Informatics Publishing Limited

M. Umaselvi ¹, S. Leena Maria ², M.J. Sridevi ³, B. Gayathri ⁴, Khaled A. A. Alloush ⁵

Affiliations
1 Department of Computer Science Engineering, P.A. College of Engineering and Technology, IN
2 Department of Mathematics, Government Engineering College, Hassan, IN
3 Department of Mathematics, Government First Grade College for Women, IN
4 Department of Computer Science, Bishop Heber College, IN
5 Department of Computer Science, Arab Open University, SA

Abstract

The increasing movement from rural areas to urban areas, along with the widening gap in population, has resulted in metropolitan areas becoming extremely overpopulated. As a result of the high volume of traffic that occurs in these areas, traffic monitoring is an extremely important activity. According to the findings of this study, an improved authentication and communication protocol that is based on clusters could be implemented for Intelligent Transportation Systems in Vehicular Ad Hoc Networks (VANETs). Our number one objective is to enhance the sharing of resources amongst vehicles through improved communication. Cluster-based routing protocols allowed us to increase the scalability, stability, and dependability of fast-moving VANETs. This was accomplished in the context of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. To easing concerns regarding privacy and safety, we arranged for the vehicles to be certified by an independent contractor. Through the utilization of Graph Neural Networks (GNNs), we can reduce the number of instances in which links fail, as well as minimize end-to-end (E2E) delays and route requests. Our approach has resulted in several important benefits, including enhancements to throughput, reductions in the amount of time required for TCP socket initialization, acceleration of TCP handshake response, and DNS lookup. Shortrange peer-to-peer wireless communication is the focus of the protocols that are used within a cluster that is 400 meters in radius. Utilizing new peer-to-peer wireless communications over VANET is what is meant by the term resource-conserving in this context. Within the framework of the suggested protocol secure authentication method, a certifying authority is responsible for the generation of a secure authentication key for the vehicle, which is subsequently provided to the vehicle.

Keywords

VANETs, V2V, Graph Neural Network, TCP

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M. Umaselvi ¹, P. Subburethina Bharathi ²

Affiliations
1 Majan University College, Sultanate of Oman, OM
2 Hallmark Business School, Tiruchirappalli–620 102, IN

Abstract

One of the major problems facing by the developing and developed country is quality of work life. The issue is not just one of achieving greater human satisfaction but it also aims at improving productivity, adaptability and overall effectiveness of organization. The quality of work life movement in a broader sense seeks to achieve integration among technological, human, organizational and societal demands. The researcher has chosen the college teachers for a study “Quality of Work Life” because the past researchers have concentrated their studies in the field of Medical,Industries, Universities and other areas. The global economy presents the organizations with new challenges to be faced with a need for the employees’ involvement and commitment in achieving organizational goals. Such involvement and commitment could be secured only through improved Quality of Work Life. Under the affiliation of Bharathidasan University Arts and Science colleges in Tiruchirappali City the study was conducted and the universe of the study includes 16 colleges located within the city limit and 1479 college teachers were working during May 2007 – February 2009. Disproportionate Stratified random sampling method was select a sample of 239 respondents from the universe. The collected data after being coded,were analysed using Statistical Package for Social sciences Research(SPSS) and various statistical tests were applied based on hypotheses and matching variables.

Keywords

Quality of Work Life, Employee and Working Environment.

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K. Prabhakar ¹, M. Umaselvi ², Shibili Said ³, Saswata Das ⁴

Affiliations
1 Department of Computer Science and Engineering, School of Engineering and Technology, CMR University, IN
2 Department of Computer Science and Engineering, P.A College of Engineering and Technology, IN
3 Department of Computing and Engineering, University of West London, AE
4 Telus International, West Bengal, IN

Abstract

This research paper introduces a novel approach for the classification of dementia disease using Rotation Forests based on Deep Convolutional Generative Adversarial Networks (DCGAN). Dementia is a significant cognitive disorder prevalent among the elderly population, demanding accurate and early diagnosis for effective intervention. Traditional methods often rely on manual feature extraction and shallow learning, which may lack the ability to capture intricate patterns in complex medical data. In this study, we propose a fusion of Rotation Forests, a robust ensemble learning technique, with DCGAN, a deep learning model recognized for its feature extraction capabilities. The Rotation Forests enhance the diversity of the base classifiers, while DCGAN learns meaningful features from raw medical imaging data. We validate the proposed approach on a comprehensive dataset and compare its performance against existing methods. The experimental results demonstrate the effectiveness of the Rotation Forests based on DCGAN approach in accurately classifying dementia diseases, showcasing its potential as a valuable tool in medical diagnosis.

Keywords

Dementia disease, Classification, Rotation Forests, Deep Convolutional Generative Adversarial Networks, Medical Imaging

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