This book presents new theories and working models in the areas of data analytics and learning. The papers included in this book were presented at the third International Conference on Data Analytics and Learning (DAL 2024), which was organized by MGM's College of Engineering Nanded, Maharashtra, India in association with the Department of Studies in Computer Science, University of Mysore, Mysuru, Karnataka, India. The areas covered include pattern recognition, image processing, deep learning, computer vision, data analytics, machine learning, artificial intelligence, and intelligent systems.

Presents research works in the field of data analytics and learning Provides original works presented at DAL 2024 held in Nanded, India Serves as a reference for researchers and practitioners in academia and industry

Autorentext

Dr. D. S. Guru received his B.Sc., M.Sc., and Ph.D. degrees in Computer Science from the University of Mysore, Mysore, India, in 1991, 1993, and 2000. respectively. He is currently Senior Professor of Computer Science at University of Mysore. He was Fellow of BOYSCAST and a visiting research scientist at Michigan State University, USA. He has authored 100+ journals and 300+ peer reviewed conference research papers at international levels. In addition, he also co-authored 3 textbooks and co-edited 8 books. He serves as Associate Editor for journal of pattern recognition. He is a member of a few national expert committee constituted by the University Grants Commission (UGC), India. He has received several prestigious awards, such as Business Tycoons Award in Education, Best Ethical Teacher Award, ARP award and Mysuru district Rajyotsava Puraskar award.

Dr. Archana M. Rajurkar has received her B.E. CSE degree in 1991 and ME Instrumentation in1997 from Dr. BAM University Aurangabad, Maharashtra, and Ph.D. from IIT Roorkee in CSE in 2003. She joined Department of CSE, MGM’s College of Engineering Nanded in 1991. Currently, she is Professor and Head at Department of CSE, MGM’s College of Engineering Nanded, India. She has worked as Principal Investigator for many research projects. She has received around 61.11 Lakhs research grant from various government bodies. She has published more than 100 Research Papers in International Conferences and has written 10 Book Chapters. She has organized several International Conferences. She is Fellow of Institute of Engineers, Member of IEEE and CSI.

Dr. N. Vinay Kumar received his B.Sc., M.S., and Ph.D. degrees in Computer Science and Technology from the University of Mysore, Mysore, India, in 2009, 2012, and 2019, respectively. He is currently working as Senior Machine Learning Engineer at the Standard Chartered GBS, Bangalore, India. He was a DST-INSPIRE Fellow and a DST-SERB awardee for the year 2014 and 2017, respectively. He has authored 30+ journals and peer reviewed conference papers at international levels. He is a life member of several academic bodies and served as program chair and reviewers in many prestigious AI related conferences. His area of research interest covers machine learning, biometrics, deep learning computer vision, data analytics, object recognition, and symbolic data analysis.

Dr. Venkat N. Gudivada received his Ph.D. degree in Computer Science from the Center for Advanced Computer Studies at the University of Louisiana (Lafayette). He is currently Chairperson and Professor in the Computer Science Department at East Carolina University. Prior to this, he was Founding Chair and Professor in the Department of Computer Sciences and Electrical Eng. at Marshall University and worked as a Vice President for Wall Street companies in New York city for over 6 years including Bank of America Merrill Lynch and Golden Source. His previous academic tenure includes work at the University of Michigan, Missouri University of Science and Technology, and Ohio University. His current research interests are in cognitive computing, computational linguistics/NLP, information retrieval, automated question generation, DBMS, and personalization of learning.

Inhalt

Impact the accuracy of AQI predications using Human-AI teaming.- Predicting Stroke Risk: A Machine Learning Approach.- Vehicle Recognition System for Intelligent Traffic System using Feed-Forward Neural Network Techniques.- Optimized Pothole Detection with a Fine-Tuned YOLOv8 Model and Depth Estimation on Indian Roadways using monocular vision method.- Detection of DDOS Attacks in IoT Based Smart Home Networks using ML Algorithms.- Improved Brain Tumor Detection Using an Enhanced VGG-19 Model with Modified Stochastic Gradient Descent Optimizer.- Radial Pulse Pattern Recognition Using Deep Learning.- Full Paper: Improved LinkNet model with Statistical and Deep Feature Extractors for Breast Cancer Detection with Mammogram Image.- Algorithmic Simplicity: A Step Towards Intelligent Analysis.- Leveraging Big Data Analytics in Biomedical Informatics for Early Disease Detection and Personalized Treatment Plans to Improve Health Outcomes and Reduce Healthcare Costs.- AI-based neurological disease detection for early diagnosis.- Innovation In Indian Sign Language For Deaf And Dumb Individual Using Deep Learning.- Segmentation of Dermatoscopic images using wavelet Transform.- LBP features in cattle age estimation: Optimized Feature Engineering Pipeline.- Shape-aware thoracic edge map chest x-ray representation for pulmonary abnormality screening.- Retrieval of Kannada vachanas based on philosophical meaning using machine learning.- Advancing Text-to-Video Models: A study of Schedulers and Learning Techniques in Video Synthesis.- Enhancing Signature Detection from Scanned Documents Using Hybrid Detection Model and Post Processing.- Enhancing Indian Traffic Sign Detection Efficiency with Iterative Training Techniques and Augmentation.- Analysis of Explainable AI Techniques in a Computer Vision-based Maritime Surveillance System.- Adaptive Localization Strategies for Underwater WSNs: The PSO-AUV Hybrid Model.- A Hybrid Transformer Model for Robust Multimodal Emotion Recognition Using Audio and Text Data.- MIVT: Medical-Informed Vision Transformer for Early Epilepsy Diagnosis.