Combining Visual Intelligence and Federated Learning in Smart Healthcare

Combining Visual Intelligence and Federated Learning in Smart Healthcare

Pages: 300
DOI: 10.4018/979-8-3693-6094-1
ISBN13: 9798369360941|ISBN13 Softcover: 9798369360958|EISBN13: 9798369360965
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Description & Coverage
Description:

Smart devices in healthcare can interact with the environment by gathering, processing, interpreting, storing, and retrieving information originated from sensors, neuromorphic analog circuits, robots, and other data retrieving sources through explainable AI, Internet of Things, Gestural technology, federated learning. These systems can exploit Visual Languages to improve communication with people in real-life scenarios, such as intelligent devices which Recognize patterns easily, sharpens the perception. This technology aims to transforms images from the original grayscale and reveals hidden patterns, into new well-defined structured patterns empowering research community, and computer analytics to remove indicative uncertainty. Such languages, combined with intelligent, experience-based, healthcare systems, fall in the area of Visual Intelligence, empowering people to understand how machines process the data smart healthcare devices. Moreover, Visual Intelligence comprises all the processes enabling machines to analyze and interpret the data. The combination of smart healthcare and Visual Intelligence with federated learning has given rise to exciting new applications in fields as diverse as healthcare, education, marketing, games, and automotive. For example, instead of relying only on textual data to create a self-driving car, customers can provide data about the routes they take in a day along with images or videos. Thus, people can help intelligent systems to be trained on a variety of new multimedia sources. Aims and scope, plus topics of interest.

Anchored at healthcare applications, Visual Intelligence focuses on research areas that connect visual information processing with intelligence, federated learning, and the Internet of things, promoting their integration and exciting new developments. Analog VLSI circuits for the perception of visual motion is a unique reference for advanced students in electrical engineering, artificial intelligence, robotics, and computational neuroscience.

This special issue invites research papers/letters/ review articles from all over the globe to publish leading international research on the theories, practices, and experiences in the field of smart healthcare for visual intelligence with federated learning. Through smart healthcare for visual intelligence, it is expected to improve teaching and learning practices.

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Editor/Author Biographies

Dr. Manisha Guduri is currently a visiting scholar at the University of Louisiana at Lafayette, USA. She is the author/ coauthor of more than 52 research papers in reputed journals, book chapters, and international conferences. Her research interests include Artificial Intelligence, Biomedical Applications, VLSI/CAD design. She is currently working on VLSI and AI in the biomedical field. She published 5 patents out of which 2 are under FER. She received one patent grant. She is the reviewer of IEEE TVLSI, Microelectronics Journal, IET digital circuits, IEEE Journal of Biomedical and Health Informatics, etc. She has one on-going funded project from the Department of Science and Technology.

She is a senior member of IEEE, USA. She is also currently member of various IEEE Societies such as IEEE Young Professionals, IEEE Women in Engineering, Circuits and Systems, Computer Society, Sensor Council, etc. She is appointed as IEEE WiE CASS representative for 2023 & 2024. She is executive committee member in Women in Engineering Affinity Group IEEE Hyderabad Section 2022. She is IEEE WiE DL program Coordinator and IEEE Computer Society Lafayette section Vice Chair for 2024. She has delivered more than 35 invited talk/tutorial speech/expert talk in various platforms like International Conference /technical programs. She has organized 10 international conferences under different roles.

Chinmay Chakraborty is a Senior Assistant Professor in the Dept. of Electronics and Communication Engineering, BIT Mesra, Jharkhand, India. His primary areas of research include WBAN, Internet of Medical Things, Medical Imaging and Telemedicine. Prior to BIT, he worked at the Faculty of Science and Technology, ICFAI University, Agartala, India as a Sr. Lecturer. He worked as a Research Consultant in the Coal India project at Industrial Engineering & Management, IIT Kharagpur. He worked as a project coordinator of Telecom Convergence Switch project under the Indo-US joint initiative. He also worked as a Network Engineer in System Administration at MISPL under Global Teleservice Ltd., India. He is the author of “PSTN-IP Telephony Gateway for Ensuring QoS in Heterogeneous Networks” (2014) and “Advanced Classification Techniques for Healthcare Analysis” (2019). He is an Editorial Board Member in the Journal of Wireless Communication Technology, Int. Journal of Telecomm. Engg. etc. and also a member of the International Advisory Board for Malaysia Technical Scientist Congress and the MIR Labs. He has been also editing four books on IGI, IET, CRC, and Pan Standford Publishing. He is a guest editor of Future Internet journal special issue (Internet of Healthcare Things (IoHT): Methods, Advances, and Applications) and SoCTA-19 Int. Conf., Patna on Computational Intelligence and Bioinformatics for Smart Healthcare Technology. He received a young research excellence award, Global Peer Review Award, Young Faculty Award, and Outstanding Researcher Award. He was the speakers for AICTE, DST sponsored FDP and CEP Short Term Courses.

Prof. Martin Margala, PhD joined the School of Computing and Informatics as Professor and Director in August 2021. Before joining UL Lafayette, from September 2011 to July 2021, Dr. Margala was Professor and Chair of the Electrical and Computer Engineering Department at the University of Massachusetts Lowell and a Co-Director of the Center for Smart Cyber-Physical Systems (SCyPS). He received his PhD degree in Electrical and Computer Engineering from the University of Alberta, Canada (#61 in Global Ranking in North America region; #13 in Global subject specific ranking Electrical and Electronic Engineering in North America region USNews) in the spring of 1998. He is a senior member of ACM, IEEE, and SPIE with more than 50 journal and 200 peer reviewed conference publications in the areas of Design for Testability for Energy Efficient Architectures and Systems, High-Performance Reliable Low-Power Architectures and Reconfigurable Secure Architectures and Systems. Dr. Margala has directed 22 PhD students and 19 MS students, many of whom now hold leading positions in academia and industry. He has served on numerous program committees of international conferences and on workgroups (such as the International Technology Roadmap for Semiconductors) that have a great impact on the future direction of academia and industry.

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