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240503 ||| eng |
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|a 1119812712
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|a 1119812704
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|a 9781119812715
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|a 1119812720
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|a 9781119812722
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|a 9781119812708
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|a TK5103.2
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| 100 |
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|a Gupta, Deepak
|e editor
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| 245 |
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|a AI-enabled 6G networks and applications
|c edited by Deepak Gupta, Dr. Mahmoud Ragab, Romany Fouad Mansour, Aditya Khamparia, Ashish Khanna
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| 260 |
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|a Hoboken, NJ
|b John Wiley & Sons, Inc.
|c 2023
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| 300 |
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|a xxii, 154 pages
|b illustrations (chiefly color)
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| 505 |
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|a Includes bibliographical references and index
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|a Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt
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|a 1Postdoctoral Fellow, Federal Institute of Education, Science, and Technology of Ceara (IFCE), Fortaleza, Ceara, Brazil. -- 2Associate Professor, Department of Computer Science & Engineering, GL Bajaj Institute of Technology & Management, Knowledge Park-3, Greater Noida (U.P.) India- 201306 -- 3Graduate Program in Telecommunication Engineering, Federal Institute of Education, Science and Technology of Cear,̀ Fortaleza/CE, Brazil. -- 4Meteora, Fortaleza/CE, Brazil -- 5Department of Teleinformatics Engineering, Federal University of Cear,̀ Fortaleza/CE, Brazil -- -- Chapter 6 -- Adaptive Whale Optimization with Deep Learning Enabled RefineDet Network for Vision Assistance on 6G Networks -- 1,2Vinita Malik, 3,4Marcello Carvalho dos Reis, 5Victor Hugo C. de Albuquerque -- 1Postdoctoral Fellow, Federal Institute of Education, Science, and Technology of Ceara (IFCE), Fortaleza, Ceara, Brazil. -- 2Information Scientist, Central University of Haryana, Haryana, India- 201306 --
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|a Notes on Contributors -- Preface -- About the Editors -- -- Chapter 1 -- Metaheuristic Moth Flame Optimization Based Energy Efficient Clustering Protocol for 6G Enabled Unmanned Aerial Vehicle Networks -- Adnen El Amraoui -- Associate Professor, Univ. Artois, U.R. 3926 -- Laboratoire de Génie Informatique et d'Automatique de l'Artois (LGI2A), Béthune, France -- -- Chapter 2 -- A Novel Data Offloading with Deep Learning Enabled Cyberattack Detection Model for Edge Computing in 6G Networks -- Elsaid M. Abdelrahim -- Department of Mathematics Computer Science Division, Faculty of Science, Tanta University, Tanta, Egypt. -- -- Chapter 3 -- Henry Gas Solubility Optimization with Deep Learning Enabled Traffic Flow Forecasting in 6G Enabled Vehicular Networks -- 1,2José Escorcia-Gutierrez, 3Melitsa Torres-Torres, 4Kelvin Beleño, 5Carlos Soto -- 1Electronics and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 08001, Colombia --
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| 505 |
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|a 3Graduate Program in Telecommunication Engineering, Federal Institute of Education, Science and Technology of Cear,̀ Fortaleza/CE, Brazil. -- 4Meteora, Fortaleza/CE, Brazil -- 5Department of Teleinformatics Engineering, Federal University of Cear,̀ Fortaleza/CE, Brazil -- -- Chapter 7 -- Efficient Deer Hunting Optimization Algorithm based Spectrum Sensing Approach for 6G Communication Networks -- R. Pandi Selvam, Kanagaraj Narayanasamy, M. Ilayaraja -- PG Department of Computer Science, Vidhyaa Giri College of Arts & Science, Karaikudi- 630 108, India -- Department of Computer Applications, J.J. College of Arts and Science (Autonomous), Pudukkottai- 622 422, India -- School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, India -- -- Chapter 8 -- Elite Oppositional Hunger Games Search Optimization based Cooperative Spectrum Sensing Scheme for 6G Cognitive Radio Networks -- Emad A-B Abdel-Salam, Ayman M. Mahmoud, Romany F. Mansour --
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|a 2Research Center - CIENS, Escuela Naval de Suboficiales A.R.C. "Barranquilla", Barranquilla, Colombia -- 3Research group IET-UAC,Universidad Autónoma del Caribe, 08001 Barranquilla, Colombia -- 4Mechatronics Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 08001, Colombia -- 5Mechanical Engineering Program, Universidad Autónoma del Caribe, Barranquilla, 08001, Colombia -- -- Chapter 4 -- Crow Search Algorithm based Vector Quantization Approach for Image Compression in 6G Enabled Industrial Internet of Things Environment -- Maha M. Althobaiti -- Department of Computer Science, College of Computing and Information technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia. -- -- Chapter 5 -- Design of Artificial Intelligence Enabled Dingo Optimizer for Energy Management in 6G Communication Networks -- 1,2Pooja Singh, 3,4Marcello Carvalho dos Reis, 5Victor Hugo C. de Albuquerque --
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| 653 |
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|a Artificial intelligence / fast
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|a Mobile communication systems / http://id.loc.gov/authorities/subjects/sh85086371
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|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
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|a Intelligence artificielle
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|a Radiocommunications mobiles
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|a artificial intelligence / aat
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|a Mobile communication systems / fast
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| 700 |
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|a Ragab, Mahmoud
|e editor
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| 700 |
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|a Mansour, Romany Fouad
|e editor
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|a Khamparia, Aditya
|e editor
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 024 |
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|a 10.1002/9781119812722
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| 776 |
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|z 1119812712
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| 776 |
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|z 1119812704
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| 776 |
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|z 9781119812715
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| 776 |
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|z 9781119812708
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| 776 |
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|z 9781119812722
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| 776 |
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| 776 |
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|z 1119812720
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781119812647/?ar
|x Verlag
|3 Volltext
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|a 384
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|a 621.384
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|a "This book includes review of AI techniques for 6G Networks and will focus on deployment of AI techniques to efficiently and effectively optimize the network performance, including AI-empowered mobile edge computing, intelligent mobility and handover management, and smart spectrum management. This book also includes the design of a set of evolutionary AI hybrid algorithms with communication protocols, showing how to use them in practice to solve problems relating to vehicular networks, aerial networks and communication networks. It is intended as a reference guide to advanced hybrid computational intelligence methods for 6G supportive networks and protocols for graduate students and researchers in network forensics and optimization, computer science, and engineering. The chapters have been written and edited by active researchers, which cover hypotheses and practical considerations; provide insights into the design of evolutionary AI algorithms for 6G networks with focuses on network transparency, interpretability and simulatability for vehicular networks, space systems, surveillance systems and their usages in different emerging engineering fields."--
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