Intelligent Vision and Computing

Proceedings of ICIVC 2026, Volume 2
Erscheinungsjahr 2026
ISBN: 978-3-032-42309-2
Verlag: Springer

Buch, Englisch, 685 Seiten, Format (B × H): 155 mm x 235 mm

Reihe: Lecture Notes in Networks and Systems

This book includes selected papers presented at Sixth International Conference on Intelligent Vision and Computing (ICIVC 2026), held at The ICFAI University, Dehradun, India, during June 12–13,  2026. The conference proceedings is a collection of high-quality research articles in the field of intelligent vision and computing. The topics covered in the book are artificial intelligence, machine learning, deep learning, internet of things, information security, embedded systems, cloud computing, quantum computing, bio-inspired intelligence, cyber-physical systems, hybrid systems, intelligence for security, data mining, evolutionary optimization, swarm intelligence, signal processing, blockchain technology, big data applications, natural language processing, data acquisition, storage and retrieval for big data, data representation, processing, imaging sensors technology, features extraction, image segmentation, deep learning, convolutional neural network, biometrics recognition, biomedical imaging, intelligent transport systems, and human–computer interaction.

Zielgruppe

Research

Autoren/Hrsg.

Saha, Apu Kumar

Sharma, Harish

Prasad, Mukesh

Gupta, Puneet Kumar

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

Energy-Efficient Data Aggregation in WSN-Based IoT Systems Using Adaptive Clustering Protocols.- Stock Market Prediction Using Deep Learning and Statistical Models.- A Multimodal Hybrid Recommender System Using Collaborative Filtering and Semantic Textual-Visual Representations.- RepoVisionAI: An AI-Powered Multi-Modal Platform for Repository Architecture Visualization, Code Analysis, and Automated Documentation Generation.- A Vision Based Artificial Intelligence Framework for Quantitative Assessment of Bridge Structural Components.- A Modern Academic Review of Time Series Forecasting Techniques: Statistical Models, Machine Learning, Deep Learning, and Hybrid Architectures.- Exploring the Role of Adversarial Training in Generative Adversarial Networks: A Comprehensive Analysis.- Multimodal Behavioral Signal Analysis for Mental Health Assessment Using Federated Learning and Explainable AI.- An Intelligent Hybrid Fusion Model for Accurate Skin Cancer Diagnosis based on Deep Learning Models.- Questionnaire-Based Premarital Relationship Compatibility Prediction Using Machine Learning Techniques.

Über Autor(innen)

Apu Kumar Saha is working as a professor in the Department of Mathematics, National Institute of Technology Agartala. He completed his M.Sc. degree in Mathematics from Tripura University with gold medal and completed his Ph.D. from NIT Agartala. He is an active researcher for the past fifteen plus years. His fields of research includes Artificial Intelligence, Soft Computing, Evolutionary Computation, Swarm Intelligence, Decision Support System, Waste Management, etc. He has published over one hundred and fifty papers in reputed international journals, more than 40 books and book chapters, and over seventy-five international conferences. Twelve candidates already received Ph.D. under his supervision and several others are currently pursuing Ph.D. under him.
Harish Sharma is an associate professor at Rajasthan Technical University, Kota, in Department of Computer Science & Engineering. He has worked at Vardhaman Mahaveer Open University Kota, and Government Engineering College Jhalawar. He received his B.Tech. and M.Tech. degree in Computer Engg. from Govt. Engineering College, Kota, and Rajasthan Technical University, Kota, in 2003 and 2009, respectively. He obtained his Ph.D. from ABV—Indian Institute of Information Technology and Management, Gwalior, India.
Dr. Mukesh Prasad is an associate professor at the School of Computer Science in the Faculty of Engineering and IT at UTS who has made substantial contributions to the fields of machine learning, artificial intelligence, and the Internet of Things. Mukesh’s research interests also include big data, computer vision, brain-computer interface, and evolutionary computation. He is also working in the evolving and increasingly important field of image processing, data analytics, and edge computing, which promise to pave the way for the evolution of new applications and services in the areas of healthcare, biomedical, agriculture, smart cities, education, marketing, and finance. His research has appeared in numerous prestigious journals, including IEEE/ACM Transactions and conferences, and he has written more than 250 research papers.
Dr. Puneet Kumar Gupta is an Associate Professor at The ICFAI University, Dehradun, where he also serves as the Associate Dean–Research. He earned his Ph.D. in Statistics from the University of Allahabad, Prayagraj. Prior to joining The ICFAI University, Dehradun, he served as an Assistant Statistical Officer in the Uttar Pradesh Planning Department, Lucknow. Dr. Gupta’s primary research interests include load-sharing system models, technology adoption, quantitative techniques, and Bayesian inference. He has published 35+ Scopus-indexed and 20+ Web of Science-indexed research articles in international journals and conference proceedings, with more than 1,300 citations to his scholarly work. He has been recognized for his academic excellence and is a recipient of the Dr. Prem Kansal Gold Medal and the Vice-Chancellor’s Gold Medal for securing first rank in M.Phil. Statistics.