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Submit a ProposalMachine Learning and Cognitive Computing for Mobile Communications and Wireless Networks
 | Edited by Krishna Kant Singh, Akansha Singh, Korhan Cengiz and Duc-Nhuong Le
Copyright: 2020 | Expected Pub Date:2020/06/09 ISBN: 9781119640363 | Hardcover | 268 pages | 71 illustrations Price: $195 USD  |
One Line Description
Written in a comprehensive and lucid manner, this book explores the utilization of machine learning techniques like data analytics and cognitive power that will lead to better performance of communication and wireless systems.
Audience
The book is designed for researchers and electronics engineers, computer science engineers, industrial engineers and mechanical engineers (both in academia and industry) working in the field of machine learning, cognitive computing, mobile communication and wireless network system. Because it is an amalgamation of theory, mathematics, and examples of the discussed technologies, this book will also be relevant to all levels of students—from undergraduate and postgraduate to research students—studying computer science or electronics.
The book is designed for researchers and electronics engineers, computer science engineers, industrial engineers and mechanical engineers (both in academia and industry) working in the field of machine learning, cognitive computing, mobile communication and wireless network system. Because it is an amalgamation of theory, mathematics, and examples of the discussed technologies, this book will also be relevant to all levels of students—from undergraduate and postgraduate to research students—studying computer science or electronics.
Description
Communication and network technology has witnessed recent rapid movement with the development of numerous information services and applications. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability, and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication.
Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
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Communication and network technology has witnessed recent rapid movement with the development of numerous information services and applications. These technologies have high impact on society and the way people are leading their lives. The advancement in technology has undoubtedly improved the quality of service and user experience yet a lot needs to be still done. Some areas that still need improvement include seamless wide-area coverage, high-capacity hot-spots, low-power massive-connections, low-latency and high-reliability, and so on. Thus, it is highly desirable to develop smart technologies for communication to improve the overall services and management of wireless communication.
Machine learning and cognitive computing have converged to give some groundbreaking solutions for smart machines. With these two technologies coming together, the machines can acquire the ability to reason similar to the human brain. The research area of machine learning and cognitive computing cover many fields like psychology, biology, signal processing, physics, information theory, mathematics, and statistics that can be used effectively for topology management. Therefore, the utilization of machine learning techniques like data analytics and cognitive power will lead to better performance of communication and wireless systems.
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Author / Editor Details
Krishna Kant Singh is an Associate Professor in Electronics and Communications Engineering in KIET Group of Institutions, Ghaziabad, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of machine learning and remote sensing. He has authored more than 50 technical books and research papers in international conferences and SCIE journals.
Krishna Kant Singh is an Associate Professor in Electronics and Communications Engineering in KIET Group of Institutions, Ghaziabad, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of machine learning and remote sensing. He has authored more than 50 technical books and research papers in international conferences and SCIE journals.
Akansha Singh is an Associate Professor in Department of Computer Science Engineering in Amity University, Noida, India. Dr. Singh has acquired BTech, MTech, and PhD (IIT Roorkee) in the area of neural network and remote sensing. She has authored more than 40 technical books and research papers in international conferences and SCIE journals. Her area of interest includes Mobile Computing, Artificial Intelligence, Machine Learning, Digital Image Processing.
Korhan Cengiz received his PhD in Electronics Engineering from Kadir Has University, Istanbul, Turkey, in 2016. He has served as keynote speakers at many conferences. His research interests include wireless sensor networks, routing protocols, wireless communications, 5G systems, statistical signal processing, and spatial modulation.
Dac-Nhuong Le has a MSc and PhD in computer science from Vietnam National University, Vietnam in 2009 and 2015, respectively. He is Associate Professor in Computer Science, Deputy-Head of Faculty of Information Technology, Haiphong University, Vietnam. He has a total academic teaching experience of 12+ years with many publications in reputed international conferences, journals and online book chapters. His area of research includes: evaluation computing and approximate algorithms, network communication, security and vulnerability, network performance analysis and simulation, cloud computing, IoT and image processing in biomedical.
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