{"product_id":"tiny-machine-learning-fundamentals-applications-and-security-hardback-9781394347094","title":"Tiny Machine Learning; Fundamentals, Applications, and Security (Hardback) 9781394347094","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eTiny Machine Learning\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eFundamentals, Applications, and Security\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eRajdeep Chakraborty (Edited by), R Chakraborty (Author), Rana Majumdar (Edited by), S. Balamurugan (Edited by), Sheng-Lung Peng (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394347094, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 29 June 2026\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e528 pages\u003cbr\u003e22.9 x 15.2 x 3.2 cm, 0.851 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eStay at the forefront of the embedded AI revolution by mastering the specialized hardware and software strategies needed to bring high-performance machine learning to the world’s most resource-constrained devices.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eTinyML (tiny machine learning), short for tiny machine learning, represents a groundbreaking intersection of machine learning and embedded systems, enabling the deployment of intelligent applications on resource-constrained devices. It empowers these devices to perform complex tasks, like image and speech recognition, locally without relying on cloud servers. This burgeoning field opens up many possibilities, from enhancing IoT devices to revolutionizing healthcare and intelligent infrastructure. As technology advances, TinyML promises to make our everyday devices more innovative, responsive, and efficient than ever before. By bringing inference to resource-constrained hardware, TinyML supports real-time decision-making while addressing critical concerns such as latency, power consumption, and data privacy. This book presents an overview of TinyML, including its core principles, applications, challenges, and future directions. It meticulously explores the fundamentals of machine learning and deep learning, providing a solid foundation for understanding how these techniques are adapted for tiny devices. By delving into the hardware, software, and algorithms that specifically cater to TinyML, the book addresses the unique challenges of running machine-learning models on devices with limited processing power and memory. Featuring expert insights and real-world case studies, this volume is an essential guide to researchers and industry professionals looking for solutions for today’s resource-constrained devices. \u003c\/p\u003e\n\u003cp\u003eReaders will find the volume: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDelves into the burgeoning field of TinyML, where the power of machine learning is harnessed for resource-constrained devices;\u003c\/li\u003e \u003cli\u003eServes as a comprehensive guide, equipping readers with the essential knowledge to develop and deploy TinyML applications;\u003c\/li\u003e \u003cli\u003eExplores the fundamentals of machine learning and deep learning, providing a solid foundation for understanding how these techniques are adapted for tiny devices;\u003c\/li\u003e \u003cli\u003eIntroduces the hardware, software, and algorithms that specifically cater to TinyML, addressing the unique challenges of running machine-learning models on devices with limited processing power and memory.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eEngineers, academics, researchers, and professionals in computer science, information technology, and electronics and communication.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I: Fundamentals 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 The Basics of TinyML: An Introductory Exploration 3\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDarothi Sarkar and Monalisa Dey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction to TinyML 4\u003cbr\u003e1.2 Technological Underpinnings of TinyML 6\u003cbr\u003e1.3 Real-World Applications of TinyML 11\u003cbr\u003e1.4 Challenges and Limitations of TinyML 14\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Advances in TinyML: A Systematic Review of Architectures, Algorithms, and Innovations 21\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSumanta Chatterjee, Aritra Banerjee, Tania Biswas and Somya Ranjan Bhoi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 22\u003cbr\u003e2.2 Background 24\u003cbr\u003e2.3 Tiny Machine Learning 26\u003cbr\u003e2.4 TinyML Operations 28\u003cbr\u003e2.5 Application 32\u003cbr\u003e2.6 Challenges and Proposed Solutions 40\u003cbr\u003e2.7 Impacts of TinyML 43\u003cbr\u003e2.8 Sustainable Development Through TinyML 45\u003cbr\u003e2.9 Conclusion 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Edge Intelligence and Trust: The Synergy of TinyML, IoT, and Blockchain in Modern Applications 51\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAbhishek Bhattacharya, Soumi Dutta, Anupam Ghosh, Arijit Dutta, Prabuddha Chatterjee and Sangeeta Banik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 52\u003cbr\u003e3.2 Literature Review 59\u003cbr\u003e3.3 Applications 65\u003cbr\u003e3.4 Discussion 75\u003cbr\u003e3.5 Conclusion 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Use Cases of TinyML 87\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eS. Sharmila Devi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 88\u003cbr\u003e4.2 Use Cases of TinyML 90\u003cbr\u003e4.3 Conclusion 99\u003cbr\u003e4.4 Future Scope 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II: Applications 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Advancing Smart Devices and IoT: Research Insights and Directions 107\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAjay Verma, Nahida Majeed Wani and Girraj Kumar Verma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 108\u003cbr\u003e5.2 How Smart Devices Work 109\u003cbr\u003e5.3 The Need for Smart Devices in the Real World 111\u003cbr\u003e5.4 Properties of Smart Devices 112\u003cbr\u003e5.5 Connection to the Internet of Things (IoT) 115\u003cbr\u003e5.6 Security and Privacy: Keeping the IoT Landscape Safe 116\u003cbr\u003e5.7 Trends and Research Opportunities in the Future 122\u003cbr\u003e5.8 Conclusion 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 TinyML for Smart Devices and IoT: Enabling Efficient and Intelligent Applications 127\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNeeta A. Ukirade\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 128\u003cbr\u003e6.2 Tools and Frameworks for TinyML Development 130\u003cbr\u003e6.3 Key Techniques in TinyML for IoT 133\u003cbr\u003e6.4 Applications of TinyML in Smart IoT Devices 135\u003cbr\u003e6.5 Challenges and Limitations 138\u003cbr\u003e6.6 Future Directions 141\u003cbr\u003e6.7 Conclusion 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Predictive Maintenance Using Tiny Machine Learning: A Revolutionary Approach to Proactive Equipment Maintenance 149\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eG. JayaLakshmi, Ch. JayaLakshmi and M. Ramesh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 150\u003cbr\u003e7.2 Predictive Maintenance: The Need for Proactivity 151\u003cbr\u003e7.3 TinyML: Scope, Advantages, and Applications 154\u003cbr\u003e7.4 TinyML in Predictive Maintenance: Key Components and Implementation Framework 157\u003cbr\u003e7.5 Analyzing Real-World Applications and Case Studies of TinyML-Based Predictive Maintenance Systems 159\u003cbr\u003e7.6 Conclusion 159\u003cbr\u003e7.7 Future Scope 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 TinyML and IoT in Agriculture: Boosting Real-Time Efficiency, Autonomy, and Resilience in Smart Farming 163\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShanthalakshmi M., Deepika N., Avvudaiyappan R.M. and Prince Raj J.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 164\u003cbr\u003e8.2 Smart Fertilizer Distribution Using Soil and Crop Data 167\u003cbr\u003e8.3 Weed Detection Using TinyML and IoT 170\u003cbr\u003e8.4 Animal Intrusion Detection in Crops 172\u003cbr\u003e8.5 Disease Prevention and Detection 176\u003cbr\u003e8.6 Enhancing Smart Irrigation with TinyML for Climate Prediction and Optimization Existing Systems 179\u003cbr\u003e8.7 Conclusion 184\u003cbr\u003e8.8 Future Scope 185\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 TinyML and IoT for Predictive Maintenance and Real-Time Decision Support in Automotive Air Conditioning 191\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eG. Bhavani and C. Jeyalakshmi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 192\u003cbr\u003e9.2 Architecture Overview 193\u003cbr\u003e9.3 Technologies Enabling TinyML 198\u003cbr\u003e9.4 Advantages of a Properly Functioning AC System 201\u003cbr\u003e9.5 Advantages of TinyML in Real-Time Data Monitoring 201\u003cbr\u003e9.6 Challenges 202\u003cbr\u003e9.7 Conclusion 204\u003cbr\u003e9.8 Future Scope 205\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Automated Harm Detection: Enhancing Women's Safety in Real Time 207\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShoban S., Rohith V., Shanthalakshmi M. and Deepika N.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 208\u003cbr\u003e10.2 Prior Knowledge 210\u003cbr\u003e10.3 Related Works 212\u003cbr\u003e10.4 Proposed Methodology 215\u003cbr\u003e10.5 Challenges 232\u003cbr\u003e10.6 Future Scope 233\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Butterfly Optimization with Random Forest for COVID-19 Prediction Using Lung Image 237\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSivanantham Kalimuthu, Ramkumar N., Arun Prakash N., Boorneush M. and Dhusiyanth M.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 238\u003cbr\u003e11.2 Literature Survey 241\u003cbr\u003e11.3 Proposed Research Methodology 243\u003cbr\u003e11.4 Implementation Results 247\u003cbr\u003e11.5 Conclusion 254\u003cbr\u003e11.6 Future Scope 255\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III: Security 259\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 AI-Powered Resilience and Privacy Preservation in Cloud-IoT Environments for Smart Devices Using Fog Computing Methodologies 261\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBiplab Gope and Soumen Santra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 262\u003cbr\u003e12.2 AI-Powered Resilience Mechanisms 264\u003cbr\u003e12.3 Privacy Preservation Techniques 265\u003cbr\u003e12.4 Fog Computing as an Enabler 265\u003cbr\u003e12.5 Key Concepts 265\u003cbr\u003e12.6 Results 266\u003cbr\u003e12.7 Current Practices and Challenges 267\u003cbr\u003e12.8 Challenges 268\u003cbr\u003e12.9 Proposed Solutions 268\u003cbr\u003e12.10 Applications and Use Cases 269\u003cbr\u003e12.11 Technological Frameworks 270\u003cbr\u003e12.12 Conclusion 270\u003cbr\u003e12.13 Future Scope 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Data Privacy and Transmission Security 279\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSaptarshi Kumar Sarkar, Anupama Sen and Piyal Roy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 279\u003cbr\u003e13.2 Foundations of Data Privacy 283\u003cbr\u003e13.3 Transmission Security 288\u003cbr\u003e13.4 Emerging Threats to Data Privacy and Transmission Security 294Contents xvii\u003cbr\u003e13.5 Impact of Emerging Technologies 300\u003cbr\u003e13.6 Challenges in Ensuring Data Privacy and Secure Transmission 306\u003cbr\u003e13.7 Practical Approaches to Enhancing Data Privacy and Transmission Security 312\u003cbr\u003e13.8 Conclusion 316\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Security and Privacy Concerns for Blockchain-Enabled Federated Learning 321\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePartha Ghosh, Ananya Biswas, Suradhuni Ghosh, Rima Bhowmik and Ankita Barua\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 322\u003cbr\u003e14.2 Importance of Security and Privacy 324\u003cbr\u003e14.3 Architecture of Federated Learning 326\u003cbr\u003e14.4 Difference between Centralized Learning, Distributed Learning, and Federated Learning 327\u003cbr\u003e14.5 Sources of Vulnerabilities in Federated Learning 329\u003cbr\u003e14.6 Security Threats in Federated Learning 332\u003cbr\u003e14.7 Defense Mechanism in Federated Learning System 337\u003cbr\u003e14.8 Federated Learning Schemes 341\u003cbr\u003e14.9 Federated Learning: An Approach to Healthcare in IIoE that Protects Privacy 342\u003cbr\u003e14.10 Homomorphic Encryption (HE) Method in IIoE-Focused Federated Learning 343Contents xix\u003cbr\u003e14.11 Blockchain-Powered Federated Learning 345\u003cbr\u003e14.12 Decentralized Data Sharing in Healthcare 347\u003cbr\u003e14.13 Public Key Infrastructure (PKI) for the System 351\u003cbr\u003e14.14 Protecting Privacy with Cross-Chained Fl Techniques 352\u003cbr\u003e14.15 Use of Blockchain-Enabled FL to Preserve Privacy 353\u003cbr\u003e14.16 Challenges and Solutions 354\u003cbr\u003e14.17 Open Research Challenges 358\u003cbr\u003e14.18 Conclusion and Future Direction 360\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Adversarial Attacks and Defenses in Security 367\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSudeshna Dey, Siddhartha Chatterjee, Sumita Gupta and Sima Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 368\u003cbr\u003e15.2 Fundamentals of Federated Learning 370\u003cbr\u003e15.3 Security and Privacy Threats in FL 373\u003cbr\u003e15.4 Attacks in Federated Learning 374\u003cbr\u003e15.5 Problems and Committing Directions 382\u003cbr\u003e15.6 Conclusion 385\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Ethical and Technical Foundations of Privacy-Preserving Federated Learning 389\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMuhammad Rifthy Kalideen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 390\u003cbr\u003e16.2 Foundations of Federated Learning 392\u003cbr\u003e16.3 Ethical Foundations of Privacy in Federated Learning 396\u003cbr\u003e16.4 Technical Foundations of Privacy-Preserving Federated Learning 402\u003cbr\u003e16.5 Interplay Between Ethical and Technical Foundations 407\u003cbr\u003e16.6 Case Studies and Real-World Applications 410\u003cbr\u003e16.7 Future Directions and Emerging Trends 412\u003cbr\u003e16.8 Conclusion 415\u003cbr\u003eReferences 416\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Integrating Security Measures in CLAHE-Enhanced YOLOV8 Model for Underwater Object Detection 423\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNiyati Sahoo, Sanjukta Mohanty and Arup Abhinna Acharya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 424\u003cbr\u003e17.2 Background 426\u003cbr\u003e17.3 Related Works 435\u003cbr\u003e17.4 Proposed Approach 438\u003cbr\u003e17.5 Experiment and Results 450\u003cbr\u003e17.6 Frequently Occurring Threats and Mitigation Policy 452\u003cbr\u003e17.7 Conclusion 454\u003cbr\u003e17.8 Future Scope 454\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 TinyML Deployment for Resource-Constrained Devices in IoT Applications with Attribute-Based Encryption Scheme 457\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eR. Lavanya and V. Thanigaivelan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 458\u003cbr\u003e18.2 Resource-Constrained Devices 461\u003cbr\u003e18.3 Background for Attribute-Based Encryption 466\u003cbr\u003e18.4 Related Work in ABE and Other Security Schemes 468\u003cbr\u003e18.5 Local Interpretable Model-Agnostic Explanations 470\u003cbr\u003e18.6 Conclusion 472\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Deep Learning-Based Adversarial Attack Detection for Cloud-IoT Systems 475\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAmit Kumar, Sachin Ahuja and Ganesh Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 476\u003cbr\u003e19.2 Background and Motivation 478\u003cbr\u003e19.3 Deep Learning for Intrusion Detection in Cloud-IoT Systems 480\u003cbr\u003e19.4 Case Study: Adversarial Attack Detection in Smart Grid Systems 483\u003cbr\u003e19.5 Challenges and Future Directions 485\u003cbr\u003e19.6 Conclusion 486\u003c\/p\u003e \u003cp\u003eBibliography 487\u003cbr\u003eIndex 489\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433819435288,"sku":"9781394347094","price":157.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394347094.jpg?v=1784853956","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/tiny-machine-learning-fundamentals-applications-and-security-hardback-9781394347094","provider":"Freshly Printed Books","version":"1.0","type":"link"}