Feature Fusion for Next-Generation AI
Description
This book delves into the fundamental concepts, methodologies, and practical implementations of feature fusion, providing valuable perspectives on how merging several data aspects might augment the decision-making skills of artificial intelligence. Feature fusion is inherently connected to the advancement of intelligent solutions from medical data as it enables the incorporation of various and complementary data sources to construct more advanced AI models. Within the medical domain, data manifests in diverse formats, including electronic health records (EHRs), medical imaging, genomic data, and real-time sensor metrics. Although each of these data kinds offers distinct perspectives, they may have limitations in terms of their breadth or depth when considered independently. The application of feature fusion enables the integration of diverse data sources into a unified model, hence improving the AI's capacity to detect patterns, make precise predictions, and produce significant insights. The fusion process facilitates the development of intelligent solutions that exhibit enhanced reliability and effectiveness by using a more extensive reservoir of knowledge. For example, an artificial intelligence system that combines imaging data with clinical history might enhance the precision of disease diagnosis, forecast patient outcomes, and suggest tailored treatment strategies. Feature fusion is the crucial factor in unleashing the complete capabilities of medical data, enabling artificial intelligence to provide intelligent solutions that not only enhance the provision of healthcare but also stimulate advancements in medical research and practice. The proposed book explores the advanced notion of feature fusion within the field of artificial intelligence, with a particular emphasis on its implementation in physiological data. The integration of many data sources is crucial in the development of more precise, dependable, and understandable AI models as the healthcare industry becomes more data-driven.
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Anindya Nag obtained an M.Sc. in Computer Science and Engineering from Khulna University in Khulna, Bangladesh, and a B.Tech. in Computer Science and Engineering from Adamas University in Kolkata, India. He is currently a Lecturer in the Department of Computer Science and Engineering at the Northern University of Business and Technology in Khulna, Bangladesh. His research focuses on health informatics, medical Internet of Things, neuro-science, and machine learning. He serves as a reviewer for numerous prestigious journals and international conferences. He has authored and co-authored about 36publications, including journal articles, conference papers, book chapters, and has co-edited 7 books.
Md. Mehedi Hassan (Member, IEEE) is a dedicated and accomplished researcher, completed the Master of Science (M.Sc.) degree in computer science and engineering at Khulna University, Khulna, Bangladesh. Mehedi completed his BSc degree in Computer Science and Engineering from North Western University, Khulna in 2022. As the founder and CEO of The Virtual BD IT Firm and VRD Research Laboratory, Bangladesh, Mehedi has established himself as a highly respected leader in the fields of biomedical engineering, data science, and expert systems. As a young researcher, Mehedi has published 52 articles and 2 books in various international top journals and conferences, which is a remarkable achievement. His accomplishments to date are impressive, and his potential for future contributions to his field is very promising. Additionally, he serves as a reviewer for 56 prestigious journals. He has filed more than 3 patents out of which 2 are granted to his name.
Anupam Kumar Bairagi, PhD is a professor in the discipline of Computer Science and Engineering, at Khulna University, Bangladesh. He received his Ph.D. degree in Computer Engineering from Kyung Hee University, South Korea, and his B.Sc. and M.Sc. degree in Computer Science and Engineering from Khulna University, Bangladesh. His research interests include wireless resource management in 5G, game theory, Health Informatics, IIoT, Agri Informatics, etc. He obtained the Vice Chancellor's Award in 2023 for his contribution in research and academic excellence. He is a senior member of IEEE.
Content
Fundamental Principles of Feature Fusion in Medical AI.- Data Preprocessing for Feature Synthesis in Medical AI.- Techniques for Selecting Features in Medical Data.- Dimensionality Reduction Techniques: Foundations and Applications in Medical Data Analysis.- Meta-Heuristic Algorithms for High-Dimensional Feature Selection.