Volume-9 Issue-5, June 2020 – International Journal of Engineering and Advanced Technology (IJEAT)

D8935049420

IJEAT Journal — Fri, 19 Nov 2021 11:16:59 +0000

The International Journal of Engineering and Advanced Technology (IJEAT) has ISSN: 2249-8958 (online), an open-access, peer-reviewed, periodical bi-monthly international journal, which is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in February, April, June, August, October and December. 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Development of IoT Based Smart Dustbin Monitoring System
B. Sridhar¹, G. Manasa², A. Mohini³, B. Naveen⁴
¹B.Sridhar*, Professor ECE department, LIET, Vizianagaram, India.
²G.Manasa, Final year UG Students, ECE department LIET Vizianagaram, India.
³A.Mohini, Final year UG Students, ECE department LIET Vizianagaram, India.
⁴B.Naveen, Final year UG Students, ECE department LIET Vizianagaram, India.
Manuscript received on May 06, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on June 30, 2020. | PP: 816-821 | Volume-9 Issue-5, June 2020. | Retrieval Number: D8935049420/2020©BEIESP | DOI: 10.35940/ijeat.D8935.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The objective of this proposed system is to stay clean and to cover all urban areas of Indian cities. In existing waste collection systems, there is no proper planning for waste collection due to certain problems that make the city unsanitary. The work which consists in cleaning the trash cans is not responsible and which aggravates the system in the event of emergency, an appropriate monitoring is compulsory to keep the city clean and green. Only the manual waste collection system is available. There are no automatic or technological systems. This proposed system is designed for efficient waste collection using the Internet of Things. The system uses a cloud-based monitoring system for monitoring waste. With the use of a cloud-based system, there is no need to regularly check the bins. The proposed model has dustbins contain Arduino based sensing and monitoring system which gives the information about the levels of filling of dustbins and locations. Whenever any dustbin is filled up, a message is sent to the concerned central station where regularly monitoring the levels and positions of dustbins is to be done. This station monitors all the dustbin levels and gives the directions of collecting the garbage by sending message to the garbage collectors and authority. This will avoid overflow of waste in the bin. A prototype of proposed system to be designed as per the consideration of all problems in garbage collection process and by this the proposal makes the city towards a smart city. That fulfils the goals of Swachhbharat.
Keywords: Internet of Things, Arduino Board, Cloud computing, Dustbins, Sensor Node Technology.

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E9613069520

IJEAT Journal — Thu, 18 Nov 2021 11:50:31 +0000

Photonic Crystal Fiber for Sensing Application
R. Hemalatha¹, S. Revathi²
¹R. Hemalatha*, Research Scholar, School of Electronics Engineering, VIT ,Vellore, India.
²S Revathi, Ph.D. in Optical Engineering, School of Electronics Engineering, VIT, Vellore, India.
Manuscript received on May 06, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on Janu 30, 2020. | PP: 481-494 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9613069520/2020©BEIESP | DOI: 10.35940/ijeat.E9613.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The concern for Sensing Application using photonic crystal had become an elevation in diverse field. Enhanced precision in sensing became an anticipation of users. Photonic crystal fibers are considered to be eccentric than optical fiber sensors due to its geometric structures. By proposing novelty in geometric structures of PCF can increase the sensitivity range as per the required application. In this paper we made a review on different sensors like physical , Curvature, Displacement, Electric and magnetic field, Refractive Index , Bio chemical, Biomedical which are used for sensing applications .
Keywords: Photonic Crystal Fiber, Biochemical PCF Fiber, Biomedical Sensors, Hollow and Dual core.

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E9659069520

IJEAT Journal — Thu, 18 Nov 2021 11:33:00 +0000

Methods for Accurate Resistance Measurement
Medishetty Akhil¹, Bhaskar H Venkataramaiah², Geethrani P³
¹Medishetty Akhil*, Department of Electronics and Communication Engineering, RV College of Engineering, Bengaluru.
²Bhaskar H Venkataramaiah, Senior Engineer, HD Renal V&V, Baxter Innovations and Business Solutions Pvt. Ltd.
³Dr. Geethrani P, Assistant Professor, Department of Electronics and Communication Engineering, RV College of Engineering, Bengaluru.
Manuscript received on May 06, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on Janu 30, 2020. | PP: 446-448 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9659069520/2020©BEIESP | DOI: 10.35940/ijeat.E9659.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Many devices have components that have very low ohmic properties. The resistances of these components should be measured to ensure their value doesn’t change. An overview is presented on precision resistance measurement for values less than 100Ω. Few techniques like Wheatstone bridge, current-voltage method, current comparators are discussed here.
Keywords: Adaptive Wheatstone bridge, Direct current comparator, Resistance measurement.

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D6782049420

IJEAT Journal — Mon, 08 Nov 2021 06:02:48 +0000

Air Quality Prediction by Classification of Supervised Machine Learning
T. R. Saravanan¹, V. Pavithra², G.Saranya³
¹Dr.T.R.Saravanan*, department of computer science and engineering, JEPPIAAR SRR engineering college, Chennai, India.
²V. Pavithra, department of computer science and engineering, JEPPIAAR SRR engineering college, Chennai, India.
³G. Saranya, department of computer science and engineering, JEPPIAAR SRR engineering college, Chennai, India.
Manuscript received on March 05, 2020. | Revised Manuscript received on March 16, 2020. | Manuscript published on April 30, 2020. | PP: 516-521 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6782049420/2020©BEIESP | DOI: 10.35940/ijeat.D6782.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Generally, air pollution refer to the release of various pollutants into the air which are threatening the human health and planet as well. The air pollution is the major dangerous vicious to the humanity ever faced. It causes major damage to animals, plants etc., if this keeps on continuing, the human being will face serious situations in the upcoming years. The major pollutants are from the transport and industries. So, to prevent this problem major sectors have to predict the air quality from transport and industries .In existing project there are many disadvantages. The project is about estimating the PM2.5 concentration by designing a photograph based method. But photographic method is not alone sufficient to calculate PM2.5 because it contains only one of the concentration of pollutants and it calculates only PM2.5 so there are some missing out of the major pollutants and the information needed for controlling the pollution .So thereby we proposed the machine learning techniques by user interface of GUI application. In this multiple dataset can be combined from the different source to form a generalized dataset and various machine learning algorithms are used to get the results with maximum accuracy. From comparing various machine learning algorithms we can obtain the best accuracy result. Our evaluation gives the comprehensive manual to sensitivity evaluation of model parameters with regard to overall performance in prediction of air high quality pollutants through accuracy calculation. Additionally to discuss and compare the performance of machine learning algorithms from the dataset with evaluation of GUI based user interface air quality prediction by attributes.
Keywords: Pollutants dataset, GUI results, machine learning, supervised algorithm.

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D6658049420

IJEAT Journal — Wed, 03 Nov 2021 07:42:16 +0000

Face Recognition based on Convolutional Neural Network
Surya Prasada Rao Borra¹, N. V. S. S. Pradeep², N. T. S. Raju³, S. Vineel⁴, V. Karteek⁵
¹Surya Prasada Rao Borra*, Dept. Of ECE, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, India. Email: suryaborra1679@gmail.com
²Nelluri Venkata Siva Sai Pradeep, Dept. Of ECE, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, India.
³Nidra Taraka Satya Raju, Dept. Of ECE, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, India.
⁴Saraswathi Vineel, Dept. Of ECE, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, India.
⁵Varkala Karteek, Dept. Of ECE, PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, India.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 17, 2020. | Manuscript published on April 30, 2020. | PP: 192-196 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6658049420/2020©BEIESP | DOI: 10.35940/ijeat.C6438.049420
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Abstract: Recognition holds great significance to give biometric authentications that are utilized in various applications particularly in attendance and security. A gathered database of the subjects is converted applying image processing methods to make this task. This paper suggests a cascade object detector based face detection and convolutional neural network alexnet based face recognition that can recognize the faces. The techniques used for face recognition are machine learning-based methods because of their great precision as associated with different methods. Face detection is the initial level before face recognition that is done utilizing a cascade object detector classifier. Face recognition is performed utilizing Deep Learning’s sub-field that is Convolutional Neural Network (CNN). It is a multi-layer network which is used to train the network, to perform a particular task using classification. Check learning of a trained CNN model that is AlexNet is used for face recognition.
Keywords: Convolutional Neural Network,Face Recogniti,,FaceDetection, MATLAB

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D6643049420

IJEAT Journal — Wed, 03 Nov 2021 07:33:03 +0000

Application of Cane Ash on Compressive Strength of Soil Uncovered to MgSO4
Mark Mikhail¹, Amin Chegenizadeh², Mahdi Keramatikerman³, Geoffrey Burns⁴, Sergei Terzaghi⁵, Hamid Nikraz⁶
¹Mark Mikhail*, was with Curtin University Bentley Campus in Perth, Western Australia.
²Dr. Amin Chegenizadeh, is with Curtin University, Perth, Western Australia.
³Dr. Mahdi Keramatikerman, is with Arup Australia, Queensland, Australia.
³Geoffrey Burns, is a Geotechnical Lead with Arup Australia, Queensland, Australia.
⁴Sergei Terzaghi, is a Geotechnical Lead with Arup America, Los Angeles, USA.
⁵Professor Hamid Nikraz, is with Curtin University, Perth, Western Australia.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 184-191 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6643049420/2020©BEIESP | DOI: 10.35940/ijeat.D6643.049420
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Abstract: 74 unconfined compressive strength tests (UCS) were conducted to examine effect of the Sugarcane Bagasse ash (SCBA) on compressive behavior of clay stabilized with cement, mixed with various cane ash contents, when uncovered to magnesium sulphate (MgSO4 ). The analysis showed an enhancement in the range of 10% to 35% for control specimens and an enhancement of 60% to 180% for magnesium sulfate exposed specimens. Scanning electron microscopic (SEM) analysis also showed interaction of clay and cement particles with sulphate confirming the results in the laboratory.
Keywords: Sulfate Attack; UCS; Clay; Cement; Bagasse Ash.

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D6650049420

IJEAT Journal — Wed, 03 Nov 2021 04:15:15 +0000

Text Extraction from Hoardings by Hybrid Model
D. Jayaram¹, J. Shiva Sai², CRK Reddy³, V. Kamakshi Prasad⁴
¹D. Jayaram*, Dept. of MCA CBIT, Hyderabad, India.
²J. Shiva Sai, Dept. of CSE, CBIT, Hyderabad, India Dr. CRK Reddy, Dept. of CSE, CBIT, Hyderabad, India
³Dr. V. Kamakshi , Prasad, Dept. of CSE, JNTUH, Hyderabad, India
Manuscript received on April 05, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 73-80 | Volume-9 Issue-4, April 2020. | Retrieval Number: D6650049420/2020©BEIESP | DOI: 10.35940/ijeat.D6650.049420
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Abstract: There are various techniques available to detect and extract the text from hoardings. Still it is a challenging task to detect text from images of various sizes, orientation, illuminations and color. With a view to improve on these, a hybrid method of text extraction and detection is proposed. The proposed method uses a symmetry features like Mutual Magnitude Symmetry (MMS), Mutual Direction Symmetry (MDS) and Gradient Vector Symmetry (GVS) to identify text pixel candidates from natural scenes. The proposed method is tested on different datasets like ICDAR, CUTE 80 and also images from mobile phones. Implementation of MMS, MDS, and GVS methods on above datasets has been carried out. Text extraction from hoardings in ICDAR is giving 74% accuracy, CUTE80 is giving 76% and on mobile images 83% of accuracy is achieved.
Keywords: MDS, MMS, GVS, edge detection, segmentation.

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A1775109119

IJEAT Journal — Tue, 02 Nov 2021 10:52:09 +0000

Emergency Escape Routing and Evacuation using Bellman Ford’s Algorithm
Baby D. Dayana¹, Shiv Pratap Singh², Shraman Das³, Pankaj Gautam⁴
¹Baby D Dayana*, Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Tamil Nadu, India.
²Shraman Das, Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Tamil Nadu, India.
³Shiv Pratap Singh, Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Tamil Nadu, India.
⁴Pankaj Gautam, Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Tamil Nadu, India.
Manuscript received on June 08, 2020. | Revised Manuscript received on June 25, 2020. | Manuscript published on June 30, 2020. | PP: 1349-1352 | Volume-9 Issue-5, June 2020. | Retrieval Number: A1775109119/2020©BEIESP | DOI: 10.35940/ijeat.A1775.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Evacuation Technique which addresses routing the populaces within the endangered architecture to the bordering emergency-exit points minimizing the mortality rate and avoiding hazards and obstacles at the same time.The fundamental magnitude would be to minimize the risk of total mortality during an emergency condition.The superlative case scenario would be to relinquish all individuals with smidgeon panic and naught transience tempo.This scrutiny will lessen the evacuation time factor by using the deluxe appropriate algorithm available for the shortest path prediction with all of the constraints.
Keywords: Bellman Ford’s Algorithm, Emergency evacuation,Iot for evacuation,Raspberry Pi,Shortest path planning.

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E1114069520

IJEAT Journal — Tue, 02 Nov 2021 10:43:11 +0000

Influence of Energy on Compaction Characteristics of High Expansive Soils
Talal Masoud¹, Manal O. Suliman²
¹Talal Masoud* ,Associate Professor, Civil Engineering department, College of Engineering, Jerash University, Jerash, Jordan.
²Manal O. Sulima, Assistant Professor, Associate Professor, Civil Engineering department, College of Engineering, Jerash University, Jerash, Jordan.
Manuscript received on June 08, 2020. | Revised Manuscript received on June 25, 2020. | Manuscript published on June 30, 2020. | PP: 1344-1348 | Volume-9 Issue-5, June 2020. | Retrieval Number: E1114069520/2020©BEIESP | DOI: 10.35940/ijeat.E1114.069520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Each soil type has different behavior with regard to determination of maximum dry density and optimum moisture content and therefore any soil type has its own compaction requirements for experimental purposes and for control the compaction in the field. The general purpose of this study is to a better understanding of the compaction characteristics of high expansive soils, with emphasis on the relationships of moisture content and dry density of high expansive soils at a range of compaction energy levels. To achieve this purpose, high expansive soils samples were subjected to Atterberg limit and a set of laboratory compaction tests to find compaction characteristics namely; maximum dry unit weight and optimum water content of high expansive soils at different compaction energy (compaction effort) for different number of hammer blows per each layer range from 10 to 50, which varied the energy per unit volume from 356 KN/m3 to 1188 KN/m3.Rather than single peak compaction curves, the most achieved compaction curves are an irregular one and half peak compaction curves. According to the comparison results of different compaction energy, it was concluded that the maximum dry unit weight of high expansive soil was not highly affected by gradually increase of applied energy. The results showed that, the maximum dry density of tested expansive soils sample increased from 1.48g/cm3 to 1.6g/cm3 with increase of compaction energy from 356 KN/m3 to 1188 KN/m3.
Keywords: Compaction energy, Compaction curve, High Expansive soils, Maximum dry density, Optimum moisture content, Standard Proctor.

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D6482049420

IJEAT Journal — Tue, 02 Nov 2021 10:31:13 +0000

Factor Affecting the Magnitude and the Direction of the Pressure Exerted on the Gravity Retaining Wall
Talal Masoud
Dr. Talal Masoud*, Civil Engineering Department, College of Engineering, Jerash University, Jerash, Jordan

Manuscript received on June 08, 2020. | Revised Manuscript received on June 25, 2020. | Manuscript published on June 30, 2020. | PP: 1340-1343 | Volume-9 Issue-5, June 2020. | Retrieval Number: D6482049420/2020©BEIESP | DOI: 10.35940/ijeat.D6482.069520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Gravity retaining wall are structures used to retain the soil by its weight .the stability of such type of walls depend on the magnitude and direction of the horizontal forces exerted by soil . it found that there is many factors affect the value and the acting point of acting. Based on this, a study was conducted to investigate the effect of water table, external vertical loads, sloping of the backfill and the type of the backfill soil. It show that, the value of the horizontal soil pressure increase from147KN/m’ on dry soil to about 307 KN/m’ as the soil become saturated.also,effect of external loads are studied , and show its increase the total horizontal forces of the soil pressure. Sloping the backfill soil behind the retaining wall also great effect on the earth pressure. The type of the backfill soil behind the retaining wall also investigated and found its effect of the earth forces.
Keywords: Gravity mass retaining wall, Earth pressure, Backfill, Retaining wall, Drainage system, Vertical loads, Internal friction

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