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Produktbild: Automated Machine Learning (AutoML) for Zero-touch Network and Service Management
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Automated Machine Learning (AutoML) for Zero-touch Network and Service Management

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Beschreibung

Automated Machine Learning (AutoML) for Zero-touch Network and Service Management provides a comprehensive examination of how AutoML techniques are transforming next-generation network operations. As modern communication networks continue to grow in scale, heterogeneity, and complexity, traditional...

Beschreibung

Automated Machine Learning (AutoML) for Zero-touch Network and Service Management provides a comprehensive examination of how AutoML techniques are transforming next-generation network operations. As modern communication networks continue to grow in scale, heterogeneity, and complexity, traditional manual configuration and management approaches have become increasingly impractical. Zero-touch network and service management (ZSM) has therefore emerged as a critical paradigm for enabling autonomous, self-configuring, self-optimizing, and self-healing networks.

This book bridges the gap between machine learning automation and intelligent network management by exploring the role of AutoML in the design, deployment, and operation of zero-touch networks. It presents fundamental concepts, system architectures, and enabling technologies, while addressing key practical challenges such as model selection, hyperparameter optimization, data scarcity, explainability, scalability, and the lifecycle management of machine learning models in real-world operational environments.

Through in-depth discussions, real-world use cases, and emerging research directions, the book offers valuable insights into applying AutoML across network domains, including software-defined networking, network function virtualization, 5G/6G systems, cloud-native services, and edge computing. It serves as both a research reference and a practical guide, it is an essential resource for researchers and practitioners seeking to build intelligent, autonomous, and resilient networked systems.

Portrait

Dr. Prithi Samuel is an Assistant Professor in the Department of Computational Intelligence at SRM Institute of Science and Technology, Chennai. She holds a Ph.D. in information and communication engineering from Anna University and has over 15 years of teaching experience. Her research focuses on automata theory, machine learning, deep learning, computational intelligence, and IoT. She has published extensively in reputed journals, conferences, and leading publishers, and is an active member of IEEE, ACM, ISTE, and IAENG.
Dr. Malathy Sathyamoorthy is an Associate Professor in the Department of Information Technology at KPR Institute of Engineering and Technology, Coimbatore. She received her M.Eng. in computer science and engineering in 2012 and completed her Ph.D. in information and communication engineering from Anna University in 2023. She has published over 25 SCI/Scopus-indexed journal papers, 22 international conference papers, 2 patents, 1 book, and 8 book chapters. Her research interests include wireless sensor networks, networking, security, and machine learning. She is a senior member of IEEE, life member of ISTE and IAENG, a reviewer for Springer Wireless Networks, and serves on editorial and conference boards.
Dr. Rajesh Kumar Dhanaraj is a distinguished Professor at Symbiosis International (Deemed University), Pune, with prior tenure at Galgotias University. He is recognized among the global top 2% scientists by Elsevier and Stanford University, he holds a B.Eng., M.Tech, and Ph.D. in computer science from Anna University. He has authored or edited over 50 books, published more than 100 research papers, and holds 21 patents. His research spans machine learning, cyber-physical systems, and wireless sensor networks. A senior IEEE member, he also contributes as an associate and guest editor to leading journals and serves on the Expert Advisory Panel of Texas Instruments.
Balamurugan Balusamy is a Professor and Chairperson in the School of Engineering and IT at Manipal University Dubai, and an Adjunct Professor at Taylor University, Malaysia. Previously, he served as Professor and Associate Dean (Academics) at Shiv Nadar Institution of Eminence, Delhi-NCR, and as Professor and Director (International Relations) at Galgotias University, India. He has been recognized among the top 2% of most-cited scientists worldwide (2023) by Stanford University in Data Science, AI, and ML. Prof. Balusamy has authored and edited 200+ books and published 200+ high-impact research papers with Springer, Elsevier, and IEEE. His expertise spans engineering education, blockchain, data science, and Industrial IoT, with extensive global academic and industry collaborations. An active academic leader, he has delivered 210+ invited talks and spearheaded 10+ IEEE and ACM international conferences across 15+ countries.

Produktdetails

Einband

Gebundene Ausgabe
Erscheinungsdatum

21.01.2027
Herausgeber Prithi Samuel + weitere
Verlag River Publishers

Produktdetails

Einband

Gebundene Ausgabe
Erscheinungsdatum

21.01.2027
Herausgeber
Verlag River Publishers
Seitenzahl

398
Sprache

Englisch
EAN

9788743810544
Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Automated Machine Learning (AutoML) for Zero-touch Network and Service Management

  • 1. Introduction to Automated Machine Learning (AutoML) in Network and Service Management: Overview and Significance 2. AutoML Techniques and Algorithms for Network and Service Management: Applications and Research Challenges 3. AutoML for Network Performance Monitoring and Predictive Maintenance: Service Assurance and Quality Monitoring 4. Human-centric Design in AutoML Solutions in Automated Systems for Network Management: Research Directions and Challenges 5. Deploying Intrusion Detection Systems with AutoML Techniques in Software-defined Network (SDN) Environments 6. Role of Blockchain with Zero-touch Network and Service Management: Research Challenges and Future Directions 7. Edge Computing and Automated Machine Learning for Zero-touch Networks and Service Management: Applications and Future Trends 8. Quantum-inspired Machine Learning Applications in AI-driven Network Optimization and Zero-touch Networks 9. Federated Learning for Collaborative AutoML in Zero-touch Network and Service Management: Applications and Challenges 10. Neuromorphic Computing and Neuromorphic Hardware for AutoML in Zero-touch Network: Applications and Challenges 11. Empowering 5G and IoT Applications with AutoML for Next-level Connectivity and Automation 12. AutoML-powered Innovations in Software-defined Networking (SDN) and Network Function Virtualization 13. Leveraging Augmented Analytics for Enhanced AutoML Insights and AI-driven Decision Support Systems for Network Management 14. AutoML-driven Fire Detection: A Zero-Touch Network Approach Using CNNs and RCNNs 15. Anticipated Future Developments and Integration of Emerging Tech in AutoML Networks.