Machine Learning Deep Learning And Computational Intelligence For Wireless Communication


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Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication


Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication

Author: E. S. Gopi

language: en

Publisher: Springer Nature

Release Date: 2021-05-28


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This book is a collection of best selected research papers presented at the Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2020) held during October 22nd to 24th 2020, at the Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, India. The presented papers are grouped under the following topics (a) Machine Learning, Deep learning and Computational intelligence algorithms (b)Wireless communication systems and (c) Mobile data applications and are included in the book. The topics include the latest research and results in the areas of network prediction, traffic classification, call detail record mining, mobile health care, mobile pattern recognition, natural language processing, automatic speech processing, mobility analysis, indoor localization, wireless sensor networks (WSN), energy minimization, routing, scheduling, resource allocation, multiple access, power control, malware detection, cyber security, flooding attacks detection, mobile apps sniffing, MIMO detection, signal detection in MIMO-OFDM, modulation recognition, channel estimation, MIMO nonlinear equalization, super-resolution channel and direction-of-arrival estimation. The book is a rich reference material for academia and industry.

Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication


Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication

Author: E. S. Gopi

language: en

Publisher:

Release Date: 2021


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This book is a collection of best selected research papers presented at the Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2020) held during October 22nd to 24th 2020, at the Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, India. The presented papers are grouped under the following topics (a) Machine Learning, Deep learning and Computational intelligence algorithms (b)Wireless communication systems and (c) Mobile data applications and are included in the book. The topics include the latest research and results in the areas of network prediction, traffic classification, call detail record mining, mobile health care, mobile pattern recognition, natural language processing, automatic speech processing, mobility analysis, indoor localization, wireless sensor networks (WSN), energy minimization, routing, scheduling, resource allocation, multiple access, power control, malware detection, cyber security, flooding attacks detection, mobile apps sniffing, MIMO detection, signal detection in MIMO-OFDM, modulation recognition, channel estimation, MIMO nonlinear equalization, super-resolution channel and direction-of-arrival estimation. The book is a rich reference material for academia and industry.

Proceedings of the International Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication


Proceedings of the International Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication

Author: E. S. Gopi

language: en

Publisher: Springer Nature

Release Date: 2024-04-15


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This book is a collection of best selected research papers presented at the 2nd Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication (MDCWC 2023) held during June 22-24th, 2023, at the National Institute of Technology Tiruchirappalli, India. The presented papers are grouped under the following topics (a) Machine learning, Deep learning and Computational intelligence algorithms (b) Wireless communication systems and (c) Mobile data applications. The topics include the latest research and results in the areas of network prediction, traffic classification, call detail record mining, mobile health care, mobile pattern recognition, natural language processing, automatic speech processing, mobility analysis, indoor localization, wireless sensor networks (WSN), energy minimization, routing, scheduling, resource allocation, multiple access, power control, malware detection, cyber security, flooding attacks detection, mobile apps sniffing, MIMO detection, signal detection in MIMO-OFDM, modulation recognition, channel estimation, MIMO nonlinear equalization, super-resolution channel and direction-of-arrival estimation. The book is a rich reference material for academia and industry.