M Tao, "Asset Allocationin Spectrum-Sharing OFDMAFemtocells With HeterogeneousServices", IEEE Transactionson Communications, vol. 62, no. 7, pp. 2366-2377, 2014.
  2. HZhang, CJiang, R. Hu and YQian, "Selfassociation in catastrophe versatile heterogeneous littlecell systems", IEEENetwork, vol. 30, no. 2, pp. 116-121, 2016.
  3. HZhang, XChu, WGuo and SWang, "Conjunction of WiFi andheterogeneous little cell systems sharing unlicensed range", IEEE Commun. Mag.,vol. 53, no. 3, pp. 158-164,2015.
  4. Dastangoo, CFossa, YGwon and HKung, "Contending Cognitive Resilient Networks", IEEE Trans Cogn. CommunNetw., vol. 2, no. 1, pp. 95-109, 2016.
  5. 1000x: MoreSpectrum-Especially for SmallCells," in Presentation by QUALCOMM Inc., 2013[Online].
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  7. VChandrasekhar, JAndrews, TMuharemovic, Zukang Shen and AGatherer, "Power control in twolevel femtocell systems", IEEE Transactionson WirelessCommunications, vol. 8, no. 8, pp. 4316-4328, 2009.
  8. XKang, RZhang and MMotani, "Cost BasedResource Allocation forSpectrum-Sharing Femtocell Networks: A Stackelberg Game Approach", IEEE J. Select. Zones Commun., vol. 30, no. 3, pp. 538-549, 2012.
  9. JYun and KShin, "Versatile Interference Managementof OFDMAFemtocells for Co-Channel Deployment", IEEE J. Select. TerritoriesCommun., vol. 29, no. 6, pp. 1225-1241, 2011.
  10. Guruacharya, DNiyato, DKim and EHossain, "Progressive Competition forDownlink Power Allocation inOFDMA FemtocellNetworks", IEEE Transactionson Wireless Communications, vol. 12, no. 4, pp. 1543-1553, 2013.
  11. Huang and V. Krishnamurthy, "Subjective Base Stations in LTE/3GPP Femtocells: A Correlated Equilibrium Game-Theoretic Approach", IEEE Transactions on Communications, vol. 59, no. 12, pp. 3485-3493, 2011.
  12. YChen, JZhang and QZhang, "Utility-Aware RefundingFramework for HybridAccess FemtocellNetwork", IEEE Transactions on Wireless Communications, vol. 11, no. 5, pp. 1688-1697, 2012.
  13. VHa and LLe, "Disseminated BaseStation Association and Power Control forHeterogeneous CellularNetworks", IEEETrans. Veh. Technol., vol. 63, no. 1, pp. 282-296, 2014.
  14. YanqingLiu and LiangDong, "Range Sharing inMIMO Cognitive RadioNetworks Based on CooperativeGame Theory", IEEETransactions onWireless Communications, vol. 13, no. 9, pp. 4807-4820, 2014.
  15. JChen and ASwindlehurst, "ApplyingBargaining Solutions toResource Allocationin Multiuser MIMO-OFDMA BroadcastSystems", IEEE J. Sel. Top. Flag Process., vol. 6, no. 2, pp. 127-139, 2012.
  16. QNi and CZarakovitis, "Nash Bargaining GameTheoretic Scheduling for JointChannel and Power Allocation in Cognitive Radio Systems", IEEE J. Select. Territories Commun., vol. 30, no. 1, pp. 70-81, 2012.
  17. NPrasad, KaiLi and XiaodongWang, "Reasonable RateAllocation in MultiuserOFDM-SDMANetworks", IEEE Transactions on Signal Processing, vol. 57, no. 7, pp. 2797-2808, 2009.
  18. GKramer, IMarić and RYates, "Helpful Communications", FNT in Networking, vol. 1, no. 3-4, pp. 271-425, 2006.
  19. LDing, TMelodia, SBatalama and JMatyjas, "Appropriated asset assignment insubjective andhelpful impromptu systems through joint steering, transfer choice and range distribution", Computer Networks, vol. 83, pp. 315-331, 2015.
  20. ZHANG, W. XU, S. LI and J. LIN, "Asset assignment forthe group basedagreeable multicastin OFDMbased intellectual radio frameworks", The Journal of China Universities of Posts and Telecommunications, vol. 20, no. 4, pp. 1-7, 2013.
  21. ATajer, NPrasad and XWang, "Beamforming and RateAllocation in MISO CognitiveRadio Networks", IEEE Transactions on Signal Processing, vol. 58, no. 1, pp. 362-377, 2010.
  22. FKhan, CMasouros and TRatnarajah, "Impedance DrivenLinear Precoding inMultiuser MISO Downlink Cognitive RadioNetwork", IEEE Transactions on Vehicular Technology, vol. 61, no. 6, pp. 2531-2543, 2012.
  23. ZFei, JNi, DZhao, CXing, NWang and JKuang, "Ergodic mysteryrate of two-client MISO impedancestations with measurable CSI", Science China Information Sciences, vol. 57, no. 10, pp. 1-14, 2014.
  24. CWang and HWang, "On the SecrecyThroughput Maximization for MISO CognitiveRadio Network in Slow FadingChannels", IEEE Transactions on Information Forensics and Security, vol. 9, no. 11, pp. 1814-1827, 2014.
  25. Xiaoming ChenZhaoyang Zhang and Chau Yuen, "VersatileMode Selection inMultiuser MISO CognitiveNetworks With Limited Cooperation andFeedback", IEEE Transactions on Vehicular Technology, vol. 63, no. 4, pp. 1622-1632, 2014.
  26. DNg, MShaqfeh, RSchober and HAlnuweiri, "Strong LayeredTransmission in Secure MISO MultiuserUnicast CognitiveRadio Systems", IEEE Transactions on Vehicular Technology, vol. 65, no. 10, pp. 8267-8282, 2016.
  27. SumRate Maximization for CognitiveMISO Broadcast Channels: Beamforming Design and LargeSystems Analysis", IEEETransactions on WirelessCommunications, vol. 13, no. 5, pp. 2383-2401, 2014.
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  29. Zhou, F. Liu, Y. Yin, Q. Li and J. Qin, "StrongBeamforming for SimultaneousWireless Information and PowerTransfer in MISOInterference Channels", WirelessPersonal Communications, 2016.
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  31. Murali,P.Dhana Lakshmi “Spectrum sensing based on forward activechannel allocation in wireless 5Gcommunications  ”  published in IJMTST  Volume 2 | Issue 2 | February  2016 | ISSN 2455-3778   
  32. B. Awoyemi, B. Maharaj and A. Alfa, "Ideal asset portion answers forheterogeneous psychological radio systems", DigitalCommunications andNetworks, vol. 3, no. 2, pp. 129-139, 2017.

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61.

Authors:

Dhana Lakshmi Potteti , Venkateswara Rao N

Paper Title:

Performance Comparison of Eigenvalue based Blind Spectrum Sensing Algorithms

Abstract: In the past few years opportunistic spectral access schemes has been proved as a prominent solution for the prevailing problem of spectrum scarcity. These schemes employ spectral sensing approaches to detect the presence or absence of a primary user and to subsequently allow the secondary user to transmit the data. With the evolution of Multi I/p Multi O/p (MIMO) and massive MIMO systems that picked up momentum from 3G and 4G respectively, sensing with multiple antenna systems has been popularized. In this paper, blind spectrum sensing for multiple antenna systems using eigenvalue based approaches has been compared for Rayleigh and Nakagami fading channel environments. Particularly, Covariance Absolute VAule (CAV), Akaike Information Criterion (AIC) and Minimum Description Length (MDL), Weighted Covariance Detection (WCD) and Energy Detection (ED) based sensing schemes have been compared for their detection performance as a function of Signal to Noise Ratio (SNR). The simulation results showed that AIC and MDL based sensing approaches outperform the others compared in both Rayleigh and Nakagami fading channels.

Keywords: spectrum sensing, detection probability, opportunistic spectrum access, secondary user, multiple antenna sensing

References:

  1. Malik, Shahzad A., Shah, M. A.,etc.al "Comparative analysis of primarytransmitter detectionbased spectrumsensing techniques in cognitive radiosystems." Australian journal of basic and applied sciences 4 (9) (2010) 4522-4531.
  2. etc.al "Signalprocessing in cognitiveradio." Proceedings of theIEEE 97 (5) (2009) 805-823.
  3. Dandawate,Amod V., and Georgios B. Giannakis. "Statisticaltests for presenceof cyclostationarity." IEEE Transactions onSignal processing 42 (9) (1994) 2355-2369.
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  5. Zeng,Yonghong, and Ying-ChangLiang. "Spectrumsensing algorithms for cognitiveradio based onstatistical covariances." IEEEtransactions on Vehicular Techlogy 58 (4)(2009) 1804-1815.
  6. Wang,Rui, and MeixiaTao. "Blind spectrumsensing by informationtheoretic criteria." Global TelecommunicationsConference (GLOBECOM 2010), IEEE (2010) 1-5.
  7. Jin, Ming, et al. "Spectrumsensing using weightedcovariance matrix in Rayleighfading channels." IEEE Transactions on Vehicular Technology 64 (11) (2015) 5137-5148.

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62.

Authors:

Ranjan Kumar, S A Edalatpanah, Sripati Jha, Ramayan Singh

Paper Title:

A Novel Approach to Solve Gaussian Valued Neutrosophic Shortest Path Problems

Abstract: We have exhibited a novel method for finding the neutrosophic shortest path problem (NSSPP) consid- ering Gaussian valued Neutrosophic number. We have used linear programming approach for finding the NSS- PP for Gaussian valued Neutrosophic number which is as per best of our information, hasn’t been used till date for any other research work. In this article, we have introdu- ced a novel method which deals Gaussian shaped neutro- sophic problem easily without using any ranking method. We have used this method to solve uncertain network pro-blems to find the shortest path which can help in taking crucial uncertain decisions. Finally, some numerical con- siderations are provided to show the effectiveness of the proposed model.

Keywords: Neutrosophic fuzzy numbers; shortest path pro- blem; gaussian valued neutrosophic number; network; programming method.

References:

  1. Kumar, S. Jha, and R. Singh, "A different appraoch for solving the shortest path problem under mixed fuzzy environment," International journal of fuzzy system applications, vol. 9, issue 2 p. Article 6, Forthcoming.
  2. Abdel-Basset, M. Gunasekaran, M. Mohamed, and F. Sma-randache, "A novel method for solving the fully neutrosophic linear programming problems," Neural Computing and Applications, Mar. 2018.
  3. Mohamed, M. Abdel-Basset, A. N. Zaied, and F. Smaran- dache, "Neutrosophic integer programming problem," Neutrosophic Sets and Systems, vol. 15, pp. 3-7, 2017.
  4. Karaaslan, "Gaussian single-valued neutrosophic numbers and its application in multi-attribute decision making," Neutrosophic Sets and Systems, vol. 22, pp. 101-117, 2018.
  5. Şahin and P. Liu, "Maximizing deviation method for neutrosophic multiple attribute decision making with incomplete weight information," Neural Computing and Applications, vol. 27, pp. 2017-2029, Oct. 2016.
  6. Broumi, A. Bakali, M. Talea, F. Smarandache, and M. Ali,., "Shortest path problem under interval valued neutrosophic setting," Journal of Fundamental and Applied Sciences, vol. 10, pp. 168-174, 2018.
  7. Broumi, A. Bakali, M. Talea, F. Smarandache, and M. Ali, "Shortest path problem under bipolar neutrosphic setting," in Advanced Research in Area of Materials, Aerospace, Robotics and Modern Manufacturing Systems, vol. 859, Feb. 2017, pp. 59-66.
  8. Kumar, S. A. Edalatpanah, S. Jha, S. Broumi, and A. Dey, "Neutrosophic shortest path problem," Neutrosophic Sets and Systems, vol. 23, pp. 5-15, 2018.
  9. Broumi, A. Bakali, M. Talea, F. Smarandache, and L. Vladareanu, "Computation of shortest path problem in a network with SV-trapezoidal neutrosophic numbers," in 2016 International Conference on Advanced Mechatronic Systems (ICAMechS), 2016, pp. 417-422.
  10. Broumi, A. Bakali, M. Talea, F. Smarandache, and L. Vladareanu, "Computation of Shortest Path Problem in a Network with SV-Triangular Neutrosophic Numbers," in IEEE International Conference on Innovations in Intelligent Systems and Applications (INISTA), Gdynia, Poland, 2017, pp. 426-431.
  11. Wang, F. Smarandache, Y-Q Zhang, and R. Sunderraman, Interval Neutrosophic Sets and Logic:Theory and Applications in Computing.: Infinite Study, 2005.
  12. Wang, F. Smarandache, Y-Q. Zhang, and R. Sunderraman, "Single valued neutrosophic sets," Multispace and Multistructure, 2005.
  13. Biswas, S. Pramanik, and B. C. Giri., "Aggregation of triangular fuzzy neutrosophic set information and its application to multi-attribute decision making.," Neutrosophic Sets and Systems., vol. 12, pp. 20-40, 2016.
  14. Pramanik, and B. C. Giri, "TOPSIS Strategy for Multi-Attribute Decision Making with Trapezoidal Neutrosophic Numbers," Neutrosophic Sets and Systems, vol. 19, pp. 29-39, 2018.
  15. Biswas, S.
  16. Ye, "An extended TOPSIS method for multiple attribute group decision making based on single valued neutrosophic linguistic numbers," Journal of Intelligent & Fuzzy Systems, vol. 28, pp. 247-255, 2015.
  17. Liu, "The aggregation operators based on archimedean t-conorm and t-norm for single-valued neutrosophic numbers and their application to decision making," International Journal of Fuzzy Systems, vol. 18, pp. 849-863, 2016.
  18. Ye, "Similarity measures between interval neutrosophic sets and their applications in multicriteria decision-making," Journal of Intelligent & Fuzzy Systems, vol. 26, pp. 165-172, 2014.
  19. Uluçay, I. Deli, and M. Şahin, "Similarity measures of bipolar neutrosophic sets and their application to multiple criteria decision making," Neural Computing and Applications, vol. 29, pp. 739-748, 2018.
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  25. Ye, "Hesitant interval neutrosophic linguistic set and its application in multiple attribute decision making," International Journal of Machine Learning and Cybernetics, pp. 1-12, Nov. 2017.
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63.

Authors:

S. Sherine, S. Prakash, A. Navaneethamoorthy

Paper Title:

Investigation on Solar Panels with and Without Shading Effects in Series and Parallel Connections

Abstract: Solar power is the change of vitality from daylight into power, either directly utilizing photovoltaic (PV), or in a roundabout way utilizing concentrated solar power. Since solar panels are the major alternate source of energy a survey has been taken to find out the impacts created by even small disturbances. Based on the survey results, it has been found that the performance of the solar panel is mostly affected due to the effects of shading caused by the trees and nearby buildings. Due to the shading in solar module hot spots are developed which may cause permanent damage to the cells that are shaded, hence affecting the performance of solar PV cells to a greater extent. With the help of available prototype model, an investigation has been done to find out the impacts created by shading. Results shows that the potential of solar cells is more without shading pattern and on further analysis a very small impact created by shading even partially on single solar cell totally affects the performance of solar PV panels. The power of the solar panel decreased when the load is increased. Thus, this paper describes the effect of shading on solar module with load conditions and also the shading effect was observed in both series and parallel connections. The output power of the series and parallel connection was compared.

Keywords: Solar Panels, PV Modules, Shading Effects, Non-Renewable Energy Sources

References:

  1. Suryakumari and G. Sahiti, 2013, Analysis and Simulation of Modified Adaptive Perturb and Observe MPPT Technique for PV Systems. International Journal of Emerging Trends in Electrical and Electronics, 9(1), 1-7.
  2. Rabiul Islam, Youguang Guo, Jian Guo Zhu, M.G Rabbani, 2010, Simulation of PV Array Characteristics and Fabrication of Microcontroller Based MPPT, Proc., 6th International Conference on Electrical and Computer Engineering ICECE, Dhaka, Bangladesh, 18-20.
  3. Nielsen, R. (2005) Solar Radiation, [Online]. Available:                http://home. iprimus.com.au/nielsens.
  4. Michael Boxwell, 2012, Solar Electricity Handbook, Greenstream Publishing, U.K.
  5. Olivia Mah, 1998, Fundamentals of Photovoltaic Materials, National Solar Power Research Institute, Inc.
  6. Sachin Jain, Vivek Agarwal, 2007, New current control based MPPT technique for single stage grid connected PV systems, Science Direct Energy Conversion and Management, 48 (2), 625–644.
  7. S. Rohella, 2013, Harnessing Electrical Energy through Solar CellConcentrators, Akshay Urja-Renewable Energy, 7(1), 29-32.
  1. Solametric Application Note PVA-600- 1, 2011, Guide to Interpreting I-V Curve Measurements of PV arrays.
  2. Sathyanarayana P., Rajkiran Ballal I., Girish Kumar, Laksmisagar P.S., 2014, Effect of light concentration by flat mirror reflectors on the electrical power output of the photovoltaic panel Carbon – Sci. Tech. 6 (1), 342 – 348.
  3. Prakash&V.Jayalakshmi ,“ Hybrid Solar-Wind Energy System With MPPT Using Cuk-Sepic Fused Converter”, International Journal of Pure and Applied Mathematics, Volume 119 No. 12 2018, 6851-6859.
  4. S.Prakash & S.Sherine,”Power Smoothening Modelling For Grid Connected Fed Direct-Driven Pmsg Wind Turbines”, International Journal of Pure and Applied Mathematics, Volume 116 No. 18 2017, 1314-3395.

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64.

Authors:

Deepanshi Bansal, Pooja Gupta

Paper Title:

Multiple Speaker Recognition

Abstract: Multiple Speaker Recognition is a powerful tool determining the number of speakers in a random speech along with determination of time span and segregation of voice signal of each speaker according to his/her MFCC and delta MFCC , chroma factors etc. which are different for different people because of variation in frequency of vocal chord which in turn effect the places of stress and syllables used.The methodology used in the research paper enable user to extract and isolate the individual voice streams at the receiver end. It can be used in various situations such as office meetings, the country parliament sessions to identify the speaker and his/her content to draw conclusions. The system may help the user to highlight a particular speaker’s voice amongst other speakers. Even if the number of speakers is not known, the algorithm used in the paper will be able to determine the number of speakers based on the concept of clustering and then extracting common features of voice , it will be able to distinguish the speakers and separate their voice with each other thus giving the output as the duration of the discussion done by each speaker along with separated voice saved in wav format. If the voice sample of the speaker is stored in the speaker recognition model then, the model will also be able to show the name of the speaker else the representation of different speakers will be in the form speaker 1 , speaker 2 and so on. The methodology discussed in the paper could be used in present day interview process during group discussions and in admission process for higher studies and this could ease the work load on the recruiters giving them a idea about contribution of different speakers in the discussion.

Keywords: clustering, diarization, voice activity detection

References:

  1. Speaker Recognition for Multi-Source Single- Channel Recordings by Jose Krause Perin, Maria Frank, and Neil Gallagher,2015
  2. Speaker Diarization: A Review of Recent Research Xavier Anguera, Member, IEEE, Simon Bozonnet, Student Member, IEEE, Nicholas Evans, Member, IEEE, Corinne Fredouille, Gerald Friedland, Member, IEEE, Oriol Vinyals , 2011
  3. Performance Validation of the Modified KMeans Clustering Algorithm Clusters Data by S. Govinda Rao Associate Professor and Dr.A. Govardhan
  4. The Clustering Validity with Silhouette and Sum of Squared Errors published in Proceedings of the 3rd International Conference on Industrial Application Engineering 2015 by Tippaya Thinsungnoena*, Nuntawut Kaoungkub , Pongsakorn Durongdumronchaib , Kittisak Kerdprasopb , Nittaya Kerdprasop
  5. The cocktail party problem by Josh H. McDermott. http://mcdermottlab.mit.edu/papers/McDermott_2010_cocktail_party_problem.pdf  1016,2009
  6. Douglas A Reynolds, Thomas F. Quatieri, and Robert B. Dunn. Speaker verification using adapted Gaussian mixture models. Digital signal processing, 10:19–41, 2000
  7. Efficient DSP implementation of median filtering for real-time audio Noise reduction. Stephan Herzog. of the 16th Int. Conference on Digital Audio Effects (DAFx-13), Maynooth, Ireland, September 2-5, 2013.
  8. Christopher M. Bishop. Pattern Recognition and Machine learning. Springer Science, 2006.
  9. Thiruvengatanadhan, Speech Recognition using SVM, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 ,Sep 2018
  10. Practice on Classification using Gaussian Mixture Model Computer Science, Tufts University, Medford, USA , 2010
  11. Voice Activity Detection Using MFCC Features and Support Vector Machine Tomi Kinnunen1 , Evgenia Chernenko2 , Marko Tuononen2 , Pasi Fränti2 , Haizhou Li1 1 Speech and Dialogue Processing Lab, Institute for Infocomm Research (I2 R), Singapore 2 Speech and Image Processing Unit, Department of Computer Science, University of Joensuu, Finland ,2007

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65.

Authors:

Raghavendra S, Santosh Kumar J, Raghavendra B. K.

Paper Title:

Performance Evaluation of MachineLearning Techniques inDiabetes Prediction

Abstract: Diabetes diagnosis is very important at preliminary stage rather than treatment. In today’s world devices like sensors are used for detection of diabetes. Accurate classification techniques are required for automatic identification of diabetes disease. In regards to research diabetes prediction with minimal number of attributes (test parameters) is to be identified earlier research states about feature reduction but with less predictive accuracy. In this regards, this work exploits machine learning techniques(methodology) such as Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN) with 10-fold Cross Validation (CV) for classification and prediction of diabetes with Feature Selection Methods (FSMs) using R platform. Above all models enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The experiment was conducted on PIMA Indian diabetes dataset selected from UCI machine learning repository. From the experimental results it is identified that for full set of diabetes dataset attributes, Classification Accuracy (CA) achieved was 84.25%whereas with reduced set attributes an accuracy of 85.24% is achieved using NN with 10-fold CV technique compared to others which will help in medical application to predict diabetes with minimal features.

Keywords: Logistic regression; Artificial neural network; Random forest; Support vector machine; Neural network with 10-fold.

References:

  1. K. Wasan and V. Bhatnagar and H. Kaur, “The Impact of Data Mining Techniques on Medical Diagnostics,” Data Science Journal, vol. 5, pp.119-126, 2006.
  2. K. Bodla and S. M. Malik and M. T. Rasheed and M. Numan and M. Z. Ali and J. B. Brima,“Logistic Regression and Feature Extraction Based Fault Diagnosis of Main Bearing of Wind Turbines,” IEEE 11th International Conference on Industrial Electronics and Applications (ICIEA), pp. 1628-1633, 2016.
  3. P. Prathibhamol and K. V. Jyothy and B. Noora,“Multi Label Classification Based on Logistic Regression (MLC-LR),” International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 2708-2712, 2016.
  4. K. Raghavendraand J. B. Simha,“Performance Evaluation of Logistic Regression and Neural Network Model with Feature Selection Methods and Sensitivity Analysis on Medical Data Mining,” International Journal of Advanced Engineering Technology, vol. 2, no. 1, pp. 289-298, 2011.
  5. Cortes and V. Vapnik, “Support Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
  6. K. Ho, “The Random Subspace Method for Constructing Decision Forests,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 832-844, 1998.
  7. G. Touw and J. R. Bayjanov and L. Overmars and L. Backus and J. Boekhorst and M. Wels and S. A. F. T. V. Hijum, “Data Mining in the Life Sciences with Random Forest: a Walk in the park or lost in Jungle?,” Briefings in Bioinformatics, vol. 14, no. 3, pp. 315-326, 2012.
  8. L. Raymer and T. E. Doom and L. A. Kuhn and W. F. Punch, “Knowledge Discovery in Medical and Biological Datasets Using a Hybrid Bayes Classifier/Evolutionary Algorithm,”IEEE Transactions on Systems, Man and Cybernatics, vol. 33, no. 5, pp. 802-813, 2003.
  9. B. Dowlatshahi and M. Rezaeian, “Training Spiking Neurons with Gravitational Search Algorithm for Data Classification,” IEEE 1st International Conference on Swarm Intelligence and Evolutionary Computation, pp. 53-58, 2016.
  10. Tuba and M. Tuba and D. Simian,“Adjusted Bat Algorithm for Tuning of Support Vector Machine Parameters,” IEEE Congress on Evolutionary Computation, pp. 2225-2232, 2016.
  11. Bruni and G. Bianchi, “Effective Classification Using a Small Training Set Based on Discretization and Statistical Analysis,” IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 9, pp. 2349-2361, 2015.
  12. Sykacek and S. Roberts, “Adaptive Classification by Variational Kalman Filtering,” Advances in Neural Information Processing Systems (NIPS), pp. 737-744, 2002.
  13. J. Li and Y. H. Qian and J. T. Wang and J. Y. Liang, “Multigranulation Information Fusion: A Dempster-Shafer Evidence Theory Based Clustering Ensemble Method,” Proceedings of IEEE International Conference on Machine Learning and Cybernetics (ICMLC), vol. 1, pp. 58-63, 2015.
  14. J. Perantonis and V. Virvilis, “Input Feature Extraction for Multilayered Perceptrons Using Supervised Principal Component Analysis,” Neural Processing Letters, vol. 10, no. 3, pp. 243-252, 1999.
  15. A. Ratanamahatana and D. Gunopulos, “Feature Selection for the Naïve Bayesian Classifier Using Decision Trees,” Applied Artificial Intelligence (AAI), vol. 17, no. 5-6, pp. 475-487, 2003.
  16. Divina and E. Marchiori, “Knowledge-Based Evolutionary Search for Inductive Concept Learning,” Knowledge Incorporation in Evolutionary Computation, Springer, vol. 167, pp. 237-253, 2005.
  17. T. Hilda and R. R. Rajalaxmi, “Effective Feature Selection for Supervised Learning Using Genetic Algorithm,” IEEE 2nd International Conference on Electronics and Communication Systems (ICECS 2015), pp. 909-914, 2015.
  18. Blayvas and R. Kimmel, “Machine Learning via Multiresolution Approximations,” IEICE Transaction on Information System, vol. E86-D, no. 7, pp. 1172-1180, 2003.
  19. Watkins and J. Timmis and L. Boggess, “Artificial Immune Recognition System (AIRS): An Immune Inspired Supervised Learning Algorithm,” Genetic Programming and Evolvable Machines, vol. 5, no. 3, pp. 291-317, 2004.
  20. Dora and S. Sundaram and N. Sundararajan,“A Two Stage Learning Algorithm for a Growing-Pruning Spiking Neural Network for Pattern Classification Problems,”International Joint Conference on Neural Networks (IJCNN), pp. 1-7, 2015.
  21. Raghavendra and M. Indiramma, “Performance Evaluation of Logistic Regression and Artificial Neural Network Model with Feature Selection Methods Using Cross Validation Sample and Percentage Split on Medical Datasets,” International Conference on Emerging Research in Computing, Information, Communication and Applications, vol. 2, 2014.
  22. Raghavendra and M. Indiramma, “Classification and Prediction Model Using Hybrid Technique for Medical Datasets,” International Journal of Computer Applications, vol. 127, no. 5, pp. 20-15, 2015.
  23. Raghavendra and M. Indiramma, “Hybrid Data Mining Model for the Classification and Prediction of Medical Datasets,” International Journal of Knowledge Engineering and Soft Data Paradigms,” vol. 5, no. 3/4, pp. 262- 284, 2017.
  24. G. Woldemichael and S. Menaria, “Prediction of Diabetes Using Data Mining Techniques,” 2nd International Conference on Trends In Electronics and Informatics, pp. 414-418 , 2018.
  25. K. Choubey and S. Paul and S. Kumar and S. Kumar,“Classification of Pima Indian Diabetes Dataset Using Naïve Bayes with Genetic Algorithm as an Attribute Selection,” Communication and Computing Systems, Taylor & Francis Group, pp. 451-455, 2017.
  26. Wei and X. Zhao and C. Miao, “A Comprehensive Exploration to the Machine Learning Technique for Diabetes Dataset,” IEEE 4th World Forum on Internet of Things, pp. 291-295, 2018.
  27. Mercaldo and V. Nardone and A. Santone,“Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques,”Procedia Computer Science,vol. 112, pp. 2519–2528, 2017.
  28. D. Sisodia and D. S. Sisodia.“Prediction of Diabetes using Classification Algorithms,”Procedia Computer Science, vol. 132, pp. 1578–1585, 2018.

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66.

Authors:

S. M. K. Chaitanya, P. Rajesh Kumar

Paper Title:

Classification of Kidney Images Using Cuckoo Search Algorithm and Artificial Neural Network

Abstract: Ultrasound (US) imaging is used to provide the structural abnormalities like stones, infections and cysts for kidney diagnosis and also produces information about kidney functions. The goal of this work is to classify the kidney images using US according to relevant features selection. In this work, images of a kidney are classified as abnormal images by pre-processing (i.e. grey-scale conversion), generate region-of-interest, extracting the features as multi-scale wavelet-based Gabor method, Cuckoo Search (CS) for optimization and Artificial Neural Network (ANN). The CS-ANN method is simulated on the platform of MATLAB and these results are evaluated and contrasted. The outcome of these results proved that the CS-ANNN had 100% specificity and 94% accuracy. By comparing it with the existing methods, the CS-ANN achieved 0% false-acceptance rate.

Keywords: Kidney diagnosis, Gabor feature extraction, Cuckoo search, Artificial Neural Network, Ultrasound images.

References:

  1. Kanavati, T. Tong, K. Misawa, M. Fujiwara, K. Mori, D. Rueckert, and B. Glocker, “Supervoxel classification forests for estimating pairwise image correspondences.,” Pattern Recognition,  vol. 63, pp. 561-569, 2017.
  2. Eklund, P. Dufort, D. Forsberg and S. M. La Conte, “Medical image processing on the GPU – past, present and future,” Med. Image Anal., vol. 17, pp. 1073–1094, 2013.
  3. Reiche, K. Häublein, M. Reichenbach, M. Schmid, F. Hannig, J. Teich and D. Fey, “Synthesis and optimization of image processing accelerators using domain knowledge,” J. Syst. Architect., vol. 61, pp. 646–658, 2015.
  4. K. Sharma, N. D. Toussaint, G. J. Elder, R. Masterson, S. G. Holt, P. L. Robertson, and C. S. Rajapakse, “Magnetic resonance imaging based assessment of bone microstructure as a non-invasive alternative to histomorphometry in patients with chronic kidney disease,” Bone, 2018.
  5. Razik, C. J. Das, and S. Sharma, "Angiomyolipoma of the Kidneys: Current Perspectives and Challenges in Diagnostic Imaging and Image-Guided Therapy," Current problems in diagnostic radiology 2018.
  6. Świetlicka, “Trained stochastic model of biological neural network used in image processing task,” Appl. Math. Comput, vol. 267, pp. 716–726, 2015.
  7. Tian, J. Xue, Y. Dai, J. Chen and J. Zheng, “A novel software platform for medical image processing and analyzing,” IEEE Trans. Inf. Technol. Biomed, vol. 12, pp. 800–812, 2008.
  8. S. Gur and M. Top, “Regional clustering of medical imaging technologies,” Comput. Hum. Behav., vol. 61, pp. 333–343, 2016.
  9. Rengier, M. F. Häfnerb, R. Unterhinninghofenc, R. Nawrotzkid, J. Kirsch, H.-U. Kauczor and F. L. Giesel, “Integration of interactive three-dimensional image post-processing software into undergraduate radiology education effectively improves diagnostic skills and visual-spatial ability,” Eur. J. Radiol, vol. 82, pp. 1366–1371, 2013.
  10. Sajn and M. Kukar, “Image processing and machine learning for fully automated probabilistic evaluation of medical images,” Comput. Methods Prog. Biomed, vol. 104, pp. e75–e86, 2011.
  11. Zhao, J. Zhao, W. Zhao, F. Qu and L. Sui, “Local region statistics combining multi-parameter intensity fitting module for medical image segmentation with intensity in homogeneity and complex composition,” Optics Laser Technol, vol. 82, pp. 17–27, 2016.
  12. Serrat, F. Lumbreras, F. Blanco, M. Valiente, and M. López-Mesas, “myStone: A system for automatic kidney stone classification,” Expert Systems with Applications,  vol. 89, pp. 41-51, 2017.
  13. Verma, M. Nath, P. Tripathi, and K. K. Saini, “Analysis and identification of kidney stone using Kth nearest neighbour (KNN) and support vector machine (SVM) classification techniques,” Pattern Recognition and Image Analysis,  vol. 27, no. 3, pp. 574-580, 2017.
  14. B. Subramanya, V. Kumar, S. Mukherjee, and M. Saini, “SVM-based CAC system for B-mode kidney ultrasound images,” Journal of digital imaging,  vol. 28, no. 4, pp. 448-458, 2015.
  15. A. Tuncer, and A. Alkan, "A decision support system for detection of the renal cell cancer in the kidney." Measurement vol. 123, pp. 298-303, 2018.
  16. K. D. Krishna, V. Akkala, R. Bharath, P. Rajalakshmi, A. M. Mohammed, S. N. Merchant, and U. B. Desai, “Computer aided abnormality detection for kidney on FPGA based IoT enabled portable ultrasound imaging system,” Irbm,  vol. 37, no. 4, pp. 189-197, 2016

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67.

Authors:

K. M.Dhanya, S. Kanmani

Paper Title:

Mutated Butterfly Optimization Algorithm

Abstract: Butterfly optimization algorithm is a nature inspired metaheuristic algorithm which adapts food foraging behavior of butterflies. Butterfly optimization algorithm was introduced to solve benchmark functions and engineering design optimization problems. Mutated butterfly optimization algorithm, a new variant of butterfly optimization algorithm is proposed in this work for solving global optimization problems. It is an approach which combines butterfly optimization algorithm with Cauchy mutation to achieve global optimal solution by avoiding entrapment in local optima. The validation of proposed algorithm is carried out on low dimensional and high dimensional test functions. The experimental results are compared with basic butterfly optimization algorithm and other variants of it reported in the literature. The Wilcoxon signed rank test is also performed to identify the significance of proposed algorithm with other methods. The proposed method has achieved better results than basic butterfly optimization algorithm and its variants on various test functions.

Keywords: Butterfly Optimization Algorithm, Nature Inspired Metaheuristic Algorithm, Mutated Butterfly Optimization Algorithm, Cauchy Mutation, Wilcoxon Signed Rank Test

References:

  1. Arora, S. and Singh, S.,” Butterfly optimization algorithm: a novel approach for global optimization”, Soft Computing, 2018, pp.1-20.
  2. Wang, G.G., Deb, S. and Cui, Z.,” Monarch butterfly optimization “, Neural Computing & Applications, 2015.
  3. Faris, H., Aljarah, I. and Mirjalili, S.,” Improved monarch butterfly optimization for unconstrained global search and neural network training”, Applied Intelligence, 48(2), 2018, pp.445-464.
  4. Wang, G.G., Deb, S., Zhao, X. and Cui, Z.,” A new monarch butterfly optimization with an improved crossover operator”, Operational Research, 2016, pp.1-25.
  5. Qi, X., Zhu, Y. and Zhang, H.,” A new meta-heuristic butterfly-inspired algorithm”, Journal of Computational Science, 23, 2017, pp.226-239.
  6. Zheng, J.G., Zhang, C.Q. and Zhou, Y.Q.,” Artificial bee colony algorithm combined with grenade explosion method and Cauchy operator for global optimization”, Mathematical Problems in Engineering, 2015.
  7. Paiva, F.A., Silva, C.R., Leite, I.V., Marcone, M.H. and Costa, J.A.,” Modified bat algorithm with cauchy mutation and elite opposition-based learning”, 2017, November, In Computational Intelligence (LA-CCI), 2017 IEEE Latin American Conference on (pp. 1-6). IEEE.
  8. Ali, M. and Pant, M.,” Improving the performance of differential evolution algorithm using Cauchy mutation”, Soft Computing, 15(5), 2011, pp.991-1007.
  9. Wang, H., Wang, W., Sun, H. and Rahnamayan, S.,” Firefly algorithm with random attraction”, International Journal of Bio-Inspired Computation, 8(1), 2016, pp.33-41.
  10. Salgotra, R. and Singh, U., “Application of mutation operators to flower pollination algorithm”,Expert Systems with Applications, 79, 2017,pp.112-129.
  11. Iqbal, M.A,Khan,N.K., Jaffar, M.A.,Ramzan, M.and Baig, A.R,” Opposition based genetic algorithm with Cauchy mutation for function optimization”, 2010, April In Information Science and Applications (ICISA), 2010 International Conference on (pp. 1-7). IEEE.
  12. Xu, S., Wang, Y. and Lu, P.,” Improved imperialist competitive algorithm with mutation operator for continuous optimization problems”, Neural Computing and Applications, 28(7), 2017, pp.1667-1682.
  13. Wang, H., Li, H., Liu, Y., Li, C. and Zeng, S.,” Opposition-based particle swarm algorithm with Cauchy mutation”, 2007, September, In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on (pp. 4750-4756). IEEE.
  14. Wu, Q. and Law, R.,” Cauchy mutation based on objective variable of Gaussian particle swarm optimization for parameters selection of SVM”, Expert Systems with Applications, 38(6), 2011, pp.6405-6411.
  15. Wang, H., Li, C., Liu, Y. and Zeng, S.,” A hybrid particle swarm algorithm with Cauchy mutation”, 2007, April, In Swarm Intelligence Symposium, 2007. SIS 2007. IEEE (pp. 356-360). IEEE.
  16. Arora, S., Singh, S. and Yetilmezsoy, K.,” A modified butterfly optimization algorithm for mechanical design optimization problems”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 40(1), 2018, p.21.
  17. Arora, S. and Singh, S.,” An improved butterfly optimization algorithm with chaos. Journal of Intelligent & Fuzzy Systems”, 32(1), 2017, pp.1079-1088.
  18. Arora, S. and Singh, S.,” An Improved Butterfly Optimization Algorithm for Global Optimization”, Advanced Science, Engineering and Medicine, 8(9), 2016, pp.711-717.
  19. Arora, S. and Singh, S.,” An Effective Hybrid Butterfly Optimization Algorithm with Artificial Bee Colony for Numerical Optimization”, International Journal of Interactive Multimedia & Artificial Intelligence, 4(4), 2017.
  20. Arora, S. and Singh, S.,” A hybrid optimisation algorithm based on butterfly optimisation algorithm and differential evolution”, International Journal of Swarm Intelligence, 3(2-3), 2017, pp.152-169.
  21. Jamil, M. and Yang, X.S.,” A literature survey of benchmark functions for global optimization problems”, 2013, arXiv preprint arXiv:1308.4008.
  22. McCrum-Gardner, E.,” Which is the correct statistical test to use?”, British Journal of Oral and Maxillofacial Surgery, 46(1), 2008, pp.38-41.
  23. Derrac, J., García, S., Molina, D. and Herrera, F.,” A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms”, Swarm and Evolutionary Computation, 1(1), 2011, pp.3-18.
  24. Wilcoxon, F.,” Individual comparisons by ranking methods”, Biometrics bulletin, 1(6), 1945, pp.80-83.
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68.

Authors:

N. K. Kund

Paper Title:

EMS Route Designed for SSM Processing

Abstract: The various segments of the EMS arrangement are established. Also, the experimental EMS system comprises the cooling as well as shearing for getting the final microstructure of the semisolid cast billets. The comprehensive experiments are performed with the stated electromagnetic stirrer (EMS) using molten A356 aluminum alloy at pouring temperature of 625 °C. Through the reasonably moderate cooling the stirring intensity corresponding to the shear rate of 200 s-1 is used. The semisolid cast billets thus manufactured are sliced to make samples for further processing which is successively intended for metallographic analysis with optical microscope. It also illustrates the microstructure thus obtained at the stated shear rate. It is observed that the grains are almost uniformly distributed because of the moderate cooling alongside the shearing. Additionally, it also demonstrates the corresponding grain size frequency distribution as developed from the stated metallographic analysis. As a whole, the present study unveils the experimental preparations accompanied by the instrumentations besides the trials (such as melt preparation, treatment and transfer, temperature measurement with K-type thermocouple and flow measurement with rotameter) for semisolid casting experiments (with the EMS) and microstructure studies considering A356 aluminum alloy.

Keywords: Mould, Processing, EMS, Semisolid.

References:

  1. Kund N. K., P. Dutta P., 2010, Numerical simulation of solidification of liquid aluminium alloy flowing on cooling slope, Trans. Nonferrous Met. Soc. China, Vol. 20, pp. s898-s905.
  2. Kund N. K., Dutta P., 2012, Scaling analysis of solidification of liquid aluminium alloy flowing on cooling slope, Trans. Indian Institute of Metals, Vol. 65, pp. 587-594.
  3. Kund N. K., 2014, Influence of melt pouring temperature and plate inclination on solidification and microstructure of A356 aluminum alloy produced using oblique plate, Trans. Nonferrous Met. Soc. China, Vol. 24, pp. 3465−3476.
  4. Kund N. K., 2015, Influence of plate length and plate cooling rate on solidification and microstructure of A356 alloy produced by oblique plate, Trans. Nonferrous Met. Soc. China, Vol. 25, pp. 61−71.
  5. Kund N. K., Dutta P., 2015. Numerical study of solidification of A356 aluminum alloy flowing on an oblique plate with experimental validation, J Taiwan Inst. Chem. Ers., Vol. 51, pp. 159−170.
  6. Kund N. K., Dutta P., 2016, Numerical study of influence of oblique plate length and cooling rate on solidification and macrosegregation of A356 aluminum alloy melt with experimental comparison, J. Alloys Compd., Vol. 678, pp. 343−354.
  7. Kund N. K., 2018, Effect of tilted plate vibration on solidification and microstructural and mechanical properties of semisolid cast and heat-treated A356 Al alloy, Int. J. Adv. Manufacturing Technol., Vol. 97, pp. 1617−1626.

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69.

Authors:

Sudipta Gayen, Sripati Jha, Manoranjan Singh, Ranjan Kumar

Paper Title:

On a Generalized Notion of Anti-fuzzy Subgroup and Some Characterizations

Abstract: We have presented a new notion of anti-fuzzy subgroup (AFS). For this, we have considered general t-conorm. The main contributions of this paper are fourfold: (1) we have proposed a new notion of anti-fuzzy subgroup (AFS), (2) we have also defined infimum image of a fuzzy set, (3) Furthermore, we have defined subgroup generated anti-fuzzy subgroup (SGAFS), function generated anti-fuzzy subgroup (FGAFS) and (4) we have shown that an AFS proposed earlier belong to a special class of subgroup generated anti-fuzzy subgroup (SGAFS). To justify our proposed notion we have discussed some drawbacks of the existing notion of AFS with numerical examples. Finally, we have concluded that our proposed notion is superior to the existing one.

Keywords: Infimum image; Subgroup generated anti-fuzzy subgroup; Function generated anti-fuzzy subgroup.

References:

  1. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, pp. 338-358, 1965.
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  7. Nanda, “Fuzzy fields and fuzzy linear spaces,” Fuzzy Sets and Systems, vol. 19, pp. 89-94, 1986.
  8. Schweizer and A. Sklar, Probabilistic metric spaces, Courier Corporation, 2011.
  9. C. Lee, “Fuzzy logic in control systems: fuzzy logic controller. I,” IEEE Transactions on Systems, Man and cybernetics, vol. 20, pp. 404-418, 1990.
  10. Grabisch, “Fuzzy integral in multi-criteria decision making,” Fuzzy Sets and Systems, vol. 69, pp. 279-298, 1995.
  11. M. Gupta and J. Qi, “Theory of T-norms and fuzzy inference methods,” Fuzzy Sets and Systems, vol. 40, pp. 431-450, 1991.
  12. Garg, “Generalized Pythagorean fuzzy geometric aggregation operators using Einstein t-norm and t-conorm for multi-criteria decision-making process,” International Journal of Intelligent Sy-stems, vol. 32, pp. 597-630, 2017.
  13. M. Anthony and H. Sherwood, “Fuzzy groups redefined,” Jour-nal Of Mathematical Analysis And Applications, vol. 69, pp. 124-130, 1979.
  14. M. Anthony and H. Sherwood, “A characterization of fuzzy subgroups,” Fuzzy Sets and Systems, vol. 7, pp. 297-305, 1982.
  15. Biswas, “Fuzzy subgroups and anti-fuzzy subgroups,” Fuzzy Sets and Systems, vol. 35, pp. 121-124, 1990.
  16. Y. Li, C. Y. Zhang and S. Q. Ma, “The Intuitionistic Anti-fuzzy Subgroup in Group G,” in Fuzzy Information and Engin-eering, Springer, 2009, pp. 145-151.
  17. T. Atanassov, “Intuitionistic fuzzy sets.” Fuzzy sets and Syst-ems, vol. 20, pp. 87-96, 1986.
  18. T. Atanassov, Intuitionistic fuzzy sets, Springer, 1999, pp. 1-137.
  19. Feng and B. Yao, “On (λ, μ)-anti-fuzzy subgroups,” Journal of Inequalities and Applications, vol. 2012, p. 78, 2012.
  20. Yuan, C. Zhang and Y. Ren, “Generalized fuzzy groups and many-valued implications,” Fuzzy Sets and Systems, vol. 138, pp. 205-211, 2003.
  21. H. Kim and Y. H. Yon, “On anti-fuzzy ideals in near-rings,” Iranian Journal of Fuzzy Systems, vol. 2, pp. 71-80, 2005.
  22. H. Kim and Y. B. Jun, “Anti fuzzy R-subgroups of near-rings,” Scientiae Mathematicae, vol. 2, pp. 147-153, 1999.
  23. M. Hong and Y. B. Jun, “Anti fuzzy ideals in BCK-algebras,” Kyungpook Mathematical Journal, vol. 38, pp. 145-145, 1998.
  24. A. Borzooei, R. Ali, A. Saeid, B. Arsham, A. Rezaei and R. Ameri, “Anti-fuzzy filters of CI-algebras,” Afrika Matematika, vol. 25, pp. 1197-1210, 2014.
  25. B. Jun, “Doubt fuzzy BCK/BCI-algebras,” Soochow Journal of Mathematics, vol. 20, pp. 351-358, 1994.
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  28. R. Yager and L. A. Zadeh, An introd- uction to fuzzy logic ap-plications in intelligent systems, vol. 165, Springer Science & Bu-siness Media, 2012.
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  34. D. Driankov and A. Saffiotti, Fuzzy logic techniques for autono-mous vehicle navigation, vol. 61, Physica, 2013.

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70.

Authors:

Namgay Tenzin, R.P. Saini

Paper Title:

Wind and Solar Resource Potential Assessment in Bhutan

Abstract: Renewable energy sources have become important part of energy mix in the modern power grid because of limited non-renewable resources, increasing price of fossil fuels and environmental concern due to use of fossil fuels. Wind and solar power technologies are two most mature technologies today which can offset the use of fossil fuel-based power plants and decarbonize the electrical grid. Bhutan is generating renewable power by using its rivers in the run off river hydropower scheme, which is supplied across the whole country. Apart from hydropower small scale wind and standalone solar PV systems are currently operational; limited studies are available on solar and wind power potentials in Bhutan. Therefore, this paper discusses the assessment wind and solar resource potential of some of the selected sites of Bhutan using measured data from local weather stations and also NRELs climate data. The wind data is used to analyze the wind power density and wind power potential using Weibull distribution. Also, wind power potential at different heights are estimated after extrapolating wind speeds using wind log formulae. Wind power potential estimate analysis shows that valleys of Wangduephodrang, Tsirang and Trashiyangtse are found to have good potential for wind farm development. Solar PV system potential assessment using NREL’s TMY data for solar power development Wangduephodrang and Paro valleys is found to have very good potential.

Keywords: Solar, Wind, Wind power density, Weibull distribution 

References:

  1. 2018 REN21, Renewables 2018 Global Status Report. Paris: REN21 Secretariat, 2018.
  2. RGoB, “Economic Development Policy Royal Government of Bhutan June 2016,” no. June, 2016.
  3. Department of Renewabale Energy, MoEA, “Bhutan Energy Data Directory,” Thimphu, 2015.
  4. M. Bataineh and D. Dalalah, “Assessment of wind energy potential for selected areas in Jordan,” Renew. Energy, vol. 59, pp. 75–81, 2013.
  5. Mentis, S. H. Siyal, A. Korkovelos, and M. Howells, “A geospatial assessment of the techno-economic wind power potential in India using geographical restrictions,” Renew. Energy, vol. 97, pp. 77–88, 2016.
  6. Luankaeo and Y. Tirawanichakul, “Assessment of Wind Energy Potential in Prince of Songkla University (South Part of Thailand): Hatyai campus,” Energy Procedia, vol. 138, pp. 704–709, 2017.
  7. Waewsak, C. Kongruang, and Y. Gagnon, “Assessment of wind power plants with limited wind resources in developing countries : Application to Ko Yai in southern Thailand,” Sustain. Energy Technol. Assessments, vol. 19, pp. 79–93, 2017.
  8. Aksas and A. Gama, “Assessment of wind and solar energy resources in Batna, Algeria,” Energy Procedia, vol. 6, pp. 459–466, 2011.
  9. Mohammadi, A. Mostafaeipour, and M. Sabzpooshani, “Assessment of solar and wind energy potentials for three free economic and industrial zones of Iran,” Energy, vol. 67, pp. 117–128, 2014.
  10. Watts, N. Oses, and R. Pérez, “Assessment of wind energy potential in Chile: A project-based regional wind supply function approach,” Renew. Energy, vol. 96, pp. 738–755, 2016.
  11. A. Prasad, R. A. Taylor, and M. Kay, “Assessment of solar and wind resource synergy in Australia,” Appl. Energy, vol. 190, pp. 354–367, 2017.
  12. M. Ershad, R. J. Brecha, and K. Hallinan, “Analysis of solar photovoltaic and wind power potential in Afghanistan,” Renew. Energy, vol. 85, pp. 445–453, 2016.
  13. Ucar and F. Balo, “Assessment of wind power potential for turbine installation in coastal areas of Turkey,” Renew. Sustain. Energy Rev., vol. 14, no. 7, pp. 1901–1912, 2010.
  14. Gilman, D. Heimiller, and S. Cowlin, “Potential for Development of Solar and Wind Resource in Bhutan,” 2009.
  15. M. Patel, Wind and Solar Power Systems Design,Analysis and Operation, 2nd Editio. New York,USA: CRC Press, Taylor & Francis Group, 2006.

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71.

Authors:

Manish Kumar Rawat, Sanjeev Kumar Gupta

Paper Title:

Effect of Plain Shear Velocity Profile over a Streamline Cylinder

Abstract: Flow with plain shear velocity profile with high Reynolds Number (Re = 0.5 ×106 to 3.6×106) around a flat (2D) streamlined cylinder, is simulated by using a k- ε turbulence model. The main target of this study is to assess the influence of plain shear velocity and axis ratio on the important flow variables, such as drag force, point of separation and mean drag coefficient, along the exterior surface of a flat elliptical cylinder. Drag force over a circular cylinder as compare to elliptical cylinder is higher. Reduction in drag may be increased by reducing the axis ratio with plain Shear velocity profile. The point of separation moves forward as the cylinder became streamlined. 

Keywords: Flat Streamlined Cylinder, Plain Shear Velocity, Axis Ratio, Turbulent Flow etc. 

References:

  1. Achenbach, E., 1968. Distribution of local pressure and skin friction around a circular cylinder in cross-flow up to Re= 5×106. J Fluid Mech; 34(4):625–39.
  2. Ong, MC., Utnes, T., Holmedal, LE., Myrhaug, D., Pettersen, B., 2009. Numerical simulation of flow around a smooth circular cylinder at very high Reynolds numbers. Marine Structures 22: 142–153.
  3. Catalano, P., Wang, M., Laccarino, G., Moin, P., 2003. Numerical simulation of the flow around a circular cylinder at high Reynolds numbers. International Journal of Heat and Fluid Flow 24: 463–469.
  4. Mochimaru, Y., 1992. Numerical simulation of flow past an elliptical cylinder at moderate and high Reynolds numbers, using a spectral method. 11th Australasian fluid mechanics conference.
  5. Li, Z., Davidson, J., Mantell, S., 2005. Numerical simulation of flow field and heat transfer of streamlined cylinders in crossflow. ASME Summer Heat Transfer Conference, 2005­­­­
  6. Mittal, R., Balachandar, S., 1996. Direct Numerical Simulation of Flow Past Elliptic Cylinders. Journal of computational physics 124, 351–367.
  7. Singh, SP., Mittal, S., 2005. Flow past a cylinder: shear layer instability and drag crisis. Int J Numer Meth Fluids 2005,75–98.
  8. Tutar, M., Holdo, AE., 2001. Computational modeling of flow around a circular cylinder in sub-critical flow regime with various turbulence models. Int J Numer Meth Fluids 2001;35, 763–84.
  9. Rodi, W., 1993. Turbulence models and their application in hydraulics. A state-of-the-art review. IAHR Monograph Series. 3rd ed. Rotterdam, Netherlands: A.A. Balkema; 1993.[10]  Launder, BE., Spalding, DB., 1972. Mathematical models of turbulence. London: Academic Press.

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72.

Authors:

Vamsi Naidu Pothana, Sriram Konduru, Venkata Rao Junju,

Paper Title:

Implementing Mining in Educational Digitization

Abstract: This paper projects the aspects and criteria in which the present day education system is running in our country and what are the changeovers we had from the past system with also the benevolent changes that could aid for a more sophisticated education system for the future generations of the country. Also we will discuss certain aspects which will enhance the student to become a more efficient student if the terms are met and followed by the institutions. The system has gone under a drastic change in the recent years and is changing minute to minute in this informatics world. The world in which information is everything education may become the only means for the future generations to survive in this world.

Keywords: Education system, Change, information, institutions.

References:

  1. The case against education: Why the education system is a waste of time and money-B Caplan – 2018
  2. Research performance and teaching quality in the Spanish higher education system: Evidence from a medium-sized university- J Artés, F Pedraja-Chaparro… - Research Policy, 2017
  3. Impact of Socio Economic Trends on Students in Quality Education System- R Billaiya, S Malaiya, KS Parihar - International Journal of Social …, 2017
  4. Duflo, R. Hanna, S. RyanIncentives work: getting teachers to come to school-Am. Econ. Rev., 102 (4) (2012), pp. 1241-1278
  5. Anand, A. Mizala, A. RepettoUsing school scholarships to estimate the effects of private education on the academic achievement of low-income students in Chile-Econ. Educ. Rev., 28 (2009), pp. 370-381
  6. “The case for curriculum reform in Australian information management & library and information science education: Part 1., Technology and digitization as drivers” by Hairong Yuand Mari Davis, Information Management Research Group, School of Information Systems, Technology and Management, The University of New South Wales, Sydney NSW 2052, Australia
  7. https://www.wikipedia.com
  8. https://www.google.co.in

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73.

Authors:

R Sujatha, S. Sree Dharinya, E P Ephzibah, R Kiruba Thangam

Paper Title:

K-Means and Hierarchical based Clustering in Suicide Analysis

Abstract: Machine learning is the intriguing area of research that spreads across all domains helping in providing quality decisions. Demographic have more influence in social happenings along with various personal and social factors. Suicide analysis is one such issue to be handled with great concern that will provide precautionary based on situations. Suicide prediction can be carried on using data mining that can be used to predict the suicide earlier so that it can be prevented. Suicide is an action resulting in death performed by themselves. Common factors that influence the rate of suicides are cause, method of suicide, year, gender, educational qualification, social status. For this clustering technique in datamining that falls under unsupervised provides great platform. Silhouette score is used for mapping the number of cluster to get the good clustering. Various plots like box plot, scatter plot and so on helps to provide greater insight. Based on analysis the required remedial could be arrived.

Keywords: Clustering, Suicide, Gender, Age, Education,

References:

  1. Linthicum, Kathryn P., Katherine Musacchio Schafer, and Jessica D. Ribeiro. "Machine learning in suicide science: Applications and ethics." Behavioral sciences & the law(2019).
  2. https://www.befrienders.org/suicide-statistics
  3. Beck, Aaron T., Dan J. Lettieri, and Harvey LP Resnik, eds. The prediction of suicide. Bowie, MD: Charles Press Publishers, 1974.
  4. Larose, Daniel T. Data mining methods & models. John Wiley & Sons, 2006.
  5. Mandge, Omprakash L., and Bandra Reclamation. "A data mining tool for prediction of suicides among students." In Proceedings of National Conference on New Horizons in IT-NCNHIT, p. 178. 2013.
  6. https://www.kaggle.com/rajanand/suicides-in-india
  7. Anil, R. A. N. E., and Abhijit Nadkarni. "Suicide in India: a systematic review." Shanghai archives of psychiatry26, no. 2 (2014): 69.
  8. Beautrais, Annette L. "Risk factors for suicide and attempted suicide among young people." Australian & New Zealand Journal of Psychiatry34, no. 3 (2000): 420-436.
  9. Capó, Marco, Aritz Pérez, and Jose A. Lozano. "An efficient K-means clustering algorithm for massive data." arXiv preprint arXiv:1801.02949(2018).
  10. Choo, Carol, Joachim Diederich, Insu Song, and Roger Ho. "Cluster analysis reveals risk factors for repeated suicide attempts in a multi-ethnic Asian population." Asian journal of psychiatry8 (2014): 38-42.
  11. https://en.wikipedia.org/wiki/Silhouette_(clustering) Accessed 20 February 2019.
  12. Bani, Marco, Gabriele Travagin, Michele Monticelli, Manuela Valsecchi, Emanuele Truisi, Federico Zorzi, Mariagrazia Strepparava, Massimo Clerici, Umberto Mazza, and Giorgio Rezzonico. "Pattern of self-injurious behavior and suicide attempts in Italian custodial inmates: A cluster analysis approach." International Journal of Law and Psychiatry64 (2019): 1-7.
  13. Maltsberger, John T., Herbert Hendin, Ann Pollinger Haas, and Alan Lipschitz. "Determination of precipitating events in the suicide of psychiatric patients." Suicide and Life-Threatening Behavior33, no. 2 (2003): 111-119.
  14. Marks, Mason. "Artificial Intelligence Based Suicide Prediction." Yale Journal of Health Policy, Law, and Ethics, Forthcoming(2019).
  15. Velupillai, Sumithra, Gergö Hadlaczky, Enrique Baca-Garcia, Genevieve M. Gorrell, Nomi Werbeloff, Dong Nguyen, Rashmi Patel et al. "Risk Assessment Tools and Data-driven Approaches for Predicting and Preventing Suicidal Behaviour." Frontiers in Psychiatry10 (2019): 36.
  16. Glenn, Jeffrey J., Alicia Nobles, Laura Barnes, and Bethany Teachman. "Can Text Messages Identify Suicide Risk in Real Time? A Within-Subjects Pilot Examination of Temporally-Sensitive Markers of Suicide Risk." (2019).

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74.

Authors:

N. K. Kund

Paper Title:

Cooling Slope Practice for SSF Technology

Abstract: Currently, the usual methods available for large scale production of semisolid slurry are mechanical stirring, electromagnetic stirring, etc. These suffer from drawbacks like complex design, high cost, structural inhomogeneity and low efficiency. The cooling slope is considered to be a simple but effective method because of its simple design and easy control of process parameters, low equipment and running costs, high production efficiency and reduced inhomogeneity. With this perspective, the primary objective of the present research is to investigate experimentally the solidification on a cooling slope, in addition to the study of final microstructure of the semisolid cast billets. In most casting applications, dendritic microstructure morphology is not desired because it leads to poor mechanical properties. Forced convection causing sufficient shearing in the mushy zone of the partially solidified melt is one of the means to suppress this dendritic growth. The dendrites formed at the solid-liquid interface are detached and carried away due to strong fluid flow to form slurry. This slurry, consisting of rosette or globular particles, provides less resistance to flow even at a high solid fraction and can easily fill the die-cavity. The stated principle is the basis of a new manufacturing technology called “semi-solid forming” (SSF), in which metal alloys are cast in the semi-solid state. This technique has numerous advantages over other existing commercial casting processes, such as reduction of macrosegregation, reduction of porosity and low forming efforts.

Keywords: Aluminum Alloy, Casting, Cooling Slope, Semisold, Slurry.

References:

  1. Kund N. K., P. Dutta P., 2010, Numerical simulation of solidification of liquid aluminium alloy flowing on cooling slope, Trans. Nonferrous Met. Soc. China, Vol. 20, pp. s898-s905.
  2. Kund N. K., Dutta P., 2012, Scaling analysis of solidification of liquid aluminium alloy flowing on cooling slope, Trans. Indian Institute of Metals, Vol. 65, pp. 587-594.
  3. Kund N. K., 2014, Influence of melt pouring temperature and plate inclination on solidification and microstructure of A356 aluminum alloy produced using oblique plate, Trans. Nonferrous Met. Soc. China, Vol. 24, pp. 3465−3476.
  4. Kund N. K., 2015, Influence of plate length and plate cooling rate on solidification and microstructure of A356 alloy produced by oblique plate, Trans. Nonferrous Met. Soc. China, Vol. 25, pp. 61−71.
  5. Kund N. K., Dutta P., 2015. Numerical study of solidification of A356 aluminum alloy flowing on an oblique plate with experimental validation, J Taiwan Inst. Chem. Ers., Vol. 51, pp. 159−170.
  6. Kund N. K., Dutta P., 2016, Numerical study of influence of oblique plate length and cooling rate on solidification and macrosegregation of A356 aluminum alloy melt with experimental comparison, J. Alloys Compd., Vol. 678, pp. 343−354.
  7. Kund N. K., 2018, Effect of tilted plate vibration on solidification and microstructural and mechanical properties of semisolid cast and heat-treated A356 Al alloy, Int. J. Adv. Manufacturing Technol., Vol. 97, pp. 1617−1626.

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75.

Authors:

Sergei Sergeevich Golubev, Vyacheslav Ivanovich Volkov, Anton Gennadievich Shcherbakov, Vladimir Dmitriyevich Sekerin, Anna Evgenievna Gorokhova

Paper Title:

Manpower Support for Digital Technology Implementation Processes in Industrial Enterprises

Abstract: Today’s rapid advances in digital technology are resulting in a transformation of the future labor market. Given the ever-increasing use of digital technology, the resolving of complex production objectives may well result in job cuts, changes in personnel requirements, and new areas of activity emerging as a result of digital transformations. Issues related to manpower support for the implementation of digital technology are setting new objectives not only in terms of fostering the competencies of the future but also in terms of organizing a business and training and retraining a workforce at the level of both the industrial sector and the national economy at large.The paper describes some of the new trends in the labor market associated with job robotization and analyzes a set of factors influencing the rate of workforce engagement in the robotized production process. The authors suggest that boosts in entrepreneurial activity in light of the extensive implementation of digital technology in production are associated with not so much job cuts due to automation as with new vistas of opportunity that the digital era is offering.The paper explores the role played by the state in resolving the objectives of ensuring social stability in a climate of the digitalization of the economy. The authors highlight some of the key skills that representatives of “high-risk” occupations may need to acquire nowadays. This may help design appropriate educational strategies aimed at guarding this group of workers from the undesired effects of production digitalization.

Keywords: digital technology, industrial enterprises, manpower support, labor market, entrepreneurship, social effects. 

References:

  1. Kergroach, “Industry 4.0: New challenges and opportunities for the labour market”, Foresight and STI Governance, 11(4), 2017, p. 6–8.
  2. Seidl da Fonseca, “The future of employment: Evaluating the impact of STI foresight exercises”, Foresight and STI Governance, 11(4), 2017, p. 9–22.
  3. Arntz, T. Gregory, U. Zierahn, “The risk of automation for jobs in OECD countries”: A comparative analysis. 2016. Retrieved from https://www.oecd-ilibrary.org/the-risk-of-automation-for-jobs-in-oecd-countries_5jlz9h56dvq7.pdf?itemId=%2Fcontent%2Fpaper%2F5jlz9h56dvq7-en&mimeType=pdf
  4. Brynjolfsson, A. McAfee, “Race against the machine: How the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy”, Lexington, MA: Digital Frontier Press, 2011.
  5. Nissen, T. Lezina, A. Saltan, “The role of IT management in the digital transformation of Russian companies”, Foresight and STI Governance, 12(3), 2018, p. 53–61.
  6. Tarabrin, “Ot tochechnykh IT-reshenii k proryvu – sozdaniyu “umnykh fabrik” v OPK” [Konstantin Tarabrin: “From targeted IT solutions to a breakthrough – the creation of “smart factories” within the military-industrial complex], Connect, 4, 2017, p. 4–11. www.connect-wit.ru/wp-content/uploads/2017/05/001_128_Connect_04_17_Sm.pdf   
  7. Bacon, M. Kojima, “Issues in estimating the employment generated by energy sector activities”, 2011. https://openknowledge.worldbank.org/bitstream/handle/10986/16969/827320WP0emplo00Box379875B00PUBLIC0.pdf?sequence=1&isAllowed=y
  8. S. Golubev, S.S. Chebotarev, “Informatsionnye tekhnologii kak klyuchevoi mekhanizm ustoichivogo razvitiya oboronnykh promyshlennykh predpriyatii v sovremennykh usloviyakh” [Information technology as a key mechanism for the sustainable development of military industrial enterprises in present-day conditions], Ekonomicheskie Strategii, 20(3), 2018, p. 68–81.
  9. A. Chulok, “Perestat' bespokoit'sya i nachat' uchit'sya. Mega trendy: Vzglyad na dinamicheskie portfeli kompetentsii budushchego” [Stop worrying and start studying. Megatrends: A look into dynamic portfolios of competencies of the future], Brics Business Magazine, 1, 2017, p. 58–61.
  10. PricewaterhouseCoopers”, 2017, Global Digital IQ® Survey. https://www.pwc.ch/en/publications/2017/global-digital-iq-survey-report-pwc.pdf
  11. S. Golubev, S.S. Chebotarev, A.M. Chibinev, R.M. Iusupov, Metodologiya nauchno-tekhnologicheskogo prognozirovaniya Rossiiskoi Federatsii v sovremennykh usloviyakh [A methodology for scientific-technological forecasting for the Russian Federation in present-day conditions], Moscow, Russia: Kretivnaya Ekonomika, 2018.
  12. International Federation of Robotics”, 2018, World Robotics 2018 Industrial Robots Executive Summary. https://ifr.org/downloads/press2018/Executive_Summary_WR_2018_Industrial_Robots.pdf
  13. Boiko, “Rynok sovremennoi robototekhniki v otsenkakh IFR” [IFR’s latest assessments for the robotics market], 2017.  http://robotrends.ru/pub/1716/rynok-sovremennoy-robototehniki-v-ocenkah-ifr---robotrends.ru  
  14. Carroll, “Global deployment of industrial robots to double by 2020”, 2018. https://www.vision-systems.com/articles/2018/05/global-deployment-of-industrial-robots-to-double-by-2020.html
  15. -H. Chang, P. Huynh, “ASEAN in transformation: The future of jobs at risk of automation”, Bureau for Employers’ Activities Working Paper, 9, 2016. International Labour Organization website. https://www.ilo.org/actemp/publications/WCMS_579554/lang--en/in dex.htm
  16. Keisner, J. Raffo, S. Wunsch-Vincent, “Robotics: Breakthrough technologies, innovation, intellectual property”, Foresight and STI Governance, 10(2), 2016, p. 7–27.
  17. Shamsi, “The relationship between knowledge management and managerial skills: The role of creative thinking”, Foresight and STI Governance, 11(4), 2017, p. 44–51.
  18. Roshchin, S. Solntsev, D. Vasilyev, “Recruiting and job search technologies in the Age of Internet” Foresight and STI Governance, 11(4), 2017, p. 33–43.
  19. B. Frey, M.A. Osborne, “The future of employment”: How susceptible are jobs to computerisation? 2013. https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf
  20. Goos, M., Manning, A., & Salomons, A., “Job polarization in Europe”, American Economic Review, 99(2), 2009, p. 58–63.
  21. Aghion, P. Howitt, “Growth and unemployment”, The Review of Economic Studies, 61(3), 1994, p. 477–494.
  22. Havas, D. Schartinger, M. Weber, “The impact of foresight on innovation policy-making”: Recent experiences and future  perspectives, Research Evaluation, 19(2), 2010, p. 91–104.
  23. S. Mahroum, B. Dachs, M. Weber, “Trend spotting the future of information society technology human resources”, International Journal of Foresight and Innovation Policy, 3(2), 2007, p. 169–186.

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76.

Authors:

Satrughan Kumar, Jigyendra Sen yadav, Kumar Manoj, S. Rajsekaran, Ranjeet Kumar

Paper Title:

Object localization and Tracking Using Background Subtraction and Dual-Tree Complex Wavelet Transform

Abstract: As seen, the object localization is coupled to many vision applications such as tracking, activity recognition region, security concern, etc. Therefore, segmenting the region of interest to assert the best detection of the target in the sequence of frames is the primary aim of this research. This paper presents an algorithm that detects and tracks the moving object in complex video sequence using the background subtraction and wavelet transform. The work proposes an adaptive background model based on clustering method for regularizing the objection extraction phase. Afterward, it computes the energy of the moving mask using the wavelet coefficient and updates the position of the object by matching this energy to that of moving mask corresponding to next frame. The work also com-pares qualitative and quantitative performance of the proposed method with other existing state-of-the-arts motion detection methods.

Keywords: Background subtraction, Wavelet transform, Object tracking, Fuzzy clustering. 

References:

  1. Mandellos, Nicholas A., Iphigenia Keramitsoglou, and Chris T. Kiranoudis. "A background subtraction algorithm for detecting and tracking vehicles." Expert Systems with Applications, Vol. 38, No.3, pp.1619-1631, 2011.
  2. Fu, Zhaoxia, and Yan Han. "Centroid weighted Kalman filter for visual object tracking." Measurement, Vol.45, No. 4, pp. 650-655, 2012.
  3. Lucas, B.D, Kanade, T. An iterative image registration technique with an application to stereo vision. In IJCAI, Proceedings of the 7th international joint conference on Artificial intelligence, 81, pp. 674-679,1981. Kumar, Satrughan, and Jigyendra Sen Yadav, "Video object extraction and its tracking using background subtraction in complex environments." Perspectives in Science, Vol.7, No.1, pp.317-322,2016.
  4. Oral, Mustafa, and Umut Deniz. "Centre of mass model–A novel approach to background modelling for segmentation of moving objects." Image and Vision Computing, Vol.25, No.8, pp.1365-1376, 2007 .
  5. Soeleman, MochArief, Mochamad Hariadi, and Mauridhi Hery Purnomo. "Adaptive threshold for background subtraction in moving object detection using Fuzzy C-Means clustering." In TENCON ,IEEE Region 10 Conference, pp.1-5,2012.
  6. Keivani, Arghavan, Jules-Raymond Tapamo, and FarzadGhayoor. "Motion-based moving object detection and tracking using automatic k-means." In AFRICON,  IEEE, pp.   32-37,2017.
  7. Khare, Manish, Tushar Patnaik, and Ashish Khare. "Dual tree complex wavelet transform based video object tracking." In International Conference on Advances in Information and Communication Technologies, Springer, (2010), pp. 281-286,2010.
  8. Kushwaha, Alok Kumar Singh, and Rajeev Srivastava. "Complex wavelet based moving object segmentation using approximate median filter based method for video surveillance." In Advance Computing Conference (IACC), IEEE, pp. 973-978.
  9. Stauffer, Chris, and W. Eric L. Grimson. "Learning patterns of activity using real-time tracking." IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp.747-757,
  10. Weng, Shiuh-Ku, Chung-Ming Kuo, and Shu-Kang Tu. "Video object tracking using adaptive Kalman filter." Journal of Visual Communication and Image Representation, vol.17, no. 6, pp. 1190-1208, 2006.
  11. Zhang, Chi, Jin Zheng, Yugui Zhang, Mengxiong Han, and Bo Li. "Moving object detection algorithm based on pixel spatial sample difference consensus." Multimedia Tools and Applications, vol.76, no. 21, pp.2077-2093, 2017.
  12. Liu, Yifan, Zhenjiang Cai, and Xuesong Suo. "A Multiframes Integration Object Detection Algorithm Based on Time-Domain and Space-Domain.", Mathematical Problems in Engineering, (2018), pp.1-15, 2018.
  13. Supreeth, H. S. G., and Chandrasekhar M. Patil. "Efficient multiple moving object detection and tracking using combined background subtraction and clustering." Signal, Image and Video Processing, Vol.12, No.9, pp.1097-1105,2018.
  14.   Elharrouss, Omar, Abdelghafour Abbad, Driss Moujahid, Jamal Riffi, and Hamid Tairi. "A block-based background model for moving object detection." Electronic Letters on Computer Vision and Image Analysis, Vol.15, No. 3, pp.17-31,2017.
  15. S Kumar and J S Yadav, “Segmentation of moving object using background subtraction method in complex environments”, Radioengineering, vol.25,no.2,pp.398-408, 2016.
  16. Satrughan Kumar and Jigyendra Sen Yadav, “An efficient motion detection method based on estimation of initial motion field using local variance, Advances in Intelligent Systems and Computing ,Springer ,vol. 381,pp.417-426,July 2015.

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77.

Authors:

K. Kamala Devi, Abdul Gafoor Shaik

Paper Title:

Detection of Islanding in Wind Farm Distributed Generation Of Distribution System Using Wavelet Based Alienation Technique.

Abstract: In the present paper, a wavelet transform based alienation technique has been presented for the detection of islanding condition in a distribution system incorporate with distributed generations. A radial five bus system integrated with four doubly fed induction wind generators (DFIG) has been considered for the study. The voltage signals at various DG buses were decomposed with Daubechies wavelet transform to get approximation coefficients. The alienation coefficients of these approximate decompositions were termed as Islanding indexes. These indexes were compared with a predetermined threshold to detect Islanding condition. The same threshold value was utilized to discriminate a transient associated with sudden changes with load. The suggested algorithm was established by a few case studies involving changes in incidence angle and load. Thus, the proposed algorithm is found to be successful for Islanding detection.

Keywords: Distributed generation, Alienation coefficients, Distribution system, Islanding condition, wavelet transform, and load change.

References:

  1. Ackermann, G. Andersson, and L. Soder, “Electricity Market Regulations and their Impact on Distributed Generatiron,” IEEE,   International Conference on Electric Utility Deregulation and Restructuring and Power Technologies,   2000.
  2. Ahmad Abd-Elkader, M. Saber, M. B. Saleh, Magdi Eiteba,             “A passive islanding  detection strategy for multi-distributed generations” Electrical power and Energy  systems, vol. 99, 2018, pp. 146-155.                
  3. Ali, B. Marzieh, S. B. Naderi, N. Michael, J. Amin, and B. Frede,  “Novel Islanding Detection Scheme for Synchron Distributed   Generation Using Rate of Change of Exciter Voltage over Reactive  Power at DG-Side ” Universities Power Engineering Conference     (AUPEC), Australasian, 2017.
  4. Aziah, S. Hussain, B. Erdal, K. Tamer, “A review of islanding detection techniques for renewable distributed generation system.” Renewable and Sustainable Energy Reviews. Vol. 28, 2013, pp. 483–493.
  5. Yu Chen, Xu Zhao, and O. Jacob, "Security assessment for intentional island operation in modern power system." Electric power systems research 81 (9), 2011, pp, 1849-1857.
  6. Funabashi, K. Koyanagi, and R. Yokoyama, “A review of islanding detection methods for distributed resources,” IEEE Bologna Power Tech Conference, Bologna, Italy, vol. 2, pp. 23-26, 2003.
  7. A. Hashemi, Kazemi, and S. Soleymani. "A new algorithm to detection of anti- islanding based on dqo transform." Energy Procedia, vol. 14, 2012, pp. 81-86.
  8. IEEE “Recommended Practice for Utility Interconnected Photovoltaic (PV) Systems”, IEEE Standard 929-2000, 2000.
  9. IEEE “Standard for Interconnecting Distributed Resources into Electric Power Systems”, IEEE Standard 1547TM, 2003.
  10. Vivek, and M. Hashem Nehrir. "A hybrid islanding detection technique using voltage unbalance and frequency set point." IEEE Transactions on Power Systems, vol. 22, 2007, pp. 442-448.
  11.  L. Merlin, R.C. Santos, A.P. Grilo, J.C.M. Vieira, D.V.Coury., M. Oleskovicz. “A new artificial neural network based method for islanding detection of distributed generators” International Journal of Electrical Power and Energy Systems, Vol. 75,  2016, pp.139-151.
  12. Mishra, M. Sahani, P.K. Rout, “An islanding detection algorithm for distributed generation based on Hilbert-Huang transform and extreme learning machine.” Sustainable Energy, Grids and Networks, vol. 9, 2017, pp. 13–26.
  13. N. Papadimitriou, V. A. Kleftakis, and N. D. Hatziargyriou.  "A novel islanding detection method for microgrids based on variable impedance insertion." Electric Power Systems Research, vol. 121, 2015,  pp. 58-66.
  14. Sirjani, C. F. Okwose. “Combining two techniques to develop a novel islanding detection method for distributed generation units.” Measurement, vol. 81, 2016, pp. 66–79.
  15. R. Samantaray, T. M. Pujhari, B.D. Subudhi. “A new approach to Islanding detection in Distributed Generations” Third International Conference on Power Systems, Kharagpur, India, 2009, pp 27-29.
  16. Haidar, F. Hashemi, and T. Ghanbari."Minimum non detection zone for islanding detection using an optimal Artificial Neural Network algorithm based on   PSO." Renewable and Sustainable Energy Reviews, vol. 52, 2015, pp. 1-18.
  17. Hamed, B. Vahidi, R. Ali Naghizadeh, and S. Hossein Hosseinian, "Islanding detection in unbalanced distribution systems with doubly fed induction generator based distributed generation using wavelet transform." Electric Power Components and  Systems, vol. 43, 2015, pp. 866-878.
  18. Shahryari, M. Nooshyar, and B. Sobhani. "Combination of neural network and wavelet transform for islanding detection of distributed generation in a small-scale network." International Journal of Ambient Energy, 2017, pp. 1-11.
  19. Shayeghi,  B. Sobhani, E. Shahryari, and A. Akbarimajd, "Optimal neuro-fuzzy based islanding detection method for Distributed Generation." Neurocomputing, vol. 177, 2016, pp. 478-488.
  20. Srdjan, F. Kristijan, and K. Ugarkovic, "Detection and Protection of Distributed Generation From Island Operation by Using PMUs." Energy Procedia, vol. 141, 2017, pp. 438-442.
  21. Usta and M. A. Refern, “Protection of Dispersed Storage and Generation Units Against Islanding” IEEE, 0–7803–1772–6/94, 1994
  22. J. Yin, L. Chang, and C. Diduch, “Recent development in islanding detection for distributed power generation,” Large engineering Systems Conference on Power Engineering (LESCOPE)",  2004,                  pp. 124-128.

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78.

Authors:

Deshmukh Yogiraj Ramakantrao, K. V. Narasimha Rao

Paper Title:

Investigation of Performance of Heat Transfer with the Help of Coiled Wire Inserts and Dimpled Tube

Abstract: Heat transfer enhancement is classified into active and passive methods. Active techniques require external power to type in the process; in comparison, passive methods don't require any extra power to enhance the thermo hydraulic overall performance of the product. Passive techniques are popular in each numerical and experimental uses when investigating heat transfer enhancement and friction losses to help save costs and power. The numerous passive ways for increasing heat transfer rate include different elements placed in the fluid flow path, like coiled wire, coiled and tangled cables, as well nozzle tabulator’s. The existing paper belongs to an extensive review which centered on heat transfer enhancement techniques with coiled wire plus coiled wire inserts because the assembly of inserts is simpler and much more efficient. The study work involves experimentation and numerical evaluation of dimpled tube built with frequently spaced coiled wire inserts. In this paper, the explanation of the experimental set in place and instrumentation used, together with the experimental data and process reduction is presented. The primary goal of the testing is obtaining experimental data pertaining to heat transfer and fluid flow. Unique trials had been carried out with mixtures of dimpled tubes and coiled wire with different Reynolds number for warm fluid and by holding regular flow of cool fluid through annulus.

Keywords: Heat, existing, presented, Reynolds, Unique ,comparison, 

References:

  1. García A et al. The influence of artificial roughness shape on heat transfer enhancement: Corrugated tubes dimpled tubes and coiled wires. Applied Thermal Engineering. 2012; 35:196-201. DOI: 10.1016/j.applthermaleng.2011.10.030
  2. Ozceyhan V. Conjugate heat transfer and thermal stress analysis of coiled wire inserted tubes that are heated externally with uniform heat flux. Energy Conversion and Management. 2005; 46:1543-1559. DOI: 10.1016/j.enconman.2004.08.003
  3. Gunes S, Ozceyhan V, Buyukalaca O. Heat transfer enhancement in a tube with equilateral triangle cross sectioned coiled wire inserts. Experimental Thermal and Fluid Science. 2010; 34:684-691. DOI: 10.1016/j.expthermflusci.2009.12.010
  4. Gunes S, Ozceyhan V, Buyukalaca O. The experimental investigation of heat transfer and pressure drop in a tube with coiled-wire inserts placed separately from the tube wall. Applied Thermal Engineering. 2010; 30:1719-1725. DOI: 10.1016/j.applthermaleng.2010.04.001
  5. Promvonge P. Thermal performance in circular tube fitted with coiled square wires. Energy Conversion and Management. 2008; 49:980-987. DOI: 10.1016/j.enconman.2007.10.005
  6. Eiamsa-ard S, Yongsiri K, Nanan K, Thianpong C. Heat transfer augmentation by helically coiled wires as swirl and turbulence promoters. Chemical Engineering and Processing. 2012; 60:42-48. DOI: 10.1016/j.cep.2012.06.001
  7. Eiamsa-ard S, Nivesrangsan P, Chokphoemphun S, Promvonge P. Influence of combined non-uniform coiled wire and coiled wire inserts on thermal performance characteristics. International Communication of Heat and Mass Transfer. 2010; 37:850-856. DOI: 10.1016/j. icheatmasstransfer.2010.05.012
  8. SyamSundar L, Bhramara P, Ravi Kumar NT, Singh MK, Sousa ACM. Experimental heat transfer, friction factor and effectiveness analysis of Fe3O4 nanofluid flow in a horizontal plain tube with return bend and coiled wire inserts. International Journal of Heat and Mass Transfer. 2017; 109:440-453. DOI: 10.1016/j.ijheatmasstransfer.2017.02.022
  9. Promvonge P. Thermal enhancement in a round tube with snail entry and coiled-wire inserts. International Communication of Heat and Mass Transfer. 2008; 35:623-629. DOI: 10.1016/j.icheatmasstransfer.2007.11.003
  10. Saha SK. Thermal and friction characteristics of laminar flow through rectangular and square ducts with transverse ribs and coiled wire inserts. Experimental Thermal and Fluid Science. 2010; 34:63-72. DOI: 10.1016/j.expthermflusci.2009.09.003
  11. Idario P. Nascimento et al (2017), Applied Thermal Engineering, Heat transfer performance enhancement in compact heat exchangers by using shallow square dimples in flat tubes, Elsevier.
  12. Chi-Chuan Wang et al,(2014), Applied Thermal Engineering, Investigation of the Semi-dimple Vortex Generator Applicable to Fin-and-Tube Heat Exchangers.
  13. Li, Min & C K Lai, Alvin. (2015). Review of analytical models for heat transfer by vertical ground heat exchangers (GHEs): A perspective of time and space scales. Applied Energy. 151. 178-191. 10.1016/j.apenergy.2015.04.070.
  14. Chen, H. Müller-Steinhagen, G.G. Duffy, “Heat transfer enhancement in dimpled tubes”, Applied Thermal Engineering 21 (2001) 535–547.
  15. G. Vicente, A. Garcıa, A. Viedma, “Heat transfer and pressure drop for low Reynolds turbulent flow in helically dimpled tubes”, International Journal of Heat and Mass Transfer 45 (2002) 543–55.
  16. Kumbhar, Dnyaneshwar Ganpatrao, (2017). “Experimental and numerical study of heat transfer enhancement of tube in tube type heat exchanger using dimpled tube and regularly spaced twisted tape”, Thesis, Bharati Vidyapeeth Deemed University.

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79.

Authors:

Samta Jain Goyal, Arvind Kumar Upadhyay, Rakesh Singh Jadon

Paper Title:

Facial Emotion Recognition Through Hand Gesture and Its Position Surrounding The Face

Abstract: For effective interaction with computers as human being, need to recognize and understand human emotions through the analyzing of the human ‘effective state, physiology and behavior. This paper is designed to focus on different types of emotion recognition study based on face and hand gesture. Human Computer Interaction (HCI) systems is designed for the machine to behave as normal as human beings. So, for the same objective, need to develop algorithm, technology which can used to track, detect, understand facula movements, hand gesture, position of hand surrounding the face to ensure about the human emotions in an effective way. Here we will try to design a basic framework for a vision-based multimodal analyzer. This analyzer is used to combines the feature of face and hand gesture to get better results than the existing ones.

Keywords: Facial Emotion Recognition, Hand Gesture Recognition, Human Computer Interaction, Multimodal Interface,

References:

  1. Shruti Bansal, Pravin Nagar, Emotion Recognition from Facial Expression Based on Bezier Curve, International Journal of Advanced Information Technology, volume5, number 3.
  2. Ioannou,. Amaryllis T. Raouzaiou, VasilisA.Tzouvaras, TheofilosP.Maili,. Kostas C. Karpouzis, StefanosD.Kollias, Emotion recognition through facial expression analysis based on a neurofuzzy network, Neural Networks, Volume 18, Issue 4, May 2005, Pages 423-435.
  3. Ekman, P., Friesen, W. V: Unmasking the Face: A Guide to Recognizing Emotions from Facial Clues. Prentice-Hall, New Jersey (1975)
  4. ArunaChakrabortyAmitKonar, Fuzzy Models for Facial Expression-Based Emotion Recognition and Control, Emotional Intelligence in elesviewerpp 133-173.
  5. Ludmila I Kuncheva and William J Faithfull, PCA Feature Extraction for Change Detection in Multidimensional Unlabeled Streaming Data, International Conference on Pattern Recognition (ICPR 2012), November 11-15, 2012.
  6. Jia-FengYu, Yue-Dong Yang, Xiao Sun, and Ji-Hua Wang, Sequence and Structure Analysis of Biological Molecules Based on Computational Methods, BioMed Research International, Volume 2015, 2015.
  7. Tallapragada, Rajan, Improved kernel-based IRIS recognition system in the framework of support vector machine and hidden markov model, IET Image Processing in IEEE, volume 6, 2012.
  8. Tallapragada, Rajan, Improved kernel-based IRIS recognition system in the framework of support vector machine and hidden markov model, IET Image Processing in IEEE, volume 6, 2012.
  9. Hai Nguyen, Katrin Franke, and Slobodan Petrovic, Optimizing a class of feature selection measures, Proceedings of the NIPS 2009 Workshop on Discrete Optimization in Machine Learning: Submodularity, Sparsity &Polyhedra (DISCML), Vancouver, Canada, December 2009.
  10. Hongjun Li, Ching Y. Suen, A novel Non-local means image denoising method based on grey theory, Journal Pattern Recognition in ACM, 2015.
  11. Ying-li Tian, Takeo Kanade, and Jeffrey F. Cohn, Recognizing Action Units for Facial Expression Analysis, IEEETransactions on Pattern Analysis and Machine Intelligence,23(2), 2001, 97-113.
  12. Tallapragada, Rajan, Improved kernel-based IRIS recognition system in the framework of support vector machine and hidden markov model, IET Image Processing in IEEE, volume 6, 2012.
  13. Surve Pranjali, Ubale V.S, “Hand gesture recognition system: A Survey”, in International journal of Inventive Engineering and Science (IJIES), ISSN:2319-9598, Volume-3, Issue-3, February 2015.
  14. K.S. Kinage and Samara Mutha, “Study on Hand Gesture Recognition”, in International Journal of Computer Science and Mobile Computing, 2015.
  15. Gu, J.; Wang, Z.; Kuen, J.; Ma, L.; Shahroudy, A.; Shuai, B.; Liu, T.; Wang, X.; Wang, L.; Wang, G.; et al. Recent advances in convolutional neural networks. Pattern Recognition. 2017, 1, 1–24.

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80.

Authors:

Ramalingam Senthil

Paper Title:

Thermal Performance of Aluminum Oxide based Nanofluids in Flat Plate Solar Collector

Abstract: A flat plate solar collector is investigated using nanofluid as heat transfer fluid in this work. Collector aperture area is 0.135 m2. Nanofluid is a mixture of alumina-oxide and distilled water. Size of the nanoparticle is 20 -30 nm size. Experiments carried out using distilled water and 5.0% of the mass of Al2O3 nanoparticles at single flow rates of 0.051 kg/s and different radiation intensity 600, 800, 1000 W/m2. Indoor experiments are conducted with the solar sun simulator. The promising results are obtained around 21% improved heat gain as well as around 20% reduction in the heat input required to provide the same heat requirements. Further, the addition of nanoparticles to the working fluid improves the productivity of the solar collectors with more heat gain.

Keywords: Flat plate collector, thermal efficiency, nanofluid, solar thermal collector.

References:

  1. Zoran T. Pavlovi, Ljiljana T. Kosti, Variation of reflected radiation from all reflectors of a flat plate solar Collector during a year, Energy 80 (2015) 75-84.
  2. Hiroshi Tanaka, Solar thermal collector augmented by flat plate booster reflector: Optimum inclination of collector and reflector, Applied Energy 88 (2011) 1395–1404.
  3. Sethi AK, Dwivedi VK, Exergy analysis of double slope active solar still under forced circular mode, Still and Water Treatment, 51, 2013, pp.7394–7400.
  4. Ali A. Badran, Ihmad A. Al-Hallaq, Imad A. Eyal Salman, Mohammad Z. Odat. A solar still augmented with a flat-plate collector. Desalination, 172 (3) (2005), pp. 227-234.
  5. M. Morad, HendA.M. EI-Maghawry, KamalI Wasfi. Improving the double slope solar still performance by using flat plate collector and cooling glass cover. Desalination, 373 (2015), pp. 1-9.
  6. Ljiljana T. Kosti´c, Zoran T. Pavlovi´c, Optimal position of flat plate reflectors of solar thermal collector, Energy and Buildings 45 (2012) 161–168.
  7. Senthil, R., Muthuveeran, M., Harish, S.M., Kumar, N.R., Experimental investigation on a pcm integrated concentrated solar receiver for hot water generation, International Journal of Mechanical Engineering and Technology, 8(9), 2017, 391-398.
  8. Mirzaei, M., S. M. S. Hosseini, and A. M. Moradi Kashkooli. Assessment of Al2O3 Nanoparticles for the Optimal Operation of the Flat Plate Solar Collector. Applied Thermal Engineering, 134, 2018, pp. 68-77.

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81.

Authors:

M. Premkumar, R. Vijaya Krishna, R. Mohan Kumar, R. Sowmya

Paper Title:

Protection to Grid Tied Converters and Power Quality Control using Active Shunt Filter

Abstract: This paper deals with the study of power quality issue and, simulation and implementation of a power electronic converter system to improve the power quality and protection to the converters. The present work is simulated for power system employing Active Shunt Filter (ASF) and Thyristor Switched Capacitor (TSC). The behavior of the power system for a designed controller with and without fault is simulated, and the results are presented in this paper. The experimental prototype for the overall system is implemented with the help of the solid-state circuit breaker (SSCB). The simulation is done with the MATLAB/Simulink, and the simulation results are verified with the prototype results, and finally, results are compared and presented for future work.

Keywords: ASF, Fault location, Power electronic converters, Power quality, TSC. 

References:

  1. Chowdhury, SP. Chowdhury, and P. Crossley, “Microgrids and active distribution networks”, IET Renew. Ene. Series, vol. 6, pp. 230-255, 2009.
  2. Premkumar, and TR. Sumithira, “Design and implementation of new topology for solar PV based transformerless forward microinverter”, J. Electri. and Engg. and Tech., Early Access, 2019. DOI: 10.1007/s42835-018-00036-2.
  3. Abdel-Rady, I. Mohamed, and Ehab F. ElSaadany, “A control scheme for PWM voltage-source distributed-generation inverters for fast load-voltage regulation and effective mitigation of unbalanced voltage disturbances”, IEEE Trans. on Indus. Electro., vol.55, no. 5, pp. 2072-2084, 2008.
  4. Wei Li, and J. Wei He, “Distribution system harmonic compensation methods”, IEEE Indus. Electro. Maga., vol. 8, no. 4, pp. 18-31, 2014.
  5. Nikkhajoei, and RH. Lasseter, “Distributed generation interface to the CERTS microgrid”, IEEE Trans. on Power Del., vol. 24, no. 3, pp. 1598-1608, 2009.
  6. Asiminoaei, R. Teodorescu, F. Blaabjerg, and U. Borup, “A digital controlled PV-inverter with grid impedance estimation for ENS detection,” IEEE Trans. on Power Electro., vol. 20, no. 6, pp. 1480-1490, 2005.
  7. Diana, M. Sumner, and P. Zanchetta, “Non-invasive power system impedance monitoring for improved power quality,” In: Proc. of 2nd IEEE Conf. on Power Electro., Mach. and Dri., Edinburgh, pp. 265-268, April 2004.
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  9. Bacha, D. Picault, and B. Burger, I. Etxeberria-Otadui, J. Martins, “Photovoltaics in microgrids : an overview of grid integration and energy management aspects”, IEEE Indus. Electro. Maga., vol.9, no. 1, pp. 33-46. 2015.
  10. Sumner, A. Abusorrah, and D. Thomas, “Real-time parameter estimation for power quality control and intelligent protection of grid-connected power electronic converters,” IEEE Trans. on Smart Grid, vol.5, no.4, pp.1602-1607, 2014.
  11. Palizban, and K. Kauhaniemi, “Hierarchical control structure in microgrids with distributed generation: Island and grid-connected mode”, Renew. and Susta. Ener. Rev., vol. 44, pp. 797-813, 2015.
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  13. Blaabjerg, R. Teodorescu, M. Liserre, and AV. Timbus, “Overview of control and grid synchronization for distributed power generation systems”, IEEE Trans. on Indus. Electro., vol. 53, no. 5, pp. 1398-1409, 2006.
  14. Cornforth, T. Moore, and S. Sayeef, “Challenges and opportunities for inverters in microgrids”, In: Proc. of the 37th Annual Conference of IEEE Indus. Electro. Soc., Australia, pp. 3111-3116, 2011.
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  16. Ai-jun, S. Fei., and C. Wen-jin, “Zero-cross triggering technology of series SCRs with the optical fibre at medium voltage: Application for thyristor switched capacitor,” In: Proc. of the IEEE/PES Trans. and Dist. Conf. and Exhi: Asia and Pacific, Dalian, China, pp. 1-5, August 2005.
  17. Asiminoaei, R. Teodorescu, F. Blaabjerg, and U. Borup, “Implementation and test of an online embedded grid impedance estimation technique for PV inverters,” IEEE Trans. on Indus. Electro., vol. 52, no. 4, pp. 1136-1144, 2005.
  18. Sumner, B. Palethorpe, and DWP. Thomas, “Impedance measurement for improved power quality - part 2: A new technique for standalone active shunt filter control,” IEEE Trans. on Power Deli., vol.19, no.3, pp. 1457-1463, 2004.
  19. Ren and M. Kezunovic, “Real-time power system frequency and phasors estimation using recursive wavelet transform,” IEEE Trans. on Power Deli., vol.26, no.3, pp. 1392-1402, 2011.
  20. Tsukamoto, S. Ogawa, Y. Natsuda, Y. Minowa and S. Nishimura, “Advanced technology to identify harmonics characteristics and results of measuring,” In Proc. of 9th IEEE Conf. on Harm. and Quality of Power, Orlando, pp. 341-346, October 2000.
  21. Sumner, D. Thomas, A. Abusorrah, L. Yao, R. Parashar, and M. Bazargan, “Intelligent protection for embedded generation using active impedance estimation”, In Proc. of 2nd IEEE Intern. Symp. on Power Electro. for Distri. Gen.. Sys., China, pp. 47-52, June 2010.
  22. Khan, F. Baig, S. Junaid Nawaz, N. Ur Rehman and SK. Sharma, “Analysis of power quality signals using an adaptive time-frequency distribution,” Energies, vol. 933, pp. 1-13, November 2016.
  23. Lavanya, and N. Senthil Kumar, “A review: control strategies for power quality improvement in microgrid”, Inter. J. of Renew. Ener. Rese., vol.8, no.1, March 2018.
  24. Ghanem, M. Rashed, M. Sumner, MA. El-sayes and III. Mansy, "Grid impedance estimation for islanding detection and adaptive control of converters,” In Proc. of 8th IET Inter. Conf. on Power Electro., Mach. and Dri., Glasgow, 2016, pp. 1-6.

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82.

Authors:

Abdul Aabid, Ambareen khan, Nurul Musfirah. Mazlan, Mohd Azmi. Ismail, Mohammad Nishat Akhtar, S. A. Khan.

Paper Title:

Numerical Simulation of Suddenly Expanded Flow at Mach 2.2.

Abstract: A numerical simulation has been performed to investigate the control of base pressure with microjets in a suddenly expanded duct. Microjets placed at the pitch circle diameter (PCD) of 13 mm, two micro jets of 1 mm orifice diameter located at 900 for active control. The flow Mach number of the investigation was M = 2.2, the L/D ratio of the enlarged duct considered is 6, and the area ratio is 3.24. The convergent-divergent (CD) nozzle geometry has been modeled and simulated employing K-ε turbulence model for standard wall function. From the code independently was checked with the commercial computational fluid dynamics software. The numerical simulations carried for nozzle pressure ratio’s (NPR) 3, 5, 7, 9 and 11. From the present numerical investigation, it is observed that the NPR, Mach number, and area ratio plays a vital role in fixing the base pressure values. NPR's of the present study is such that the flow mostly remained over expanded. Despite jets being over-expanded the control is effective in decreasing the base suction and hence the base drag.

Keywords: M = 2.2, 3.24. 3, 5, 7, 9 and 11. NPR, Mach number.

References:

  1. A. Khan and A. Aabid, “CFD Analysis of CD Nozzle and Effect of Nozzle Pressure Ratio on Pressure and Velocity For Suddenly Expanded Flows,” International Journal of Mechanical and Production Engineering Research and Development, vol. 8, no. June, pp. 1147–1158, 2018.
  2. Khan, A. Aabid, and S. A. Khan, “CFD analysis of convergent-divergent nozzle flow and base pressure control using micro-JETS,” International Journal of Engineering and Technology, vol. 7, no. 3.29, pp. 232–235, 2018.
  3. G. M. Fharukh, A. A. Alrobaian, A. Aabid, and S. A. Khan, “Numerical Analysis of Convergent-Divergent Nozzle Using Finite Element Method,” International Journal of Mechanical and Production Engineering Research and Development, vol. 8, no. 6, pp. 373–382, 2018.
  4. Sakaki and Y. Shimizu, “Effect of the Increase in the Entrance Convergent Section Length of the Gun Nozzle on the High-Velocity Oxygen Fuel and Cold Spray Process,” Journal of Thermal Spray Technology, vol. 10, no. 3, pp. 487–496, 2001.
  5. A. Khan and E. Rathakrishnan, “Active Control of Suddenly Expanded Flows from Overexpanded Nozzles,” International Journal of Turbo and Jet Engines, vol. 19, pp. 119–126, 2002.
  6. A. Khan and E. Rathakrishnan, “Control of Suddenly Expanded Flow with Micro-Jets,” International Journal of Turbo and Jet Engines, vol. 20, pp. 63–82, 2003.
  7. A. Khan and E. Rathakrishnan, “Control of Suddenly Expanded Flows from Correctly Expanded Nozzles,” International Journal of Turbo and Jet Engines, vol. 21, pp. 255–278, 2004.
  8. A. Khan and E. Rathakrishnan, “Active Control of Suddenly Expanded Flows from Underexpanded Nozzles,” International Journal of Turbo and Jet Engines, vol. 21, pp. 233–254, 2004.
  9. A. Khan and E. Rathakrishnan, “Active Control of Suddenly Expanded Flows from Underexpanded Nozzles - Part II,” International Journal of Turbo and Jet Engines, vol. 22, pp. 163–183, 2005.
  10. A. Khan and E. Rathakrishnan, “Control of suddenly expanded flow,” Aircraft Engineering and Aerospace Technology: An International Journal, vol. 78, no. 4, pp. 293–309, 2006.
  11. A. Khan and E. Rathakrishnan, “Nozzle Expansion Level Effect on Suddenly Expanded Flow Sher,” International Journal of Turbo and Jet Engines, vol. 23, pp. 233–257, 2006.
  12. Rehman and S. A. Khan, “Control of base pressure with micro‐jets : part I,” Aircraft Engineering and Aerospace Technology, vol. 80, no. 2, pp. 158–164, 2008.
  13. Ali, A. Neely, J. Young, B. Blake, and J. Y. Lim, “Numerical Simulation of Fluidic Modulation of Nozzle Thrust,” in 17th Australasian Fluid Mechanics Conference, 2010, no. December, pp. 5–8.
  14. A. A. Baig, S. A. Khan, C. Ahmed Saleel, and E. Rathakrishnan, “Control of base flows with microjet for an area ratio of 6.25,” ARPN Journal of Engineering and Applied Sciences, vol. 7, no. 8, pp. 992–1002, 2012.
  15. M. Kumar, D. X. Fernando, and R. M. Kumar, “Design and Optimization of De Laval Nozzle to Prevent Shock-Induced Flow Separation,” vol. 3, no. 2, pp. 119–124, 2013.
  16. Cai, Z. Liu, Z. Shi, Q. Song, and Y. Wan, “Residual surface topology modeling and simulation analysis for the micro-machined nozzle,” International Journal of Precision Engineering and Manufacturing, vol. 16, no. 1, pp. 157–162, 2015.
  17. Kostic, Z. Stefanovic, and I. Kostic, “CFD modeling of supersonic airflow generated by a 2D nozzle with and without an obstacle at the exit section,” FME Transaction, vol. 43, no. 2, pp. 107–113, 2015.
  18. J. Shariatzadeh, A. Abrishamkar, and A. J. Jafari, “Computational Modeling of a Typical Supersonic Converging-Diverging Nozzle and Validation by Real Measured Data,” Journal of Clean Energy Technologies, vol. 3, no. 3, pp. 220–225, 2015.
  19. -A. Belega and T. D. Nguyen, “ANALYSIS OF FLOW IN CONVERGENT-DIVERGENT ROCKET ENGINE NOZZLE USING COMPUTATIONAL FLUID DYNAMICS,” 2015.
  20. D. Majil, A. Poojitha, and G. Devi, “Computational Study on Optimization of Rocket Nozzle,” 2016, pp. 8–13.
  21. Zhang and H. D. Kim, “Theoretical and numerical analysis on choked multiphase flows of gas and solid particle through a convergent-divergent nozzle,” The journal of computational multiphase flow, vol. 0, no. 0, pp. 1–14, 2017.
  22. R. Pilon, R. W. Powers, D. K. McLaughlin, and P. J. Morris, “Design and Analysis of a Supersonic Jet Noise Reduction Concept,” Journal of Aircraft, vol. 54, no. 5, pp. 1705–1717, 2017.
  23. Sun, X. Cao, W. Yang, and X. Zhao, “CFD modeling on non-equilibrium condensation process of H2S in CH4-H2S mixture expansion through supersonic nozzles,” Fuel Processing Technology, vol. 170, no. November 2017, pp. 53–63, 2018.
  24. A. Pathan, P. S. Dabeer, and S. A. Khan, “Optimization of Area Ratio and Thrust in Suddenly Expanded Flow at Supersonic Mach Numbers,” Case Studies in Thermal Engineering, 2018. ANSYS Inc, “ANSYS FLUENT 18.0: Theory Guidance,” Canonsburg PA, 2017.

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83.

Authors:

Geetanjali Udgirkar, G. Indumathi.

Paper Title:

Consideration of Net Weights for Performance Driven Routing.

Abstract: Objectives: In todays’ VLSI technology, interconnect delay is the predominant factor in determining the speed of the final chip. Considering the complexity and size of today’s VLSI designs, timing driven VLSI routing is very challenging problem. Methods/Statistical analysis: The obvious method is to assign weights to the nets of a given route and perform timing driven routing. There are few works in the literature on net-weighting-based timing driven routing. Findings: Based on the criticality of the nets, by assigning weights to the nets in two methods discussed in the paper, we present two novel timing driven routing algorithms. In the first method, a constant is raised to the power of a variable exponent for weight assignment, whereas, in the second method, a variable exponent is raised to the power of a constant. These weights are considered during timing driven VLSI routing for an FPGA using VPR routing tool. Improvements: The proposed methods show significant improvement in timing over VPR routing tool. We obtain improvement of 14.65% and 26.85% using the methods MethodA and MethodB respectively, over VPR.

Keywords: VLSI Routing, Global Routing, Net Weighting Method.

References:

  1. Tu, W. Chow and E. F. Y. Young, "Timing driven routing tree construction," 2017 ACM/IEEE International Workshop on System Level Interconnect Prediction (SLIP), Austin, TX, 2017, pp. 1-8.
  2. Dongsheng Wang and E. S. Kuh, "A new general connectivity model and its applications to timing-driven Steiner tree routing," 1998 IEEE International Conference on Electronics, Circuits and Systems. Surfing the Waves of Science and Technology (Cat. No.98EX196), Lisboa, Portugal, 1998, pp. 71-74 vol.2.
  3. Dongsheng Wang and E. S. Kuh, "A new timing-driven multilayer MCM/IC routing algorithm," Proceedings 1997 IEEE Multi-Chip Module Conference, Santa Cruz, CA, USA, 1997, pp. 89-94.
  4. Jin-Tai Yan, Chia-Fang Lee and Yen-Hsiang Chen, "A simulated-annealing-based approach for timing-constrained flexibility-driven routing tree construction," The 2004 47th Midwest Symposium on Circuits and Systems, 2004. MWSCAS '04., Hiroshima, Japan, 2004, pp. I-461.
  5. Tsung-Yi Ho, Yao-Wen Chang, Sao-Jie Chen and Der-Tsai Lee, "Crosstalk- and performance-driven multilevel full-chip routing," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 24, no. 6, pp. 869-878, June 2005.
  6. Fang-Jou Liu, J. Lillis and Chung-Kuan Cheng, "Design and implementation of a global router based on a new layout-driven timing model with three poles," Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS '97,
  7. Jiang Hu and S. S. Sapatnekar, "Performance driven global routing through gradual refinement," Proceedings 2001 IEEE International Conference on Computer Design: VLSI in Computers and Processors. ICCD 2001, Austin, TX, USA, 2001, pp. 481-483.
  8. Roy, "Power-dissipation driven FPGA place and route under timing constraints," in IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, vol. 46, no. 5, pp. 634-637, May 1999.
  9. Monteiro et al., "Routing-Aware Incremental Timing-Driven Placement," 2016 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), Pittsburgh, PA, 2016, pp. 290-295.
  10. Hsiao-Ping Tseng, L. Scheffer and C. Sechen, "Timing- and crosstalk-driven area routing," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 20, no. 4, pp. 528-544, April 2001.
  11. Deguchi, T. Koide and S. Wakabayashi, "Timing-driven hierarchical global routing with wire-sizing and buffer-insertion for VLSI with multi-routing-layer," Proceedings 2000. Design Automation Conference. (IEEE Cat. No.00CH37106), Yokohama, 2000, pp. 99-104.
  1. Sung-Woo Hur, A. Jagannathan and J. Lillis, "Timing-driven maze routing," in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 19, no. 2, pp. 234-241, Feb. 2000.
  2. Jin-Tai Yan, Shi-Qin Huang and Zhi-Wei Chen, "Top-down-based timing-driven steiner tree construction with wire sizing and buffer insertion," TENCON 2007 - 2007 IEEE Region 10 Conference, Taipei, 2007, pp. 1-4.
  3. 14.C. Alexandre, H. Clement, J. -. Chaput, M. Sroka, C. Masson and R. Escassut, "TSUNAMI: an integrated timing-driven place and route research platform," Design, Automation and Test in Europe, Munich, Germany, 2005, pp. 920-921 Vol.

463-467

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84.

Authors:

Vasugi. K, S. Elavenil.

Paper Title:

Confinement of Concrete by Stainless Steel Tubular Sections –A Review.

Abstract: Concrete infilled steel tubular section (CFST) is a composite column used for modern construction. CFST column system is the most successful system, and the confinement of concrete by steel tubular sections enhances in core’s strength. In recent scenario, stainless steel has been popular and widely used in many modern structural applications owing to their anti-corrosion resistance, durability, good in appearance and easy in maintenance. Stainless steel is quite expensive. To reduce the cost significantly stainless steel tube filled with concrete that is, concrete filled steel tubular (CFSST) is used and it is referred as composite material system. This composite material system has a wide application in column made up of concrete, to provide a better performance in terms of strength, stiffness, ductility and seismic resistance. Compare to all other composite material system. This paper discuss about the different types of stainless steel tubular sections, shapes used for stub column under axial loading, strength enhancement and the different modes of failures both experimentally & analytically of stub column.

Keywords: Stainless steel, stub column, shapes, coupon test, axial load, compressive strength.

References:

  1. Lui., M. Ashraf and B.Young, “Tests of cold formed-formed duplex stainless steel SHS beam-columns”, Engineering. Structures, . 2014, Vol. 74, pp. 111-121.
  1. Serkan Tokgoz, “Test on plain and steel fiber concrete -filled stainless steel”, of Construction Steel Research, 2015, Vol. 114, pp.129-135.
  2. Yuner Huang., Ben Young, “Structural performance of cold-formed lean duplex stainless steel columns”, 2014, Thin-walled Structures, Vol. 83, pp. 59-69.
  3. Ben Young, “Experimental and numerical investigation of high strength stainless steel structures”, of Construction Steel Research ,2008, Vol.44, pp 1225-1230.
  4. American Society of Civil Engineers, ASCE, Specification for the design of cold- formed stainless steel structural members.2002, SEI/ASCE-8-02-Reston (Virginia).
  5. Australian / New Zealand Standard AS/NZS. 2001. Cold formed stainless steel structure. Standards Australia;2001. Sydney (Australia) AS/NZS 4673:2001.
  6. European Committee for standardization CEN, Brussels, EC3. Eurocode 3: Design steel structures-Part 1.4; General rules-Supplementary rules for stainless steel, 1996, Env 1993-1-4.
  7. Xu-Chang., Ahong Liang Ru., Wei Zhou., Yong-Bin Zhang, “Study on concrete-filled stainless steel-carbon steel tubular (CFST) stub columns under compression”, Thin-walled Structures, 2013, Vol. 63, pp.125-133.
  8. Baddoo NR, “Stainless steel in construction : a review of research applications, challenges and opportunity”, of Construction Steel Research ,2008, Vol.64, Issues 11, pp. 1199-1206.
  9. Lin-Hai Han., Wei Li., Reidar Bjorhovde, “Developments and advanced applications of concrete-filled steel tubular (CFST) structures: members”, of Construction Steel Research ,2008, Vol.100, pp. 211-228.
  10. You-Fu Yang., Guo-Liang Ma , “Experimental behaviour of recycled aggregate concrete filled stainless steel tube stub columns and beams”, Thin-Walled Structures, 2013, Vol. 66, pp.62-75.
  11. L.Li., X.L.Zhao., R.K.Raman Singh , ” Experimental study on seawater and sea sand concrete filled GFRP and stainless steel tubular stub columns”, Thin walled Structures, 2016, Vol. 106, pp. 390-406.
  12. Nameer A. Alwash., Hayder I., AL.Salih, “ Experimental Investigation on behavior of SCC filled steel tubular stub columns strengthened with CFRP”, Engineering, 2013, Vol.I Issue 2.
  13. Ehab Ellobody., Mariam F. Ghazy, “Experimental investigation of eccentrically loaded fibre reinforcement concrete-filled stainless steel tubular columns”, Journal. of Construction Steel Research, 2012, Vol. 76, pp. 167-176.
  14. Yiyan Lu., Shan Li, “Behavior of FRP-Confined Concrete-Filled Steel Tube Columns”, Polymers.2014,Vol. 6, pp.1333-1349.

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85.

Authors:

Nafees Akhter Farooqui, Ritika, Aayush Saini.

Paper Title:

Sentiment Analysis of Twitter Accounts using Natural Language Processing.

Abstract: Every hour heaps of data are generated by blogs, social websites, and web pages. Many business houses gather all of this data to understand consumers, marketing strategies and their desires better and make appropriate changes to reshape the way businesses work. To extract information from this content, we need to rely on natural language processing (NLP) techniques. Many organizations want to get an overview of any policy or any product launched in the market. The overview of human sentiment can be calculated by using natural language processing through Python as it is a strong and easy language which is spreading across the globe covering its track in every sphere of modern technology.

Keywords: Modern Technology, Natural Language Processing, Python, Sentiments.

References:

  1. Jagdale, O.; Harmalkar, V.; Chavan, S.; Sharma, N. Twitter mining using R. Int. J. Eng. Res. Adv. Tech. 2017, 3,252–256.
  2. Anjaria, M.; Guddeti, R.M.R. Influence factor-based opinion mining of Twitter data using supervised learning. In Proceedings of the 2014 Sixth International Conference on Communication Systems and Networks, Bangalore, India, 6–10 January 2014; pp.1–8.
  3. Miranda Filho, R.; Almeida, J.M.; Pappa, G.L. Twitter population sample bias and its impact on predictive outcomes: A case study on elections. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Paris, France, 25–28 August 2015; pp. 1254–1261.
  4. Castro, R.; Kuffó, L.; Vaca, C. Back to# 6d: Predicting venezuelan states political election results through twitter. In Proceedings of the 2017 Fourth International Conference on eDemocracy & eGovernment (ICEDEG), Quito, Ecuador, 19–21 April 2017; pp. 148–153.
  5. Kanavos, A.; Nodarakis, N.; Sioutas, S.; Tsakalidis, A.; Tsolis, D.; Tzimas, G. Large scale implementations for twitter sentiment classification. Algorithms 2017, 10, 33. [CrossRef]
  6. Kanavos, A.; Perikos, I.; Hatzilygeroudis, I.; Tsakalidis, A. Emotional community detection in social networks. Comput. Electr. Eng. 2017, 65, 449–460. [CrossRef].
  7. Jose, R.; Chooralil, V.S. Prediction of election result by enhanced sentiment analysis on twitter data using word sense disambiguation. In Proceedings of the 2015 International Conference on Control Communication & Computing India (ICCC), Trivandrum, India, 19–21 November 2015; pp. 638–641.
  8. Twitter Apps. Available online: http://www.tweepy.org/ (accessed on 26 February 2018).
  9. Esuli, A.; Sebastiani, F. Sentiwordnet: A High-Coverage Lexical Resource for Opinion Mining; Institute of Information Science and Technologies (ISTI) of the Italian National Research Council (CNR): Pisa, Italy, 2006.
  10. Miller, G.A.Wordnet: A lexical database for English. Commun. ACM 1995, 38, 39–41. [CrossRef].
  11. Dokoohaki, N.; Zikou, F.; Gillblad, D.; Matskin, M. Predicting Swedish elections with Twitter: A case for stochastic link structure analysis. In Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Paris, France, 25–28 August 2015; pp. 1269–1276.
  12. Pang, B., and Lee, L. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2):1–135.
  13. Tumasjan, A.; Sprenger, T. O.; Sandner, P.; and Welpe, I. 2010. Predicting elections with Twitter: What 140 characters reveal about political sentiment. In Proceedings of ICWSM.
  14. O’Connor, B.; Balasubramanyan, R.; Routledge, B.; and Smith, N. 2010. From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of ICWSM.
  15. Barbosa, L., and Feng, J. 2010. Robust sentiment detection on twitter from biased and noisy data. In Proc. of Coling.
  16. Bifet, A., and Frank, E. 2010. Sentiment knowledge discovery in Twitter streaming data. In Proc. of 13th International Conference on Discovery Science.
  17. Hassan, A. Abbasi, and D. Zeng, “Twitter sentiment analysis: A bootstrap ensemble framework,” in Social Computing (SocialCom), 2013 International Conference on. IEEE, 2013, pp. 357–364.
  18. Coletta, N. F. F. d. Sommaggio Silva, E. R. Hruschka, and E. R. Hruschka, “Combining classification and clustering for tweet sentiment analysis,” in Intelligent Systems, 2014 Brazilian Conference on. IEEE, 2014, pp. 210–215.
  19. Kouloumpis, T. Wilson, and J. Moore, “Twitter sentiment analysis: The good the bad and the omg!” ICWSM, vol. 11, pp. 538–541, 2011.
  20. T. Ngoc and M. Yoo, “The lexicon-based sentiment analysis for fan page ranking in Facebook,” in Information Networking (ICOIN), 2014 International Conference on. IEEE, 2014, pp. 444–448.
  21. Minanovic, H. Gabelica, and Z. Krstic, “Big data and sentiment analysis using knime: Online reviews vs. social media,” in Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on. IEEE, 2014, pp. 1464–1468.
  22. Porshnev, I. Redkin, and A. Shevchenko, “Machine learning in prediction of stock market indicators based on historical data and data from Twitter sentiment analysis,” in Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on. IEEE, 2013, pp. 440–444.
  23. Troussas, M. Virvou, K. J. Espinosa, K. Llaguno, and J. Caro, “Sentiment analysis of Facebook statuses using naive bayes classifier for language learning,” in Information, Intelligence, Systems and Applications (IISA), 2013 Fourth International Conference on. IEEE, 2013, pp. 1–6.
  24. Twitter Apps. Available online: http://www.tweepy.org/ (accessed on 28 February 2018).
  25. Roesslein, J. Tweepy Documentation. 2009. Available online: http://docs.tweepy.org/en/v3.5.0/ (accessed on 26 February 2018).
  26. Available online: https://textblob.readthedocs.org/en/dev/ (accessed on 26 February 2018.

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86.

Authors:

Fazeel Ahmed Khan, Adamu Abubakar, Marwan Mahmoud, Mahmoud Ahmad Al-Khasawneh, Ala Abdulsalam Alarood.

Paper Title:

Cotton Crop Cultivation Oriented Semantic Framework Based on IoT Smart Farming Application.

Abstract: The fact that each technological concept comes from the advances in the research and development, Internet of Things (IoT) grows and touches virtually every area of human activities. This has yielded the possibility of analyzing various types of sensors-environment from any kind of IoT platform. The existing IoT platforms focuses more on the area related to urban infrastructure, smart cities, healthcare, smart industry, smart mobility and much more. In this paper, we are focusing on the architecture of designing the application of IoT based solution in agriculture with more specific to Cotton farming. Our specific approach on farming is relevant to cotton crops cultivation, irrigation and harvesting of yields. In the context of cotton crops cultivation, there are many factors that should be concerned which includes weather, legal regulation, market conditions and resource availability. As a result, this paper presents a cotton crops cultivation oriented semantic framework based on IoT smart farming application which supports smart reasoning over multiple heterogenous data streams associated with the sensors providing a comprehensive semantic pipeline. This framework will support large scale data analytic solution, rapid event recognition, seamless interoperability, operations, sensors and other relevant features covering online web based semantic ontological solution in an agriculture context.

Keywords: Internet of Things (IoT); Smart Agriculture; Smart Farming; Remote Sensing; Precision Agriculture.

References:

  1. Sidney Cox. Information technology: the goal key to presision agriuclture and sustainability. Computers and Electronics in Agriculture, 36: 93–111, (2002).
  2. Paolo Tripicchio, Massimo Satler, Giacomo Dabisias, Emanuele Ruffaldi, Carlo A. Avizzano. Towards Smart Farming and Sustainable Agriculture with Drones. International Conference on Intelligent Environments, (2015).
  3. Sohail Jabbar, Farhan Ullah, Shehzad Khalid, Murad Khan, Kijun Han. Semantic Interoperability in Heterogenous IoT Infrastructure for Healthcare. Wireless Communication and Mobile Computing, (2017).
  4. Ala Al-Fuqaha, Mohsen Guizani, Mehdi Mohammadi, Mohammed Aledhari, Moussa Ayyash. Internet of Things: A Survey on Enabling Technologies, Protocols and Applications. IEEE Communication Surveys & Tutorials, 17(4), (2015).
  5. Emanuele Pierpaoli, Giacomo Carli, Erika Pignatti, Maurizio Canavari. Drivers of Precision Agriculture Technologies Adoption: A Literature Review. International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2013).
  6. Abdul Khaliq, M. Kaleem Abbasi, Tahir Hussain. Effects of integrated use of organic and inorganic nutrient sources with effective microrganism (EM) on seed cotton yield in Pakistan. Bioresource Technology, 97: 967–972, (2006).
  7. Cotton: An importanct cash crop. http://www.pakistaneconomist.com/issue2000/issue18/i&e2.htm
  8. Yinghui Huang, Guanyu Li. A Semantic Analysis for Internet of Things. International Conference on Intelligent Computation Technology and Automation, (2010).
  9. Duan Yan-e. Design of Intelligent Agriculture Management Information System Based on IoT. Fouth International Conference on Intelligent Computation Technology and Automation. (2011).
  10. Kerry Taylor, Colin Griffith, Laurent Lefort, Raj Gaire, Michael Compton, David Lamb, Greg Falzon, Mark Trotter. Farming the Web of Things. IEEE Intelligent System, 28(6):12–19, (2013).
  11. Freddy Lècueè, Simone Tallevi-Diotallevi, Jer Hayes, Robert Tucker, Veli Bicer, Marco Sbodio, Pierpaolo Tommasi. Smart Traffic Analytic in the Semantic Web with STAR CITY: Scenarios, System and Lessons Learned in Dublin City. Web Semantic: Science, Services and Agents on the World Wide Web. 27: 26–33, (2014).
  12. CityPulse EU FP7 Project, 2016. http://www.ict-citypulse.eu/page/.
  13. OpenIoT EU FP7 Project, 2016. https://github.com/OpenIotOrg.
  14. IoT-A EU FP7 Project, 2016. http://www.iot-a.eu/public.
  15. The FIWARE Catalogue, 2016. http://catalogue.fiware.org/.
  16. Andreas Kamilaris, Yiannis Tofis, Chakib Bekara, Andreas Pitsillides, Elias Kyriakides. Integrating Web-Enabled Energy-Aware Smart Homes to the Smart Grid. International Journal on Advances in Intelligent Systems, 5(1), (2012).
  17. Michael Compton et al. The SSN ontology of the W3C semantic sensor network in incubator group. Web Semantic: Science, Services and Agents on the World Wide Web. 17: 25–32, (2012).
  18. OWL-S Semantic Markup for Web Services. https://www.w3.org/Submission/2004/SUBM-OWL-S-20041122/.
  19. Stream Annotation Ontology. http://iot.ee.surrey.ac.uk/citypulse/ontologies/sao/sao.
  20. Dan Puiu et al. CityPulse: Large Scale Data Analytics Framework for Smart Cities. IEEE Access, (2016).
  21. AGROVOC Thesaurus. http://www.taxobank.org/content/agrovoc-thesaurus.
  22. Boris Lauser et al. From AGROVOC to the Agriculture Ontology Service/ Concept Server. An OWL model for creating ontologies in agriculture domain. International Conference on Dublin Core and Metadata Applications. (2006).

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87.

Authors:

M. Sathasivam, S. Shanmugapriya, V. Yogeshwaran, A. K.Priya.

Paper Title:

Industrial Waste Water Treatment Using Advanced Oxidation Process – A Review.

Abstract: Management of Industrial wastewater is one of the complicated problems in this world. Many technologies and methods are implemented for the industrial wastewater treatment and got the maximum efficiency of removal using various chemicals and materials. But, the management of sludge from various treatment technologies is comparatively low. Various methods such as physio-chemical, biological, Membrane process, Adsorption, Advanced oxidation process and electrochemical processes were used for the treatment of different effluents. Due to the limitations of the conventional sludge stabi¬lization processes are not able to remove the heavy metals ions from waste activated sludge to make it reliable enough to be utilized as fertilizer in agricultural lands and farms. Advanced Oxidation Processes (AOPs) is the outstanding technique for treatment of polluted wastewaters containing intractable organic pollutants. AOP utilize the tough oxidising power of hydroxyl radicals that can trim down organic compounds to nontoxic end products. The most studied AOPs are photochemical-based processes (PAOPs), as UV/hydrogen peroxide, heterogeneous photo catalysis (HP), photo-Fenton (PF), UV plus ozone and combination of these technologies. Currently, advanced oxidation process is among the most frequently used approaches to remove pollutants that have low biodegradability or high chemical stability. These methods are depend on the generation of hydroxyl free radical (HO*) as a strong oxidant for the destruction of compounds which cannot be oxidized using conventional oxidants. This paper reviews that, the application of AOP for the removal of different kinds of toxic pollutants from the industrial wastewater.

Keywords: Advanced Oxidation Processes, hydroxyl free radical, sludge stabilization.

References:

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88.

Authors:

M. Sheik Dawood, A. Fardhan Ahmed, M. Jehosheba Margaret, C. Yesubai Rubavathi.

Paper Title:

Armored Modular and Non-Modular Vehicle: A Survey.

Abstract: Armored vehicles are being used in the defense sector for many years. The armored vehicles have been so efficient and productive and have survived these many years. In the year 1995, a new concept was introduced called modular vehicles, which means we can use one vehicle for almost all purposes. In the defense sector, the number of modular vehicles is less compared to the non-modular vehicles. If we choose the future with modular vehicles it is going to be the wisest decision which we take. The modular vehicles are better in every way when compared with non-modular vehicles. In this paper, we present a detailed survey on both the modular and non-modular vehicles used in the defense sector and we suggest a more advanced modular vehicle which can serve the defense sector for years without a need of replacement.

Keywords: Armored Modular Vehicle (AMV), Central Tire Inflation System (CTIS), All-Welded Steel, Armored Personnel Carrier (APC), Light Armored Vehicle (LAV), Non-Modular.

References:

  1. https://en.wikipedia.org/wiki/TATA_Kestrel
  2. http://www.military-today.com/apc/kestrel.htm
  3. https://www.militaryfactory.com/armor/detail.asp?armor_id=699
  4. https://en.wikipedia.org/wiki/M1126_Infantry_Carrier_Vehicle
  5. https://en.wikipedia.org/wiki/M1127_Reconnaissance_Vehicle
  6. https://en.wikipedia.org/wiki/M1128_Mobile_Gun_System
  7. https://en.wikipedia.org/wiki/M1129_Mortar_Carrier
  8. https://en.wikipedia.org/wiki/M1130_Commander%27s_Vehicle
  9. https://en.wikipedia.org/wiki/M1131_Fire_Support_Vehicle
  10. https://en.wikipedia.org/wiki/M1132_EngineerSquad_Vehicle
  11. https://en.wikipedia.org/wiki/M1133_Medical_Evacuation_Vehicle
  12. https://en.wikipedia.org/wiki/M1134_Anti-Tank_Guided_Missile_Vehicle
  13. https://en.wikipedia.org/wiki/M1135_Nuclear,_Biological,_Chemical,_Reconnaissance_Vehicle
  14. https://en.wikipedia.org/wiki/BTR-90
  15. http://www.military-today.com/apc/btr_90.htm
  16. https://www.militaryfactory.com/armor/detail.asp?armor_id=51
  17. https://www.forecastinternational.com/archive/disp_pdf.cfm?DACH_RECNO=1002
  18. https://en.wikipedia.org/wiki/LAV_III
  19. http://www.military-today.com/apc/kodiak.htm
  20. http://www.army-guide.com/eng/product1055.html
  21. https://en.wikipedia.org/wiki/Patria_AMV
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  23. http://www.military-today.com/apc/patria_amv.htm
  24. https://www.army-technology.com/projects/patria/
  25. http://www.deagel.com/Armored-Vehicles/AMV-8x8_a000585001.aspx
  26. https://en.wikipedia.org/wiki/Boxer_(armoured_fighting_vehicle)
  27. https://www.defenseindustrydaily.com/the-fighter-still-remains-dutch-to-continue-with-boxer-apc-program-updated-02410/
  28. http://www.military-today.com/apc/havoc.htm
  29. https://www.army-technology.com/projects/havoc-8x8-armoured-modular-vehicle/

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89.

Authors:

K. Christopher Gunasingh, G. Hemalatha, P. Mosae Selvakumar.

Paper Title:

Effect of Layered Double Hydroxide (Mg-Al) Reinforced in Concrete for Enhancing Thermal Comfort.

Abstract: Experimental study was done on the effect of layered double hydroxide (LDH) Magnesium-Aluminium (Mg-Al) on M20 grade concrete. Different weight percentage (0.5, 0.75, 1.0 & 1.25 Weight percentage of cement) of MgAl LDH as nano material was added with cement concrete cubes. These cubes were tested for X-ray diffraction (XRD), temperature absorption measurement and compressive strength. The XRD pattern of the concrete cubes shows the presence of MgAl LDH without any reaction with concrete. To find the compressive strength of the M20 concrete cubes contains with MgAl LDH, compressive strength test was performed. The experimental investigation shows the optimum percentage of MgAl LDH to be added for giving maximum temperature absorption without affecting the compressive strength and it is proved from the result that there is absorption of temperature to improve the thermal comfort.

Keywords: MgAl LDH, XRD, M20 Concrete & Compressive Strength.

References:

  1. He, Y.X., Zhang, S.L., Yang, L.Y., Wang, Y.J. and Wang, J., 2010. Economic analysis of coal price–electricity price adjustment in China based on the CGE model. Energy Policy, 38(11), pp.6629-6637.
  2. Omer, Abdeen Mustafa. "Energy, environment and sustainable development." Renewable and sustainable energy reviews 12, no. 9 (2008): 2265-2300.
  3. Rees, William E. "The ecological crisis and self-delusion: implications for the building sector." Building Research & Information 37, no. 3 (2009): 300-311.
  4. Sovacool, Benjamin K., and Ishani Mukherjee. "Conceptualizing and measuring energy security: a synthesized approach." Energy 36, no. 8 (2011): 5343-5355.
  5. Zhou, Nan, Mark D. Levine, and Lynn Price. "Overview of current energy-efficiency policies in China." Energy policy 38, no. 11 (2010): 6439-6452.
  6. Ramirez, C. A., M. Patel, and K. Blok. "The non-energy intensive manufacturing sector.: An energy analysis relating to the Netherlands." Energy 30, no. 5 (2005): 749-767.
  7. Kousksou, Tarik, Pascal Bruel, Abdelmajid Jamil, T. El Rhafiki, and Youssef Zeraouli. "Energy storage: Applications and challenges." Solar Energy Materials and Solar Cells 120 (2014): 59-80.
  8. Alva, Guruprasad, Lingkun Liu, Xiang Huang, and Guiyin Fang. "Thermal energy storage materials and systems for solar energy applications." Renewable and Sustainable Energy Reviews 68 (2017): 693-706.
  9. Soares, N., J. J. Costa, A. R. Gaspar, and P. Santos. "Review of passive PCM latent heat thermal energy storage systems towards buildings’ energy efficiency." Energy and buildings 59 (2013): 82-103.
  10. Viklund, Sarah Broberg, and Maria T. Johansson. "Technologies for utilization of industrial excess heat: potentials for energy recovery and CO 2 emission reduction." Energy Conversion and Management 77 (2014): 369-379.
  11. Kreiner, Helmuth, Alexander Passer, and Holger Wallbaum. "A new systemic approach to improve the sustainability performance of office buildings in the early design stage." Energy and Buildings 109 (2015): 385-396.
  12. Gao, Yanshan, Qiang Wang, Junya Wang, Liang Huang, Xingru Yan, Xi Zhang, Qingliang He, Zipeng Xing, and Zhanhu Guo. "Synthesis of highly efficient flame retardant high-density polyethylene nanocomposites with inorgano-layered double hydroxides as nanofiller using solvent mixing method." ACS applied materials & interfaces 6, no. 7 (2014): 5094-5104.
  13. Liu, P., Chen, W., Liu, Y., Bai, S. and Wang, Q., 2014. Thermal melt processing to prepare halogen-free flame retardant poly (vinyl alcohol). Polymer Degradation and Stability, 109, pp.261-269.
  14. Peng, Hong‐Jie, Jia‐Qi Huang, Xin‐Bing Cheng, and Qiang Zhang. "Review on High‐Loading and High‐Energy Lithium–Sulfur Batteries." Advanced Energy Materials (2017).
  15. Laoutid, Fouad, Leïla Bonnaud, Michaël Alexandre, J-M. Lopez-Cuesta, and Ph Dubois. "New prospects in flame retardant polymer materials: from fundamentals to nanocomposites." Materials Science and Engineering: R: Reports 63, no. 3 (2009): 100-125.
  16. Hull, T. Richard, Artur Witkowski, and Luke Hollingbery. "Fire retardant action of mineral fillers." Polymer Degradation and Stability 96, no. 8 (2011): 1462-1469.
  17. Levchik, Sergei V. "Introduction to flame retardancy and polymer flammability." Flame retardant polymer nanocomposites (2007): 1-29.
  18. Azwa, Z. N., B. F. Yousif, A. C. Manalo, and W. Karunasena. "A review on the degradability of polymeric composites based on natural fibres." Materials & Design 47 (2013): 424-442.
  19. Kaul, Pawan Kumar, A. Joel Samson, G. Tamil Selvan, I. V. M. V. Enoch, and P. Mosae Selvakumar. "Synergistic effect of LDH in the presence of organophosphate on thermal and flammable properties of an epoxy nanocomposite." Applied Clay Science 135 (2017): 234-243.
  20. Elvira-León, J. C., J. M. Chimenos, C. Isábal, J. Monton, J. Formosa, and L. Haurie. "Epsomite as flame retardant treatment for wood: Preliminary study." Construction and Building Materials 126 (2016): 936-942.
  21. Idumah, Christopher Igwe, and Azman Hassan. "Emerging trends in flame retardancy of biofibers, biopolymers, biocomposites, and bionanocomposites." Reviews in Chemical Engineering 32, no. 1 (2016): 115-148.
  22. Lecouvet, B., M. Sclavons, S. Bourbigot, and C. Bailly. "Towards scalable production of polyamide 12/halloysite nanocomposites via water‐assisted extrusion: mechanical modeling, thermal and fire properties." Polymers for Advanced Technologies 25, no. 2 (2014): 137-151.
  23. Schmelter, Dirk, and Horst Hintze-Bruening. "Highly Ordered Graphene Oxide and Reduced Graphene Oxide Based Polymer Nanocomposites: Promise and Limits for Dynamic Impacts Demonstrated in Model Organic Coatings." ACS applied materials & interfaces 8, no. 25 (2016): 16328-16338.
  24. Hussain, Shaik, Dipendu Bhunia, and Shamsher Bahadur Singh. "An Experimental Investigation of Accelerated Carbonation on Properties of Concrete." Engineering Journal 20, no. 2 (2016).
  25. Monkman, Sean, and Mark MacDonald. "Concrete Blocks Manufactured Using Sequestered Carbon Dioxide." Jounral of Masonry Science May (2015): 17-20.
  26. Monkman, S., and Mark MacDonald. "Carbon dioxide upcycling into industrially produced concrete blocks." Construction and Building Materials 124 (2016): 127-132.
  27. Raki*, J.J. Beaudoin, L. Mitchell, Layered double hydroxide-like materials: nanocomposites for use in concrete, Cement and Concrete Research 34 (2004) 1717–1724.
  28. Antonyraj C A, Srinivasan K. One-step hydroxylation of benzene to phenol over layered double hydroxides and their derived forms. Catalysis Surveys from Asia. 17 (2013) 47-70.
  29. Gomes A, Cocke D, Tran D and Baksi A. Layered Double Hydroxides in Energy Research: Advantages and Challenges. In Energy Technology (2015) 309-316.
  30. Tronto, J., Bordonal, A. C., Naal, Z., & Valim, J. B. Conducting polymers/layered double hydroxides intercalated nanocomposites. In Materials Science-Advanced Topics. InTech (2013).

500-505

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90.

Authors:

Balaka Ramesh Naidu, P.V.G.D Prasad Reddy.

Paper Title:

Fusion of Face and Voice for a Multimodal Biometric Recognition System.

Abstract: Biometric authentication system takes a primary role in the present modern society, computers are becoming a part of everyday life. It provides more security than the traditional systems. In traditional authentication systems password, pin-number, or signature is used for identification but these can be lost, stolen or subject to spoofing attacks. This paper introduces combination of two individual human traits, face and voice signal which are used for identification. The biometric authentication system with two traits supports more security and reliability than the single source of identification system.This paper presents a biometric recognition system integrating face and voice signal based on score level fusion. The features are extracted individually from the preprocessed traits and then classified the data using Gaussian mixture model. After classification, fuse the traits to make the training dataset. Test data is compared with the training dataset and then display the result whether the individual is genuine or an impostor. Performance measures like False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), and Failure To Capture (FTC) are calculated and performance evaluated. it is proved that the proposed biometric system overcomes the limitations of individual biometric systems and also meets the less response time as well as the good accuracy requirements.

Keywords: Biometric System, Face recognition, Voice recognition, Score level fusion, FAR, FRR, EER, FTC.

References:

  1. Israa M. Alsaadi “Physiological Biometric Authentication Systems, Advantages, Disadvantages and Future Development: A Review”, IJSTR, VOL.4, Issue 12, DEC 2015 pages 238-247.
  2. David ALeavens and Autumn BHostetter, etc.al, ”Tactical use of unimodal and bimodal communication by chimpanzees, Pan troglodytes”, Elsevier, 67, Issue 3, Mar 2004, Pages 467-476.
  3. SheetalChaudhary and RajenderNath “A New Multimodal Biometric Recognition System Integrating Iris, Face and Voice” IJARCSSE, Vol. 5, Issue 4, April 2015, pages 145-150.
  4. Sanderson and K.K. Paliwal,”Identity verification using speech and face information. Digital Signal Processing”, IDIAP, Vol.14, Issue 5, Mar 2004 pages 449–480.
  5. SheetalChaudhary and RajenderNath,”A Multimodal Biometric Recognition System Based on Fusion of Palmprint, Fingerprint and Face”, IEEE, proceedings of ICARTCC, 2009, pages 596-600.
  6. Rattani and D. R. Kisku etc.al. ”Feature Level Fusion of Face and Fingerprint Biometrics”, IEEE, the Ministry of Foreign Affairs and the Bio secure European Network of Excellence, pages 1-6, May 2007.
  7. Sheena S and Sheena Mathew, “A Study of Multimodal Biometric System”, IJERT, Vol.3, Issue 15, Dec 2014, pages 93–98.
  8. Bicego, A. Lagorio, E. Grosso and M. Tistarelli, "On the use of SIFT features for face authentication", Proceedings of CVPR Workshop, New York, 2006.
  9. Dalal N, Triggs B “Histograms of oriented gradients for human detection. In: IEEE conference on Computer vision and pattern recognition, 2005, pages 886–893.
  10. Dadi HS, PillutlaGKM (2016) Improved face recognition rate using HOG features and SVM classifier.OSR J Electron CommunEng (IOSR-JECE) Vol.11,Issue 4,pages 34–44.
  11. Prasad Reddy PVGD, SrinivasRao K, Srinivas Y, “Unsupervised image segmentation method based on finite generalized gaussian distribution with EM and K-means algorithm”, ProcInt J ComputSciNetwSecur, 2007 Vol.7, Issue 4, pages 317–321.
  12. Dominguez JA et al (2003) A practical procedure to estimate the shape parameters in the generalized gaussian distribution. In: IEEE transactions on image processing, pages 1–37.
  13. Mariko Akutsu and Yasuhiro Oikawa etc,al., “Extract voice information using high-speed camera” Published by the Acoustical Society of America through the American Institute of Physics, Jan 2013, Vol. 19 , pages 1-9.
  14. Dhanalakshmi and S. Palanivel,”Classification of audio signals using AANN and GMM”, Elsevier, Jan 2011, Applied Soft Computing Vol.11.pages 716–723.
  15. Jain A, Nanda kumar K and Ross A “Score normalization in multimodal biometric systems”, 2005, Pattern Recognition, Vol. 38, pages 2270–2285.

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91.

Authors:

K Murali Krishna, N Konda Reddy, M Raghavendra Sharma.

Paper Title:

Forecasting of Daily Prices of Gold in India using ARIMA and FFNN Models.

Abstract: The present paper is aimed to develop a forecasting model to predict the daily gold prices in India with high accuracy. The historical prices of gold were collected from 1st January, 2014 to 24th July, 2018 and the same is divided into training sample and out-of-sample. The forecasting models were developed using auto regressive integrated moving average (ARIMA) and artificial neural networks (ANN) for the daily gold prices in India. The performance of the forecasting model was evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The results show that, the feed forward neural networks (FFNN) model outperforming the traditional ARIMA model.

Keywords: Gold Prices, ARIMA, FFNN, MAE, MAPE and RMSE.

References:

  1. Box, G.E.P., Jenkins, G.M. and Reinsel, G.C., (1994), “Time Series Analysis Forecasting and Control”, 3rd, Englewood cliffs, N.J.Prentice Hall.
  2. Basikhasteh, M.A. Movafaghpour and M.Saadatifara, 2014, “Time Series Forecasting Model for Iranian Gold Price”. Indian J.Sci.Res., Vol.7, no.1, pp.1227-1232.
  3. Banhi Guha and Gautam Bandyopadhyay,Gold Price Forecasting using Arima model, Journal of Advanced Management Science, Vol 4, No.2, March 2016.
  4. Gershenson, “Artificial Neural Networks for Beginners” Networks, 2003., Vol. cs.NE/0308, pp.8.
  5. Haykin, S.S., 1999, “Neural Networks: A Comprehensive Foundation”, Upper Saddle River, N.J., Prentice Hall.
  6. Hornik, 1993, Some New result on neural network approximation, Neural Network, 6(1993), pp. 1069-1072.
  7. L Jung, G.M. and Box, G.E.P, (1979), “ On A measure of lack of Fit in Time Series Models”, Biometrika, 65297-303.
  8. A.Khan, 2013, “Forecasting of Gold Prices (Box Jenkins Approach)”. ‘International Journal of Emerging Technology and Advanced Engineering, Vol.3, PP.662-67.
  9. MAKRIDAKIS S., WHEEL WRIGHT. S.C., HYNDMAN R.J., 2003, Forecasting Methods and Applications, (John Wiley & Sons).
  10. M.Massarrat Ali Khan,Forecasting of Gold Prices Internal Journal Emerging Technology and Advanced Engineering. ISSN 2250-2459, ISO 9001:2008 Volume 3, Issue 3, March 2013
  11. Murali Krishna, Dr. M. Raghavender Sharma and Dr. N.Konda Reddy,Forecasting of silver prices using Artificial Neural Networks. JARDCS, Volume 10, 06 issue 2018.
  12. Pitigalaarachchi P.A.A.C, Jayasundara D.D.M, Chandrasekara N.V, 2016, Modelling and Forecasting Sri Lankan Gold Prices IJSBAR, ISSN 2307-4531.
  13. Raghavender, M. (2009), Forecasting paddy yield in Andhra Pradesh using seasonal Time Series Model, Bulletin of Pure and applied sciences.
  14. Rama Krishna & Naveen Kumar.B, 2013, Forecasting yield per Hectare of Rice in AP; IJMCAR, ISSN 2249-6955, Volume 3, Issue 1, March 2013, 9-14.
  15. shahriar shafiee, Erkan Topal Elsevier,An overview of global gold Market and gold price forecasting Resources policy, 35(2010), 178-189.
  16. https://www.goldpricesindia.com.

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92.

Authors:

Raffi Mohammed, B Ramgopal Reddy, Aluri Manoj

Paper Title: