{"product_id":"smart-factories-for-industry-5-0-transformation-hardback-9781394199952","title":"Smart Factories for Industry 5.0 Transformation (Hardback) 9781394199952","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eSmart Factories for Industry 5.0 Transformation\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eR. Nidhya (Edited by), Manish Kumar (Edited by), S. Karthik (Edited by), Rishabh Anand (Edited by), S. Balamurugan (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394199952, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 7 February 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e368 pages\u003cbr\u003e22.9 x 15.2 x 2.3 cm, 0.68 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\u003eThis book serves as a comprehensive guide, exploring the technologies, design principles, and operational strategies behind smart factories.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn an era where industrial expertise meets digital innovation, the “smart factory” symbolizes a new wave of efficiency and advancement. Industry 5.0 represents a paradigm shift, integrating technologies like robotics, AI, IoT, and big data to enhance human-machine collaboration while improving sustainability, quality, and efficiency. It offers businesses valuable insights and real-world examples to navigate the opportunities and challenges of Industry 5.0. \u003c\/p\u003e\n\u003cp\u003eThis book goes beyond technical explanations to examine the broader impact of the Industry 5.0 revolution on global supply chains and socioeconomic change, encouraging readers to view technology as a force for good. It appeals to all levels of expertise, providing valuable insights for experienced professionals while serving as an introduction for newcomers. Above all, it invites readers to embrace the collaborative spirit and creativity of Industry 5.0, joining in the effort to build the smart factories that will drive the future of innovation. \u003c\/p\u003e\n\u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eResearchers, industry engineers, and technologists working in artificial intelligence and Industry 5.0 application areas such as healthcare, transportation, manufacturing, and more.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Evolution of Industrial Revolution: Industry 5.0 and Beyond 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Balamurugan and B. Surya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eBrief History of Industrial Revolution 1\u003c\/p\u003e \u003cp\u003eAcknowledgement 4\u003c\/p\u003e \u003cp\u003eBibliography and Further Reading 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Personalized Healthcare Transformation via Novel Era of Artificial Intelligence-Based Heuristic Concept 5\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eS. Pradeep, R. Sathish Kumar, M. Jagadesh and A. Karthikeyan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eNomenclature 6\u003c\/p\u003e \u003cp\u003e2.1 Introduction 7\u003c\/p\u003e \u003cp\u003e2.2 Literature Survey 9\u003c\/p\u003e \u003cp\u003e2.3 Digitization, Data Sources, and AI in Healthcare 13\u003c\/p\u003e \u003cp\u003e2.4 AI Mainstreaming in Healthcare 15\u003c\/p\u003e \u003cp\u003e2.5 Current Status, Integration, and Obstacles to the Usage of Personalized Healthcare Transformation 17\u003c\/p\u003e \u003cp\u003e2.6 Prerequisites for Radical Transformation in Healthcare 25\u003c\/p\u003e \u003cp\u003e2.7 Personalized Healthcare Transformation Using MSOM-Based TOA 29\u003c\/p\u003e \u003cp\u003e2.8 Results 34\u003c\/p\u003e \u003cp\u003e2.9 Conclusion 41\u003c\/p\u003e \u003cp\u003eReferences 41\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 A Survey on Security in Data Transmission Using Wireless Communication Methods for IoT Edge Devices 45\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eV. Maruthi Prasad and B. Bharathi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 46\u003c\/p\u003e \u003cp\u003e3.2 Literature Survey 47\u003c\/p\u003e \u003cp\u003e3.3 Description of Data Protocols for IoT System 50\u003c\/p\u003e \u003cp\u003e3.4 IoT Communication Parameters 59\u003c\/p\u003e \u003cp\u003e3.5 Comparative of Communication Protocols for IoT Systems 64\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 67\u003c\/p\u003e \u003cp\u003eReferences 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Innovative Application of Conditional Deep Convolutional Generative Adversarial Networks to Enhance Chronic Kidney Disease Diagnosis with Uneven Datasets 71\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLakshmi Ramani Burra, Praveen Tumuluru, Janakiramaiah Bonam, S. Hrushikesava Raju, Sunanda Nalajala and Surya Prasada Rao Borra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 72\u003c\/p\u003e \u003cp\u003e4.2 Literature Survey 76\u003c\/p\u003e \u003cp\u003e4.3 Methodology 78\u003c\/p\u003e \u003cp\u003e4.3.1 Data Preprocessing 79\u003c\/p\u003e \u003cp\u003e4.3.2 Conditional Deep Convolutional Generative Adversarial Network 79\u003c\/p\u003e \u003cp\u003e4.3.3 Bidirectional Long-Term Memory (Bi-LSTM) Method 82\u003c\/p\u003e \u003cp\u003e4.4 Result Analysis 83\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 85\u003c\/p\u003e \u003cp\u003eReferences 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A Comprehensive Hybrid Implicit and Explicit Item-Based Collaborative Filtering Approach with Bayesian Personalized Ranking for Enhancing Book Recommendations 89\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAdidam Surekha, Radhika Gouni, Satya Keerthi Gorripati, Venubabu Rachapudi, S. Anjali Devi and Anupama Angadi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 90\u003c\/p\u003e \u003cp\u003e5.2 Related Work 92\u003c\/p\u003e \u003cp\u003e5.3 Methodology 95\u003c\/p\u003e \u003cp\u003e5.4 Experimental Results and Analysis 99\u003c\/p\u003e \u003cp\u003e5.5 Conclusion 102\u003c\/p\u003e \u003cp\u003eReferences 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 An Efficient Cluster-Based Deep Learning Model for Multi-Attack Classification in IDS Across Diverse Datasets 105\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRajesh Bingu, G. Harsha Vardhan Reddy, U. Jyothi Naga Pavan, S. Sneha Sai Sri and N. V. Praveen Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 106\u003c\/p\u003e \u003cp\u003e6.2 Literature Survey 107\u003c\/p\u003e \u003cp\u003e6.3 Proposed Model Design 110\u003c\/p\u003e \u003cp\u003e6.4 Results and Discussion 115\u003c\/p\u003e \u003cp\u003e6.5 Conclusion 118\u003c\/p\u003e \u003cp\u003eReferences 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Heart Failure Detection Through SMOTE for Augmentation and Machine Learning Approach for Classification 123\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eG. Kiran Kumar, Anila M., Naga Raju Hari Manikyam, Venkata Nagaraju Thatha, R. Vijaya Kumar Reddy and Krishna Reddy Papana\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 124\u003c\/p\u003e \u003cp\u003e7.2 Literature Survey 125\u003c\/p\u003e \u003cp\u003e7.3 Proposed Methodology 126\u003c\/p\u003e \u003cp\u003e7.4 Results and Discussion 128\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 132\u003c\/p\u003e \u003cp\u003eReferences 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Optimal Power Allocation in Cognitive Radio Networks Using Teaching-Learning-Based Optimization 135\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eN. Lakshman Pratap, N. Sunanda and V. Suryanarayana Reddy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 136\u003c\/p\u003e \u003cp\u003e8.2 Teaching-Learning-Based Optimization 137\u003c\/p\u003e \u003cp\u003e8.2.1 Teacher Phase 138\u003c\/p\u003e \u003cp\u003e8.2.2 Learner Phase 139\u003c\/p\u003e \u003cp\u003e8.3 Proposed Power Allocation Algorithm 140\u003c\/p\u003e \u003cp\u003e8.4 Numerical Results 143\u003c\/p\u003e \u003cp\u003e8.5 Conclusion 145\u003c\/p\u003e \u003cp\u003eReferences 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Using Historical Pattern Matching and Natural Language Processing in a Hybrid Approach for Stock Market 147\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eK. Sri Niharika, C.H. Srisai Naga Satya Mani Pavan, T. Baby Aparna, Dinesh Kumar Anguraj, S. Saathvik and Hari Kiran Vege\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 148\u003c\/p\u003e \u003cp\u003e9.1.1 Background 148\u003c\/p\u003e \u003cp\u003e9.1.2 Problem Description 148\u003c\/p\u003e \u003cp\u003e9.1.3 Purposes of the Research 148\u003c\/p\u003e \u003cp\u003e9.1.4 Objectives of the Research 149\u003c\/p\u003e \u003cp\u003e9.2 Literature Review 149\u003c\/p\u003e \u003cp\u003e9.2.1 Review Based on Reference Research Paper 149\u003c\/p\u003e \u003cp\u003e9.3 Methodology 153\u003c\/p\u003e \u003cp\u003e9.3.1 Overview of the Hybrid Method 153\u003c\/p\u003e \u003cp\u003e9.3.2 Sentiment Analysis 154\u003c\/p\u003e \u003cp\u003e9.3.3 News Classification Using NLP Techniques 154\u003c\/p\u003e \u003cp\u003e9.3.4 Algorithms for Historical Pattern Matching 155\u003c\/p\u003e \u003cp\u003e9.3.5 Integration 156\u003c\/p\u003e \u003cp\u003e9.4 Data Sources and Collection 156\u003c\/p\u003e \u003cp\u003e9.4.1 Sources of Financial News 156\u003c\/p\u003e \u003cp\u003e9.4.2 Market Data Historical Overview 157\u003c\/p\u003e \u003cp\u003e9.4.3 Cleaning and Pre-Processing Data 157\u003c\/p\u003e \u003cp\u003e9.5 Experimental Setup 158\u003c\/p\u003e \u003cp\u003e9.5.1 Datasets for Training and Testing 158\u003c\/p\u003e \u003cp\u003e9.5.2 Metrics for Evaluation 158\u003c\/p\u003e \u003cp\u003e9.5.3 Optimization and Tuning of Hyperparameters 159\u003c\/p\u003e \u003cp\u003e9.6 Discussion 160\u003c\/p\u003e \u003cp\u003e9.6.1 Comparison of Model Performance 160\u003c\/p\u003e \u003cp\u003e9.6.2 NLP and Pattern Matching’s Effectiveness 160\u003c\/p\u003e \u003cp\u003e9.6.3 Restrictions and Perspectives 160\u003c\/p\u003e \u003cp\u003e9.6.4 Consequences and Prospective Courses 161\u003c\/p\u003e \u003cp\u003e9.7 Results 161\u003c\/p\u003e \u003cp\u003e9.8 Conclusion 163\u003c\/p\u003e \u003cp\u003eReferences 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 An Intelligent Framework for IoT-Based Health Care Monitoring Using Fuzzy-Supported Machine Learning Algorithm 167\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMohanapriya M., Bharanidharan R., R. Santhosh and R. Reshma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 168\u003c\/p\u003e \u003cp\u003e10.2 Literature Analysis 170\u003c\/p\u003e \u003cp\u003e10.3 Integrated IoT-Based Healthcare Decision Making Model Using Machine Learning (IHM-ML) 172\u003c\/p\u003e \u003cp\u003e10.4 Result and Discussion 180\u003c\/p\u003e \u003cp\u003e10.5 Conclusion and the Future Scope 184\u003c\/p\u003e \u003cp\u003eReferences 184\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Design Strategy for Narrowband Internet of Things with Its Scope and Challenges of Security Solutions 187\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eR. Reshma, N. Mohanasundaram and R. Santhosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Prologue Study 188\u003c\/p\u003e \u003cp\u003e11.2 Fundamentals of NB-IoT Network Design 190\u003c\/p\u003e \u003cp\u003e11.3 Security Challenges and Vulnerabilities in NB-IoT Systems 216\u003c\/p\u003e \u003cp\u003e11.4 Scope of Machine Intelligence in NB-IoT Security 218\u003c\/p\u003e \u003cp\u003e11.5 Conclusion and the Future Scope 220\u003c\/p\u003e \u003cp\u003eReferences 220\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Machine Learning in Healthcare: Unlocking Precision Diagnosis and Continuous Monitoring Through Voice Analysis 229\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSmilarubavathy G., Keerthana S. M., Nidhya R., Thanga Priscilla and Pavithra D.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 230\u003c\/p\u003e \u003cp\u003e12.2 Background 232\u003c\/p\u003e \u003cp\u003e12.3 Methodology 232\u003c\/p\u003e \u003cp\u003e12.4 Results 242\u003c\/p\u003e \u003cp\u003e12.5 Discussion 243\u003c\/p\u003e \u003cp\u003eConclusion 243\u003c\/p\u003e \u003cp\u003eReferences 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Introduction of Advanced and Improved Transposition Algorithm 247\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDipesh Kumar, Nirupama Mandal and Yugal Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 248\u003c\/p\u003e \u003cp\u003e13.2 Literature Study 249\u003c\/p\u003e \u003cp\u003e13.3 Implementation 257\u003c\/p\u003e \u003cp\u003e13.3.1 Algorithm for Encryption 258\u003c\/p\u003e \u003cp\u003e13.3.2 Algorithm for Decryption 261\u003c\/p\u003e \u003cp\u003e13.4 Result 264\u003c\/p\u003e \u003cp\u003e13.4.1 Experimental Setup 264\u003c\/p\u003e \u003cp\u003e13.4.2 Experiment Result 265\u003c\/p\u003e \u003cp\u003e13.4.2.1 Encryption Process 265\u003c\/p\u003e \u003cp\u003e13.4.2.2 Decryption Process 265\u003c\/p\u003e \u003cp\u003e13.5 Conclusion and Future Direction 266\u003c\/p\u003e \u003cp\u003eReferences 266\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Performance Evaluation of Children at Risk for Schizophrenia Using Ensemble Learning 269\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRathiya R., Kalamani M., Narmadha R. P., Sreenivasa Perumal L. and Kalpana R.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 270\u003c\/p\u003e \u003cp\u003e14.2 Literature Review 271\u003c\/p\u003e \u003cp\u003e14.3 Methodology 274\u003c\/p\u003e \u003cp\u003e14.4 Performance Analysis 276\u003c\/p\u003e \u003cp\u003e14.5 Result Analysis 279\u003c\/p\u003e \u003cp\u003e14.6 Conclusion 279\u003c\/p\u003e \u003cp\u003e14.7 Future Work 280\u003c\/p\u003e \u003cp\u003eReferences 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Advanced Aquaculture Management: A Smart System for Optimizing Oxygen Levels, Shrimp Health Monitoring 283\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePrathyusha Kuncha, J. Manoranjini, Sirisha J., Suneetha Bandeela, Naveen Kumar Penjarla and Simhadri Subhash Goud\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 284\u003c\/p\u003e \u003cp\u003e15.2 Literature Survey 286\u003c\/p\u003e \u003cp\u003e15.3 System Model 288\u003c\/p\u003e \u003cp\u003e15.4 Results and Discussion 292\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 296\u003c\/p\u003e \u003cp\u003eReferences 297\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Farming Revolution: Precision Agriculture and IoT for Sustainable Growth 299\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eArepalli Gopi, Sudha L. R. and Iwin Thanakumar Joseph S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 300\u003c\/p\u003e \u003cp\u003e16.2 Data Storage and Analysis on Cloud Data 304\u003c\/p\u003e \u003cp\u003e16.3 Architecture IoT with Agriculture 306\u003c\/p\u003e \u003cp\u003e16.4 Results and Performance Validation 311\u003c\/p\u003e \u003cp\u003e16.5 Conclusion 317\u003c\/p\u003e \u003cp\u003eReferences 318\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Comparative Analysis of the Identification and Categorization of the Malaria Parasite Employing Recent Amalgamated Machine Learning Methodologies 321\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTamal Kumar Kundu, Dinesh Kumar Anguraj, R. Nidhya and V. Maruthi Prasad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 322\u003c\/p\u003e \u003cp\u003eDataset Acquisition 325\u003c\/p\u003e \u003cp\u003eMethodology 325\u003c\/p\u003e \u003cp\u003eLiterature Survey 326\u003c\/p\u003e \u003cp\u003eMethodology 328\u003c\/p\u003e \u003cp\u003eResults and Discussion 328\u003c\/p\u003e \u003cp\u003eConclusion 332\u003c\/p\u003e \u003cp\u003eReferences 334\u003c\/p\u003e \u003cp\u003eIndex 337\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mechanical engineering \u0026amp; materials [\u003ca title=\"See our other books on Mechanical engineering \u0026amp; materials\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mechanical%20engineering%20\u0026amp;%20materials%20%5BTG%5D%22\"\u003eTG\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":52433202020632,"sku":"9781394199952","price":203.45,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394199952.jpg?v=1784851580","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/smart-factories-for-industry-5-0-transformation-hardback-9781394199952","provider":"Freshly Printed Books","version":"1.0","type":"link"}