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Feature Fusion for Next-Generation AI (eBook)

Building Intelligent Solutions from Medical Data
Artikelnummer: 978-3-031-94386-7
Einband: PDF
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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.

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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VerlagSpringer Nature Switzerland
EinbandPDF
Erscheinungsjahr2025
Seitenangabe193 S.
AusgabekennzeichenEnglisch
AbbildungenXV, 193 p. 75 illus., 61 illus. in color.
Masse16'569 KB
PlattformPDF
ReiheSustainable Artificial Intelligence-Powered Applications; Artificial Intelligence
AutorNag, Anindya (Hrsg.) / Hassan, Md. Mehedi (Hrsg.) / Bairagi, Anupam Kumar (Hrsg.)

Alle Bände der Reihe "Sustainable Artificial Intelligence-Powered Applications; Artificial Intelligence (R0)"

Über den Autor Anindya (Hrsg.) Nag

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, Khulna 9100, Bangladesh. His research focuses on health informatics, medical Internet of Things, neuroscience, and machine learning. He serves as a reviewer for numerous prestigious journals and international conferences. He has authored and co-authored about 60 publications, including journal articles, conference papers, and book chapters, and has co-edited books.Md. Mehedi Hassan is currently a Ph.D. researcher in STEM (Computer and Information Science) at the University of South Australia, working on a fully funded research project. He started his Ph.D. in 2025, building on a strong academic background in computer science and engineering. He holds an M.Sc. in Computer Science and Engineering from Khulna University, Bangladesh, completed in 2024, and a B.Sc. in Computer Science and Engineering from North Western University, completed in 2022. His research spans computer science engineering and data science, with a strong focus on predictive analysis and expert system development. Md. Mehedi Hassan has authored 73 research papers and edited 5 books, actively contributing to the academic community. He serves as a peer reviewer for over 80 prestigious journals and collaborates extensively in interdisciplinary research. As a trainer for the VCourse platform, he has educated over 250 students over the past two years, sharing knowledge in emerging technologies and research methodologies. Beyond publications, he has actively engaged in intellectual property development, with several patents filed and three already granted in his name. His current research interests include computational neuroscience, machine learning for healthcare, and predictive modeling for biometrics.Dr. Riya Sil is an Associate Professor in Department of Computer Science & Engineering, Brainware University, Kolkata, India. Dr. Riya Sil holds a B.Tech & M.Tech degree in Computer Science & Engineering from Birla Institute of Technology and a PhD degree with a specialization in Legal Analytics. She has a total professional experience of about 9 years, including 7 years in academics and 2 years as Software Developer at Cognizant Technology Solutions (CTS). During her academic tenure, she has served as Asst. Professor in Computer Science & Engineering department in various institutions like Kristu Jayanti College - Autonomous, Techno India University, and Adamas University. Her research focuses on Machine Learning; Natural Language Processing (NLP); Deep Learning; Artificial Intelligence (AI) and Cloud Computing. She serves as a reviewer for various reputable journals and international conferences. She has about 40 publications to her credit which include peer-reviewed SCI and Scopus-indexed journals, conferences and book chapters.Dr. Asif Karim currently works at the Faculty of Science and Technology, Charles Darwin University. Asif does research in Machine Learning based Health Informatics and Blockchain Applications. He has considerable Industry Experience in the field of IT, primarily in Software Engineering. Complete Information about his Research Publications, Teaching and Grants are available at https://asifkarim.com/.

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