Buch, Englisch, 368 Seiten, Gewicht: 666 g
Buch, Englisch, 368 Seiten, Gewicht: 666 g
ISBN: 978-1-394-19995-2
Verlag: John Wiley & Sons Inc
In 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.
This 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.
Audience
Researchers, industry engineers, and technologists working in artificial intelligence and Industry 5.0 application areas such as healthcare, transportation, manufacturing, and more.
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen
- Technische Wissenschaften Technik Allgemein Mess- und Automatisierungstechnik
- Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
Weitere Infos & Material
Preface xi
1 Evolution of Industrial Revolution: Industry 5.0 and Beyond 1
S. Balamurugan and B. Surya
Brief History of Industrial Revolution 1
Acknowledgement 4
Bibliography and Further Reading 4
2 Personalized Healthcare Transformation via Novel Era of Artificial Intelligence-Based Heuristic Concept 5
S. Pradeep, R. Sathish Kumar, M. Jagadesh and A. Karthikeyan
Nomenclature 6
2.1 Introduction 7
2.2 Literature Survey 9
2.3 Digitization, Data Sources, and AI in Healthcare 13
2.4 AI Mainstreaming in Healthcare 15
2.5 Current Status, Integration, and Obstacles to the Usage of Personalized Healthcare Transformation 17
2.6 Prerequisites for Radical Transformation in Healthcare 25
2.7 Personalized Healthcare Transformation Using MSOM-Based TOA 29
2.8 Results 34
2.9 Conclusion 41
References 41
3 A Survey on Security in Data Transmission Using Wireless Communication Methods for IoT Edge Devices 45
V. Maruthi Prasad and B. Bharathi
3.1 Introduction 46
3.2 Literature Survey 47
3.3 Description of Data Protocols for IoT System 50
3.4 IoT Communication Parameters 59
3.5 Comparative of Communication Protocols for IoT Systems 64
3.6 Conclusion 67
References 67
4 Innovative Application of Conditional Deep Convolutional Generative Adversarial Networks to Enhance Chronic Kidney Disease Diagnosis with Uneven Datasets 71
Lakshmi Ramani Burra, Praveen Tumuluru, Janakiramaiah Bonam, S. Hrushikesava Raju, Sunanda Nalajala and Surya Prasada Rao Borra
4.1 Introduction 72
4.2 Literature Survey 76
4.3 Methodology 78
4.3.1 Data Preprocessing 79
4.3.2 Conditional Deep Convolutional Generative Adversarial Network 79
4.3.3 Bidirectional Long-Term Memory (Bi-LSTM) Method 82
4.4 Result Analysis 83
4.5 Conclusion 85
References 86
5 A Comprehensive Hybrid Implicit and Explicit Item-Based Collaborative Filtering Approach with Bayesian Personalized Ranking for Enhancing Book Recommendations 89
Adidam Surekha, Radhika Gouni, Satya Keerthi Gorripati, Venubabu Rachapudi, S. Anjali Devi and Anupama Angadi
5.1 Introduction 90
5.2 Related Work 92
5.3 Methodology 95
5.4 Experimental Results and Analysis 99
5.5 Conclusion 102
References 102
6 An Efficient Cluster-Based Deep Learning Model for Multi-Attack Classification in IDS Across Diverse Datasets 105
Rajesh Bingu, G. Harsha Vardhan Reddy, U. Jyothi Naga Pavan, S. Sneha Sai Sri and N. V. Praveen Kumar
6.1 Introduction 106
6.2 Literature Survey 107
6.3 Proposed Model Design 110
6.4 Results and Discussion 115
6.5 Conclusion 118
References 119
7 Heart Failure Detection Through SMOTE for Augmentation and Machine Learning Approach for Classification 123
G. Kiran Kumar, Anila M., Naga Raju Hari Manikyam, Venkata Nagaraju Thatha, R. Vijaya Kumar Reddy and Krishna Reddy Papana
7.1 Introduction 124
7.2 Literature Survey 125
7.3 Proposed Methodology 126
7.4 Results and Discussion 128
7.5 Conclusion 132
References 132
8 Optimal Power Allocation in Cognitive Radio Networks Using Teaching-Learning-Based Optimization 135
N. Lakshman Pratap, N. Sunanda and V. Suryanarayana Reddy
8.1 Introduction 136
8.2 Teaching-Learning-Based Optimization 137
8.2.1 Teacher Phase 138
8.2.2 Learner Phase 139
8.3 Proposed Power Allocation Algorithm 140
8.4 Numerical Results 143
8.5 Conclusion 145
References 145
9 Using Historical Pattern Matching and Natural Language Processing in a Hybrid Approach for Stock Market 147
K. Sri Niharika, C.H. Srisai Naga Satya Mani Pavan, T. Baby Aparna, Dinesh Kumar Anguraj, S. Saathvik and Hari Kiran Vege
9.1 Introduction 148
9.1.1 Background 148
9.1.2 Problem Description 148
9.1.3 Purposes of the Research 148
9.1.4 Objectives of the Research 149
9.2 Literature Review 149
9.2.1 Review Based on Reference Research Paper 149
9.3 Methodology 153
9.3.1 Overview of the Hybrid Method 153
9.3.2 Sentiment Analysis 154
9.3.3 News Classification Using NLP Techniques 154
9.3.4 Algorithms for Historical Pattern Matching 155
9.3.5 Integration 156
9.4 Data Sources and Collection 156
9.4.1 Sources of Financial News 156
9.4.2 Market Data Historical Overview 157
9.4.3 Cleaning and Pre-Processing Data 157
9.5 Experimental Setup 158
9.5.1 Datasets for Training and Testing 158
9.5.2 Metrics for Evaluation 158
9.5.3 Optimization and Tuning of Hyperparameters 159
9.6 Discussion 160
9.6.1 Comparison of Model Performance 160
9.6.2 NLP and Pattern Matching’s Effectiveness 160
9.6.3 Restrictions and Perspectives 160
9.6.4 Consequences and Prospective Courses 161
9.7 Results 161
9.8 Conclusion 163
References 164
10 An Intelligent Framework for IoT-Based Health Care Monitoring Using Fuzzy-Supported Machine Learning Algorithm 167
Mohanapriya M., Bharanidharan R., R. Santhosh and R. Reshma
10.1 Introduction 168
10.2 Literature Analysis 170
10.3 Integrated IoT-Based Healthcare Decision Making Model Using Machine Learning (IHM-ML) 172
10.4 Result and Discussion 180
10.5 Conclusion and the Future Scope 184
References 184
11 Design Strategy for Narrowband Internet of Things with Its Scope and Challenges of Security Solutions 187
R. Reshma, N. Mohanasundaram and R. Santhosh
11.1 Prologue Study 188
11.2 Fundamentals of NB-IoT Network Design 190
11.3 Security Challenges and Vulnerabilities in NB-IoT Systems 216
11.4 Scope of Machine Intelligence in NB-IoT Security 218
11.5 Conclusion and the Future Scope 220
References 220
12 Machine Learning in Healthcare: Unlocking Precision Diagnosis and Continuous Monitoring Through Voice Analysis 229
Smilarubavathy G., Keerthana S. M., Nidhya R., Thanga Priscilla and Pavithra D.
12.1 Introduction 230
12.2 Background 232
12.3 Methodology 232
12.4 Results 242
12.5 Discussion 243
Conclusion 243
References 244
13 Introduction of Advanced and Improved Transposition Algorithm 247
Dipesh Kumar, Nirupama Mandal and Yugal Kumar
13.1 Introduction 248
13.2 Literature Study 249
13.3 Implementation 257
13.3.1 Algorithm for Encryption 258
13.3.2 Algorithm for Decryption 261
13.4 Result 264
13.4.1 Experimental Setup 264
13.4.2 Experiment Result 265
13.4.2.1 Encryption Process 265
13.4.2.2 Decryption Process 265
13.5 Conclusion and Future Direction 266
References 266
14 Performance Evaluation of Children at Risk for Schizophrenia Using Ensemble Learning 269
Rathiya R., Kalamani M., Narmadha R. P., Sreenivasa Perumal L. and Kalpana R.
14.1 Introduction 270
14.2 Literature Review 271
14.3 Methodology 274
14.4 Performance Analysis 276
14.5 Result Analysis 279
14.6 Conclusion 279
14.7 Future Work 280
References 280
15 Advanced Aquaculture Management: A Smart System for Optimizing Oxygen Levels, Shrimp Health Monitoring 283
Prathyusha Kuncha, J. Manoranjini, Sirisha J., Suneetha Bandeela, Naveen Kumar Penjarla and Simhadri Subhash Goud
15.1 Introduction 284
15.2 Literature Survey 286
15.3 System Model 288
15.4 Results and Discussion 292
15.5 Conclusion 296
References 297
16 Farming Revolution: Precision Agriculture and IoT for Sustainable Growth 299
Arepalli Gopi, Sudha L. R. and Iwin Thanakumar Joseph S.
16.1 Introduction 300
16.2 Data Storage and Analysis on Cloud Data 304
16.3 Architecture IoT with Agriculture 306
16.4 Results and Performance Validation 311
16.5 Conclusion 317
References 318
17 Comparative Analysis of the Identification and Categorization of the Malaria Parasite Employing Recent Amalgamated Machine Learning Methodologies 321
Tamal Kumar Kundu, Dinesh Kumar Anguraj, R. Nidhya and V. Maruthi Prasad
Introduction 322
Dataset Acquisition 325
Methodology 325
Literature Survey 326
Methodology 328
Results and Discussion 328
Conclusion 332
References 334
Index 337
