Distributed Time-Sensitive Systems
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Distributed Time-Sensitive Systems offers a comprehensive array of pioneering advancements across various sectors within Society 5.0, underpinned by cutting-edge technological innovations. This volume delivers an exhaustive selection of contemporary concepts, practical applications, and groundbreaking implementations that stand to enhance diverse facets of societal life. The chapters encompass detailed insights into fields such as image processing, natural language processing, computer vision, sentiment analysis, and voice and gesture recognition and feature interdisciplinary approaches spanning legal frameworks, medical systems, intelligent urban development, integrated cyber-physical systems infrastructure, and advanced agricultural practices.
The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramatically reshaped the requirements for control and communication systems in the factory systems of the future. This revolution strongly affects industrial smart and distributed measurement systems, pointing to more integrated and intelligent equipment devoted to deriving accurate measurements. This volume explores critical cybersecurity analysis and future research directions for the Internet of Things, addressing security goals and solutions for IoT use cases. The interdisciplinary nature and focus on pioneering advancements in distributed time-sensitive systems across various sectors within Society 5.0 make this thematic volume a unique and valuable contribution to the current research landscape.
Audience
Researchers, engineers, and computer scientists working with integrations for industry in Society 5.0
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Tanupriya Choudhury, PhD, is a professor and an Associate Dean of Research at Graphic Era University, Dehradun, India with over 15 years of teaching and research experience. He has published 15 books, filed 25 patents, and developed 16 software programs for India’s Ministry of Human Resource Development in addition to serving as a reviewer and editor for several international journals and conferences. His areas of expertise include human computing, soft computing, cloud computing, and data mining.
Rahul Kumar Singh, PhD, is an assistant professor at the University of Petroleum and Energy Studies in Dehradun, India, with a decade of teaching experience. He has published numerous papers in international journals, presented at international conferences, and serves as a reviewer for leading AI journals. His research interests include neurosymbolic AI for enhancing natural language processing for sentiment analysis, dialogue systems, and financial forecasting, affective computing, and theoretical computer science.
Ravi Tomar, PhD, is a senior architect at Persistent Systems, India with a history in the higher education industry. He has delivered training to national and international corporations on confluent Apache Kafka, stream processing, CordaApp, J2EE, and IoT to clients like KeyBank, Accenture, Union Bank of Philippines, Ernst and Young, and Deloitte. His research interests include wireless sensor networks, image processing, data mining and warehousing, computer networks, and big data.
S. Balamurugan, PhD, is the Director of Research and Development at Intelligent Research Consultancy Services in Coimbatore, India with a history in higher education. He has contributed 45 books and over 200 papers to international journals and conferences, secured 35 patents, and holds editorial roles across more than 500 international scientific publications. His research interests include Artificial Intelligence, wearable computing, machine learning, and deep learning.
J. C. Patni, PhD, is a professor at the Symbiosis Institute of Technology Nagpur, Symbiosis International University, India with more than 18 years of teaching and administrative experience. He has authored over 80 research articles in national and international journals and conferences and over ten books and book chapters, has been granted seven Indian patents, and has filed an additional 21 patents. His research interests include Artificial Intelligence, machine learning, deep learning, high-performance computing, and software engineering.
