Tiny Machine Learning Techniques for Constrained Devices (Hardcover)
Language: English
Published by Taylor & Francis Ltd, 2026
- Hardcover
- New
Seller: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
AbeBooks seller since June 22, 2007
Condition: New
£ 157.25
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Add to basketItem description from seller
Hardcover. Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of Tiny Machine Learning (TinyML), enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge Internet of Things (IoT) nodes. This book provides a comprehensive guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments.This book offers thorough coverage of key topics, including:Foundations and Optimization of TinyML: Covers microcontroller-centric power optimization, core principles, and algorithms essential for deploying efficient machine learning models on embedded systems with strict resource constraints.Applications of TinyML in Healthcare and IoT: Presents innovative use cases such as compact artificial intelligence (AI) solutions for healthcare challenges, real-time detection systems, and integration with low-power IoT and low-power wide-area network (LPWAN) technologies.Security and Privacy in TinyML: Addresses the unique challenges of securing TinyML deployments, including privacy-preserving techniques, blockchain integration for secure IoT applications, and methods for protecting resource-constrained devices.Emerging Trends and Future Directions: Explores the evolving landscape of TinyML research, highlighting new applications, adaptive frameworks, and promising avenues for future investigation.Practical Implementation and Case Studies: Offers hands-on insights and real-world examples demonstrating TinyML in action across diverse scenarios, providing guidance for engineers, researchers, and students.This book is an essential resource for embedded system designers, AI practitioners, cybersecurity professionals, and academics who want to harness the power of TinyML for smarter, more efficient, and secure edge intelligence solutions. Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of TinyML, enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge IoT nodes. It is a guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…
Seller Inventory # 9781032897523
- Title
- Tiny Machine Learning Techniques for Constrained Devices (Hardcover)
- Author
- Khalid El-Makkaoui
- Publisher
- Taylor & Francis Ltd
- Publication year
- 2026
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 103289752X
- ISBN 13
- 9781032897523
Tiny Machine Learning Techniques for Constrained Devices explores the cutting-edge field of Tiny Machine Learning (TinyML), enabling intelligent machine learning on highly resource-limited devices such as microcontrollers and edge Internet of Things (IoT) nodes. This book provides a comprehensive guide to designing, optimizing, securing, and applying TinyML models in real-world constrained environments.
This book offers thorough coverage of key topics, including:
- Foundations and Optimization of TinyML: Covers microcontroller-centric power optimization, core principles, and algorithms essential for deploying efficient machine learning models on embedded systems with strict resource constraints.
- Applications of TinyML in Healthcare and IoT: Presents innovative use cases such as compact artificial intelligence (AI) solutions for healthcare challenges, real-time detection systems, and integration with low-power IoT and low-power wide-area network (LPWAN) technologies.
- Security and Privacy in TinyML: Addresses the unique challenges of securing TinyML deployments, including privacy-preserving techniques, blockchain integration for secure IoT applications, and methods for protecting resource-constrained devices.
- Emerging Trends and Future Directions: Explores the evolving landscape of TinyML research, highlighting new applications, adaptive frameworks, and promising avenues for future investigation.
- Practical Implementation and Case Studies: Offers hands-on insights and real-world examples demonstrating TinyML in action across diverse scenarios, providing guidance for engineers, researchers, and students.
This book is an essential resource for embedded system designers, AI practitioners, cybersecurity professionals, and academics who want to harness the power of TinyML for smarter, more efficient, and secure edge intelligence solutions.
"Synopsis" may belong to another edition of this title.
About the Author
Prof. Khalid El Makkaoui is an Associate Professor with the Department of Computer Science at the Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco. His research interests focus on cybersecurity and artificial intelligence. He has published over 40 papers (book chapters, international journals, and conferences).
Dr. Ismail Lamaakal is currently advancing towards his Ph.D. in Computer Science at the Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco. As an Artificial Intelligence Scientist, his research primarily focuses on the innovative integration of Tiny Machine Learning, the Internet of Things (IoT), Human-computer interaction, and Embedded Systems.
Prof. Ibrahim Ouahbi is a professor of computer science at the Multidisciplinary Faculty of Nador, University Mohammed Premier, Oujda, Morocco. His research interests include artificial intelligence, cybersecurity, and ICT integration in science education and learning.
Prof. Yassine Maleh is a PhD of the University Hassan 1st in Morocco in the field of Internet of Things Security and privacy, since 2013. He is Senior Member of IEEE, Member of the International Association of Engineers IAENG and The Machine Intelligence Research Labs. He has published over than 50 papers, 4 edited books and 1 authored book.
Prof. Ahmed A. Abd El-Latif (Senior Member, IEEE) is a Professor at Menoufia University, Egypt, and holds academic positions at Prince Sultan University, Saudi Arabia. He earned his Ph.D. (2013) from the Harbin Institute of Technology, China. He has authored over 350 publications in prestigious journals and conferences and has received multiple awards for his contributions.
"About the title" may belong to another edition of this title.
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