Volume-11 Issue-1, October 2021 – International Journal of Engineering and Advanced Technology (IJEAT)

A32121011121

IJEAT Journal — Mon, 08 Nov 2021 06:44:17 +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. The journal aims to publish high-quality peer–reviewed original articles in the area of Engineering and Technology that covers Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering and all interdisciplinary streams of Engineering and Technology. #Smart Computing and Information Processing #Signal and Speech Processing #Image Processing and Pattern Recognition #WSN#Artificial Intelligence and machine learning #Data mining and warehousing #Data Analytics #Deep learning #Bioinformatics #High Performance computing #Advanced Computer Networking #Cloud Computing #IoT #Parallel Computing on GPU #Human Computer Interactions #Recent Trends in Microelectronics and VLSI Design #Process & Device Technologies #Low-power design #Nanometer-scale integrated circuits #Application specific ICs (ASICs) #FPGAs #Nanotechnology #Nano electronics and Quantum Computing #3. Challenges of Industry and their Solutions, Communications #Advanced Manufacturing Technologies #Artificial Intelligence #Autonomous Robots #Augmented Reality #Big Data Analytics and Business Intelligence #Cyber Physical Systems (CPS) #Digital Clone or Simulation #Industrial Internet of Things (IIoT) #Manufacturing IoT #Plant Cyber Security #Smart Solutions – Wearable Sensors and Smart Glasses #System Integration #Small Batch Manufacturing #Visual Analytics #Virtual Reality #3D Printing # Internet of Things (IoT) #Internet of Things (IoT) & IoE & Edge Computing #Distributed Mobile Applications Utilizing IoT#Security, Privacy and Trust in IoT & IoE #Standards for IoT Applications #Ubiquitous Computing #Block Chain-enabled IoT Device and Data Security and Privacy #Application of WSN in IoT #Cloud Resources Utilization in IoT #Wireless Access Technologies for IoT #Mobile Applications and Services for IoT #Machine/ Deep Learning with IoT & IoE #Smart Sensors and Internet of Things for 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Deep Convolutional Neural Network Feed-Forward and Back Propagation (DCNN-FBP) Algorithm for Predicting Heart Disease using Internet of Things
Saranya N¹, Kavi Priya S²
¹Saranya N*, Research Scholar, Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India.
²Kavi Priya S, Associate Professor, Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi, Tamilnadu, India.
Manuscript received on October 18, 2021. | Revised Manuscript received on October 27, 2021. | Manuscript published on October 30, 2021. | PP: 283-287 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A32121011121 | DOI: 10.35940/ijeat.A3212.1011121
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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: In recent years, due to the increasing amounts of data gathered from the medical area, the Internet of Things are majorly developed. But the data gathered are of high volume, velocity, and variety. In the proposed work the heart disease is predicted using wearable devices. To analyze the data efficiently and effectively, Deep Canonical Neural Network Feed-Forward and Back Propagation (DCNN-FBP) algorithm is used. The data are gathered from wearable gadgets and preprocessed by employing normalization. The processed features are analyzed using a deep convolutional neural network. The DCNN-FBP algorithm is exercised by applying forward and backward propagation algorithm. Batch size, epochs, learning rate, activation function, and optimizer are the parameters used in DCNN-FBP. The datasets are taken from the UCI machine learning repository. The performance measures such as accuracy, specificity, sensitivity, and precision are used to validate the performance. From the results, the model attains 89% accuracy. Finally, the outcomes are juxtaposed with the traditional machine learning algorithms to illustrate that the DCNN-FBP model attained higher accuracy.
Keywords: Deep Convolutional Neural Network, Internet of Things (IoT), Wearable devices, Heart disease.
Scope of the Article: Internet of Things

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A31371011121

IJEAT Journal — Wed, 03 Nov 2021 05:44:19 +0000

Multi-Layer Classification and Segmentation with Opinion Mining Learning Ensemble
Ravi Kumar Kuchipudi¹, V.V. Jaya Rama Krishnaiah²
¹Ravi Kumar Kuchipudi*, Research Scholar, Department of Computer Science and Engineering, Acharya Nagarjuna University, Guntur (Andhra Pradesh), India.
²Dr. V.V. Jaya Rama Krishnaiah, Associate Professor, ASN Engineering College, Tenali (Andhra Pradesh), India.
Manuscript received on October 18, 2021. | Revised Manuscript received on October 27, 2021. | Manuscript published on October 30, 2021. | PP: 276-282 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A31371011121 | DOI: 10.35940/ijeat.A3137.1011121
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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: Social media has become the main area for advertising, events, campaigns, protests etc. in recent years. It offers the public a forum for expressing their opinions and beliefs to the masses. The user beliefs, habits and interests of businesses are extremely important and give users a glimpse into their thinking. Data mining is one of the tools that enables these businesses to extract useful information from user data that can be examined to generate a set of knowledge and identify a user opinion that helps companies to create user-specific products. Twitter Data Mining and other social platforms are very important since its enormous user base consists of a mixture of thoughts and opinions that may be used to anticipate results of campaigns, product assessments and similarity if correctly studied. This research provides a classification system to perform Twitter Opinion Mining Segmentation based on Ensemble Learning. The suggested approach can detect and filter out boxes and uses text segmentation for efficient text classification and voice taging.
Keywords: Machine Learning, Supervised Learning, Text Analysis, Sentiment Analysis, Natural Language Processing.
Scope of the Article: Machine Learning

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A32191011121

IJEAT Journal — Tue, 02 Nov 2021 05:17:30 +0000

Prediction of Mobile Model Price using Machine Learning Techniques
Kumuda S¹, Vishal Karur², Karthick Balaje S E.³
¹Kumuda*, Department of Electronics and Communication, NIE Institute of Technology, Mysore (Karnataka), India.
²Vishal Karur, Department of Electronics and Communication, JSS Academy of Technical Education, Bengaluru (Karnataka), India.
³Karthick Balaje S E., Department of Electronics and Communication, Karunya Institute of Technology and Sciences, Coimbatore (Tamil Nadu), India.
Manuscript received on October 20, 2021. | Revised Manuscript received on October 30, 2021. | Manuscript published on October 30, 2021. | PP: 273-275 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A32191011121 | DOI: 10.35940/ijeat.A3219.1011121
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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: Mobile phone has become a common commodity and usually the most common purchased item. Thousands of types of mobiles are released every year with new features and new specification and new designs. So the real question is prediction is that what is the real price of the mobile and to estimate the price of the mobile within the market for optimal marketing and successful launch of the product. Price has become a major factor for development of any product and its sustainability in the market. Mobile prices also impact the marketing of the mobile and also its popularity with other competitors. With the available specifications and desired designs, money is also an important factor to survive within the market. Customer usually sees that they are able to buy with the specification with the given estimated price or not. So to estimating the price is an important factor before releasing the mobile and also to know about the market and competitors. In this Prediction, Dataset is collected from the existing market and different algorithms are applied to reduce the complexity and also identify the major selection features and get the best comparison within the data. This Tool is used to find the best price with maximum specifications.
Keywords: Machine Learning, Data Collection, Forward Selection, Backward Selection.
Scope of the Article: Machine Learning

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A32161011121

IJEAT Journal — Sat, 30 Oct 2021 09:18:33 +0000

Analysing the Media Consumer Trends During the COVID-19 Period-The Rise of Otts in India
Sristy Lalaika V¹, Kalathila Uthej², Shreeya Rishi K³
¹Sristy Lalaika V*, Department of Information Technology, Indira Gandhi Technical University for Women, New Delhi, India.
²Kalathila Uthej, Department of Electronics and Communication Engineering, MVSR Engineering College, Osmania University, Hyderabad (Telangana) India.
³Shreeya Rishi K, Department of Electronics and Communication Engineering, MVSR Engineering College, Osmania University, Hyderabad (Telangana) India.
Manuscript received on October 20, 2021. | Revised Manuscript received on October 29, 2021. | Manuscript published on October 30, 2021. | PP: 267-272 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A32161011121 | DOI: 10.35940/ijeat.A3216.1011121
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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: In the wake of an unprecedented COVID-19 widespread, there is a massive development in the media and entertainment industry through OTT (Over-the-Top) platforms which offer video streaming directly to viewers via the internet without cable, broadcast, and satellite television supports. During the self-isolation period, OTTs have increased the overall media consumption to a real high and are now gradually becoming mainstream entertainment destination amidst the growing internet users. This mass intrusion has given an opportunity for many independent platforms to grow and run alongside the likes of already established biggies, Netflix and Amazon Prime. The telecom operators chipped in this aspect and are extensively working on optimizing their operations with advertising revenue and bringing in effective content strategies in order to reach out to more people. The present study focuses on this new development by using the technique of quantitative content analysis in order to know the cause of this sudden increase in OTT channels’ viewership among millennials during the lockdown period in India and its impact on traditional modes of communication.
Keywords: OTT, Meta medium, Digitization, Chi-square test, Mobile internet penetration, Net neutrality, Quantitative content analysis, Censorship.
Scope of the Article: Mobile Adhoc Network

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A32201011121

IJEAT Journal — Sat, 30 Oct 2021 09:04:08 +0000

Computation of Permeability of Soil using Artificial Intelligence Approaches
Jitendra Khatti¹, Kamaldeep Singh Grover²
¹Jitendra Khatti*, PhD Fellow, Department of Civil Engineering, Rajasthan Technical University, Kota, (Rajasthan), India.
²Kamaldeep Singh Grover, Professor, Department of Civil Engineering, Rajasthan Technical University, Kota, (Rajasthan), India.
Manuscript received on October 20, 2021. | Revised Manuscript received on October 28, 2021. | Manuscript published on October 30, 2021. | PP: 257-266 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A32201011121 | DOI: 10.35940/ijeat.A3220.1011121
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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 Gaussian Process Regression (GPR), Decision Tree (DT), Relevance Vector Machine (RVM), and Artificial Neural Network (ANN) AI approaches are constructed in MATLAB R2020a with different hyperparameters namely, kernel function, leaf size, backpropagation algorithms, number of neurons and hidden layers to compute the permeability of soil. The present study is carried out using 158 datasets of soil. The soil dataset consists of fine content (FC), sand content (SC), liquid limit (LL), specific gravity (SG), plasticity index (PI), maximum dry density (MDD) and optimum moisture content (OMC), permeability (K). Excluding the permeability of soil, rest of properties of soil is used as input parameters of the AI models. The best architectural and optimum performance models are identified by comparing the performance of the models. Based on the performance of the AI models, the NISEK_K_GPR, 10LF_K_DT, Poly_K_RVM, and GDANN_K_10H5 models have been identified as the best architectural AI models. The comparison of performance of the best architectural models, it is observed that the NISEK_K_GPR model outperformed the other best architectural AI models. In this study, it is also observed that GPR model is outperformed ANN models because of small dataset. The performance of NISEK_K_GPR model is compared with models available in literature and it is concluded that the GPR model has better performance and least prediction error than models available in literature study.
Keywords: Artificial Neural Network, Gaussian Process Regression, Optimum Performance, Relevance Vector Machine
Scope of the Article: Artificial Intelligence

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A31871011121

IJEAT Journal — Sat, 30 Oct 2021 08:57:44 +0000

Smart City for Future: Design of Data Acquisition Method using Threshold Concept Technique
Deepak NR¹, Thanuja N²
¹Deepak NR*, Professor, Department of Computer Science & Engineering, HKBK College of Engineering, Bangalore (Karnataka), India,
²Thanuja N, Asst. Prof., Department of Computer Science & Engineering, Bangalore Institute of Technology, (VTU), Bangalore (Karnataka), India.
Manuscript received on October 04, 2021. | Revised Manuscript received on October 25, 2021. | Manuscript published on October 30, 2021. | PP: 251-256 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A31871011121 | DOI: 10.35940/ijeat.A3187.1011121
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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 massive deployment of Internet of Things (IoT) is allowing Smart City projects and initiatives all over the world. Whereas IOT deals with intricate systems that integrates multiple disperse components towards their synergetic use. Due to resource constraints, there will be a problem in the future to provide all the services to the residents. To continue to serve and improve the standard of living of the growing population, it is necessary to develop smart cities. The Smart City aims to make optimal and sustainable use of all resources, while maintaining an appropriate balance between social, environmental and economic costs. The wireless sensors are attached to street lamp, water tank, dustbin. Sensors are then attached to Arduino microcontroller board where each and every necessary parameter for city are monitored and updated to cloud. The cloud is connected with Blynk server in turn which is connected to build in Blynk application of user’s Android phone. The technology we use is IOT in this model a system of interconnected smart modules is developed where each and every parameter necessary for a city is monitored and updated to the cloud. Emphasis is given on how sensing and communication technologies of IOT can effectively be used in smart city monitoring. The project aims at developing a system which facilitates aids in collection of data with the help of interconnected modules consisting of multiple sensors useful for smart city monitoring. Further this project also includes controlling of some parameters like water, light and dust bin.
Keywords: Street lamp, Water tank, Dustbin, Blynk App
Scope of the Article: Data Analytic

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A32131011121

IJEAT Journal — Sat, 30 Oct 2021 08:47:45 +0000

Classification of Grains and Quality Analysis using Deep Learning
Priyanshu Shrivastava¹, Karan Singh², Ashish Pancham³
¹Priyanshu Shrivastava*, Department of Electronics and Telecommunication Engineering, Mumbai (M.H), India.
²Karan Singh, Department of Electronics and Telecommunication Engineering, Mumbai (M.H), India.
³Ashish Pancham, Department of Computer Engineering, Mumbai (M.H), India.
Manuscript received on October 18, 2021. | Revised Manuscript received on October 27, 2021. | Manuscript published on October 30, 2021. | PP: 244-250 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A32131011121 | DOI: 10.35940/ijeat.A3213.1011121
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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: There are various varieties of Rice and lentils. Price fabrication and adulteration have been some of the various issues faced by the consumers, farmers and wholesale retailers. Traditional methods for Identification of these similar types of grains and their quality analysis are crude and inaccurate. Methods were tried to implemented earlier but due to financial inability and low efficiency, they weren’t successful. To overcome this problem, the project proposes a method that uses a machine learning technique for identification and quality analysis of these grains. Rice and Lentils which have the maximum consumption have been selected. Lentils are designated into classes based on colors. The technique of determining the elegance of a lentil is with the aid of seed coat shade. Red lentils can be confirmed through the cotyledon coloration. Lentil types may also have a huge variety of seed coat colors from inexperienced, red, speckled inexperienced, black and tan. The cotyledon colour may be red, yellow or inexperienced. The size and color of every Indian Lentil type (i.e. Red, Green, and Yellow, Black, White) are decided to be large or Medium or small, then size and colour end up part of the grade name. An smart machine is used to perceive the kind of Indian lentils from bulk samples. The proposed machine allows kernel length and coloration size using picture processing techniques. These Lentil size measurements, when combined with color attributes of the sample, classify three lentil varieties commonly grown in India with the highest accuracy. Rice is one of most consumed grains in India so its quality is of utmost importance. In this project, we identify and grade five types of rice and grade them with the help of their distinguished features such as size, color, shape, and surface. The project works in three phases viz., Feature Extraction, Training, and Testing. Various rice grain has a different shape, size, surface and various lentils come in different colors, Hence the feature that will be extracted is texture and colors. The method of regression will be adopted for the grading mechanism where the output will be in terms of percentage purity. The methodology for the extraction of the feature will be GLCM and Edge Detection where for supervised learning SVM and Back Propagation will be utilized. The project provides an efficient replacement for the traditional grading mechanism and standardizes the pricing of farm products based on their quality only.
Keywords: Resnet, Fastai.
Scope of the Article: Classification

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A32011011121

IJEAT Journal — Sat, 30 Oct 2021 08:31:42 +0000

A Data-Centric Approach to Improve Machine Learning Model’s Performance in Production
Pritom Bhowmik¹, Arabinda Saha Partha²
¹Pritom Bhowmik*, B.Tech. Department of Computer Science & Engineering, Institute of Engineering & Management, Salt-Lake, Kolkata, India.
²Arabinda Saha Partha, B.Tech. Department of Computer Science & Engineering, Institute of Engineering & Management, Salt-Lake, Kolkata, India.
Manuscript received on October 09, 2021. | Revised Manuscript received on October 27, 2021. | Manuscript published on October 30, 2021. | PP: 240-243 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A32011011121 | DOI: 10.35940/ijeat.A3201.1011121
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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: Machine learning teaches computers to think in a similar way to how humans do. An ML models work by exploring data and identifying patterns with minimal human intervention. A supervised ML model learns by mapping an input to an output based on labeled examples of input-output (X, y) pairs. Moreover, an unsupervised ML model works by discovering patterns and information that was previously undetected from unlabelled data. As an ML project is an extensively iterative process, there is always a need to change the ML code/model and datasets. However, when an ML model achieves 70-75% of accuracy, then the code or algorithm most probably works fine. Nevertheless, in many cases, e.g., medical or spam detection models, 75% accuracy is too low to deploy in production. A medical model used in susceptible tasks such as detecting certain diseases must have an accuracy label of 98-99%. Furthermore, that is a big challenge to achieve. In that scenario, we may have a good working model, so a model-centric approach may not help much achieve the desired accuracy threshold. However, improving the dataset will improve the overall performance of the model. Improving the dataset does not always require bringing more and more data into the dataset. Improving the quality of the data by establishing a reasonable baseline level of performance, labeler consistency, error analysis, and performance auditing will thoroughly improve the model’s accuracy. This review paper focuses on the data-centric approach to improve the performance of a production machine learning model.
Keywords: Annotation, Augmentation, big-data, bias-error, baseline, consistent-leveling, Data-centric, model-centric, error-analysis, good-data, Model-accuracy, Human-level-performance, proxy.
Scope of the Article: Machine Learning

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A31651011121

IJEAT Journal — Thu, 28 Oct 2021 07:37:22 +0000

Applications of Drone Technology in Construction Industry: A Study 2012-2021
Gayatri Mahajan
Gayatri Mahajan*, Assistant Professor, Allana College of Architecture, Pune. (Maharashtra). India.
Manuscript received on September 21, 2021. | Revised Manuscript received on October 23, 2021. | Manuscript published on October 30, 2021. | PP: 224-239 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A31651011121 | DOI: 10.35940/ijeat.A3165.1011121
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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: Technology plays a pivotal role in shaping construction industry. Adoption of new trends, tools, software and technology would motivate to minimize problems that arise during use of drones in construction. The paper not only elaborates previous reviews on Drone Technology (DT) in Construction Industry (CI), but also explores extensive literature review on (i) classification of drones, construction software used with drone, (iii) overview of utility of DT in construction and related industries (iv) recent construction technology trends, tools and techniques accomplish with drone technology. This is basically a review paper. The aim of this paper is to study the potential of DT in construction industry, extended it to understand the following issues in better way(i) benefits and impacts of drone in CI, (ii) record disadvantage of drone in CI(iii) integration of BIM with DT at substantial length and volume (iv)extensive description and enumeration on applications and uses of drones in CI(v) use of drone at each stage of construction stage to monitor the progress of construction rightly from the purchase of land to close out the project(vi)lastly appended a note on the impact of COVID-19 on construction. This study (2012-2021) also discusses challenges, opportunities, limitations, and strategies for the adoption of drones in construction. It assists to contractors, building planners, designers, academicians, engineers, and architects to improve the construction activities for greater efficiency and better performance. It also motivates towards inclusion of these technologies in the curriculum in Architecture Engineering.
Keywords: Drone technology, Unmanned Aerial Vehicle (UAV), Advance construction software, Construction technology trends, and construction stages, BIM integration
Scope of the Article: Construction Engineering

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A32081011121

IJEAT Journal — Thu, 28 Oct 2021 07:20:07 +0000

An Improved Vanet Clustering and Channel Quality using Movc and Weighted Probability
Sohel Rana¹, Md Alamin Hossan², Abidullha Adel³, Jayastree⁴
¹MD. Sohel Rana*, Department of Computer Science and Technology, Nanjing Univesity of Information & Science, Nanjing, China.
²Md. Alamin Hossan, Asst. Biomedical Engineer, Genetic Trading, Dhaka, Bangladesh.
³Abidullha Adel, Assistant Professor and Lecturer, Kunduz University, Afghanistan.
⁴Jayastree. J, Research Associate, Department of Electronics and Communication Engineering, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on October 13, 2021. Revised Manuscript received on October 24, 2021. Manuscript published on October 30, 2021. | PP: 211-223 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A32081011121 | DOI: 10.35940/ijeat.A3208.1011121
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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: VANET offers a vast range of application which will make travel experience safe. The concept behind VANET is quite simple which incorporates wireless communication and capable of sharing data; the vehicles are turned for offering network to provide services which can be used in the home or office networks. VANET network nodes are move in-network with higher velocity in the network. This higher mobility of vehicles causes increased resource and energy utilization which causes failure of nodes. Resource and energy consumption is higher for inefficient constructed clustering for VANET. Also, vehicle mobility causes several obstacles in data transmission this causes degraded channel quality. data transmission this causes degraded channel quality. In this paper concentrated on improving channel quality and energy efficiency in the network. Initially, performed efficient clustering through middle-order based cluster head (CH) election (MOVC) mechanism. Through effective CH election resource and energy utilization is limited. In the next stage, channel quality is improved by the proposed weighted probability approach for effective data transmission between vehicles in the VANET network. The weighted probability approach estimates the path for data transmission to destination vehicle. Through the selection of CHs using the middle-order approach, the network exhibits an effective maintenance phase here, the weighted probability is applied for the estimation of the path for effective data transmission in the VANET. The comparative analysis of the proposed approach exhibits significant performance rather than conventional technique. The estimated residual energy of the proposed approach is almost 20% higher than ACO, PSO, and KH. The packet delivery rate is observed around 80% and delay is minimal as of 2.5sec.
Keywords: MOVC Algorithm, VANET, Enhance channel Quality of VANET, Development weighted probability for Data loss.
Scope of the Article: Clustering

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