This book includes peer reviewed articles from the 5th International Conference on Data Science, Machine Learning and Applications, 2023, held at the G Narayanamma Institute of Technology and Sciences, Hyderabad on 15-16th December, India. ICDSMLA is one of the most prestigious conferences conceptualized in the field of Data Science & Machine Learning offering in-depth information on the latest developments in Artificial Intelligence, Machine Learning, Soft Computing, Human Computer Interaction, and various data science & machine learning applications. It provides a platform for academicians, scientists, researchers and professionals around the world to showcase broad range of perspectives, practices, and technical expertise in these fields. It offers participants the opportunity to stay informed about the latest developments in data science and machine learning.

Autorentext

Dr. Amit Kumar is a DNA Forensics Professional, Entrepreneur, Engineer, Bioinformatician and an IEEE Volunteer. In 2005 he founded the first Private DNA Testing Company Bio Axis DNA Research Centre (P) Ltd in Hyderabad, India with an US Collaborator. He has vast experience of training 1000+ Crime investigation officers and helped 750+ Criminal and non-criminal cases to reach justice by offering analytical services in his laboratory. His group also works extensively on Genetic Predisposition risk studies of cancers and has been helping many cancer patients from 2012 to fight and win the battle against cancer. Amit was member of IEEE Strategy Development and Environmental Assessment committee (SDEA) of IEEE MGA. He is senior member of IEEE and has been a very active IEEE Volunteer at Section, Council, Region, Technical Societies of Computational Intelligence and Engineering in Medicine and Biology and at IEEE MGA levels in several capacities. He has driven a number of IEEEConferences, Conference leadership programs, Entrepreneurship development workshops, Innovation and Internship related events.

Dr. Vinit Kumar Gunjan is an Associate Professor in the Department of Computer Science & Engineering and Dean of Academic affairs at CMR Institute of Technology Hyderabad (Affiliated to Jawaharlal Nehru Technological University, Hyderabad). He is a Senior Member of IEEE, An active Volunteer of IEEE Hyderabad section; 2023-24 additional treasurer; 2021 additional secretary; 2021 Vice Chairman - IEEE Computational Intelligence Society; volunteered in the capacity of Treasurer, Secretary & Chairman of IEEE Young Professionals Affinity Group & IEEE Computer Society. He was awarded with the outstanding IEEE Young Professional award in 2017 by IEEE Hyderabad Section and Early Career Research Awardee by Depart of Science and Technology, Government of India.

Prof. Sabrina Senatore received her M.S. and Ph.D. degrees in computer science from the University of Salerno, Fisciano, Italy. Since 2005, she has been a Faculty Member with the University of Salerno, where she is currently a Full Professor of Computer Science. Her current research interests include the development and application of intelligent systems based on the combination of techniques from soft computing, computational intelligence, text mining, information retrieval, semantic Web, and intelligent agents. She is the Chair of IEEE CIS Task Force on Intelligent Agents and a member of the IEEE CIS Intelligence Society Task Force: Fuzzy Systems for Web Intelligence.

Prof. Yu-Chen Hu received his Ph.D. in computer science and information engineering from the Department of Computer Science and Information Engineering, National Chung Cheng University, Chiayi, Taiwan. He is currently a Professor with the Department of Computer Science and Information Management, Providence University, Sha-Lu, Taiwan. His research interests include image and signal processing, data compression, information hiding, and data engineering. He is a member of ACM.

Inhalt

.- Attention-Based Recurrent Neural Networks for Medical Image Classification.

.- Evolving ATM Networks for High Volume Transactions.

.- Unraveling Disease-Specific Diffusion Patterns in Human Brain Using Diffusion Tensor Image Analysis.

.- Enhancement of High Angular Resolution Diffusion-Weighted Imaging through Parallel Imaging and Motion Correction.

.- Domain Adaptation using Generative Adversarial Networks for Medical Image Synthesis.

.- Detecting Electromagnetic Vulnerabilities in Networking Topologies.

.- Measuring the Impact of Flexible Communications Soft-ware Design in Network Applications.

.- Analyzing Clinical Data for Improved Diagnosis Using AI/ML Algorithms in Smart Health Care.

.- Exploring Representations Learned via Self-Supervised Transfer Learning for Medical Image Classification.

.- A Comprehensive Study of Convolutional Neural Net-works for Medical Image Segmentation, etc.