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Umaselvi, M.
- A Study on Quality of Work Life as Perceived by College Teachers
Authors
1 Majan University College, Sultanate of Oman, OM
2 Hallmark Business School, Tiruchirappalli–620 102, IN
Source
Digital Image Processing, Vol 1, No 7 (2009), Pagination: 304-319Abstract
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.- Dementia Disease Classification With Rotation Forests Based DCGAN
Authors
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
Source
ICTACT Journal on Image and Video Processing, Vol 14, No 1 (2023), Pagination: 3055-3059Abstract
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 ImagingReferences
- T. Grimmer and A. Drzezga, “Clinical Severity of Alzheimer's Disease is associated with PIB uptake in PET”, Neurobiology of Aging, Vol. 30, No. 12, pp. 1902-1909, 2009.
- B. Subramanian, T. Gunasekaran and S. Hariprasath, “Diabetic Retinopathy-Feature Extraction and Classification using Adaptive Super Pixel Algorithm”, International Journal on Engineering Advanced Technology, Vol. 9, pp. 618-627, 2019.
- D. Irfan, S. Srivastava and V. Saravanan, “Prediction of Quality Food Sale in Mart using the AI-Based TOR Method”, Journal of Food Quality, Vol. 2022, pp. 1-12, 2022.
- L. Vaisvilait and K. Specht, “Time-of-Day Effects in Resting-State Functional Magnetic Resonance Imaging: Changes in Effective Connectivity and Blood Oxygenation Level Dependent Signal”, Brain Connectivity, Vol. 12, No. 6, pp. 515-523, 2022.
- S.A. Mofrad and A. Lundervold, “Alzheimer's Disease Neuroimaging Initiative A Predictive Framework based on Brain Volume Trajectories Enabling Early Detection of Alzheimer's Disease”, Computerized Medical Imaging and Graphics, Vol. 90, pp. 1-13, 2022.
- G. Kiruthiga, “Improved Object Detection in Video Surveillance using Deep Convolutional Neural Network Learning”, International Journal for Modern Trends in Science and Technology, Vol. 7, No. 11, pp. 108-114, 2021.
- K.N.G. Veerappan, J. Perumal and S.J.N. Kumar, “Categorical Data Clustering using Meta Heuristic Link-Based Ensemble Method: Data Clustering using Soft Computing Techniques”, Proceedings of IEEE International Conference on Dynamics of Swarm Intelligence Health Analysis for the Next Generation, pp. 226-238, 2023.
- S. Buyrukoglu, “Early Detection of Alzheimer’s Disease using Data Mining: Comparison of Ensemble Feature Selection Approaches”, Konya Journal of Engineering Sciences, Vol. 9, No. 1, pp. 50-61, 2021.
- G. Battineni and F. Amenta, “Improved Alzheimer’s Disease Detection by MRI using Multimodal Machine Learning Algorithms”, Diagnostics, Vol. 11, No. 11, pp. 2103-2109, 2021.