Causal Artificial Intelligence
Editat de Rajdeep Chakraborty, Aleem Ali, Amit Paul, Nawaf R. Alharbe, S. Balamuruganen Limba Engleză Hardback – 12 noi 2026
Causal Artificial Intelligence (CAI) represents a significant advancement in the development of AI systems, marking a shift from purely correlation-based models to those capable of understanding and reasoning cause-and-effect relationships. In the context of disciplinary and industry development, casual AI addresses critical challenges in AI's ability to generalize, explain decisions, and make safe interventions across dynamic environments. Its emergence is reshaping fields like healthcare, economics, autonomous systems, and social sciences, offering a more reliable and interpretable form of AI that can predict the effects of actions, simulate outcomes, and inform decision-making more accurately. This book provides a comprehensive exploration of how causal reasoning can enhance the transparency, reliability, and ethical foundations of artificial intelligence systems. Addressing both theoretical and practical dimensions, it examines structural causal models, explainable AI, and privacy-preserving learning frameworks across diverse fields, including healthcare, climate science, manufacturing, automotive safety, and cybersecurity. It highlights innovative research in areas such as CNN-based causal inference for signal detection, explainable mobile AI for disease prediction, image processing for Ayurvedic material identification, and AI-driven systems for driver fatigue and collision detection. By bridging theoretical foundations with real-world applications, this book serves as a valuable resource for researchers, practitioners, and policymakers committed to developing transparent, secure, reliable, and ethically aligned intelligent technologies.
Readers will find the volume:
- Offers a deep dive into frameworks like structural causal models (SCMs), Bayesian networks, and do-calculus, allowing readers to explore how AI systems can identify and leverage cause-and-effect relationships;
- Emphasizes real-world applications of causal artificial intelligence through detailed case studies in fields such as healthcare, economics, and autonomous systems;
- Illustrates how causal models are applied in complex, dynamic environments, demonstrating casual AI's potential to revolutionize industries by providing actionable insights;
- Focuses on the ethical implications and interpretability of AI systems built on causal reasoning.
AI researchers, data scientists, academics, industry professionals, business leaders, medical researchers, and policymakers interested in advancing from traditional machine learning techniques to causal models.
Preț: 1308.25 lei
Preț vechi: 1635.31 lei
-20% Precomandă
Carte nepublicată încă
Specificații
Notă biografică
Aleem Ali, PhD is a Professor and Research Coordinator in the Department of Computer Science and Engineering at Chandigarh University, Punjab, India. With over 17 years of academic and research experience, he has established himself as a leading contributor in the areas of Internet of Things, artificial intelligence, machine and deep learning, and network security. He has an extensive research portfolio with more than 65 peer-reviewed publications in reputed journals and conferences and six books.
Amit Paul, PhD is an Associate Professor of Computer Science and Engineering at St. Thomas' College of Engineering and Technology, Kolkata, India. He has authored five journal articles and seven international conference papers. His research centers on causal inference and interpretable machine learning for biomedical applications.
Nawaf R. Alharbe, PhD is a Professor in the College of Computer Science and Engineering at Taibah University in Saudi Arabia. He has more than 20 publications in international journals and conferences of repute. His interests include knowledge management systems in smart healthcare operations using emerging technologies such as RFID, ZigBee, Internet of Things (IoT), and cloud computing.
S. Balamurugan, PhD is the Director of Intelligent Research Consultancy Services, Coimbatore, India. He has published more than 70 books, 300 articles in international and national journals and conferences, and 300 patents. He is a research consultant for many companies, startups, SMEs, and MSMEs.
Cuprins
Series Preface xix
Preface xxi
Acknowledgement xxv
Part I: Fundamentals 1
1 Causal Artificial Intelligence: Fundamentals and Applications 3
Barnali Gupta Banik and Geeta Ambildhuke
1.1 Introduction 4
1.2 Literature Survey 7
1.3 Basics of Causal AI 8
1.4 Applications of AI 13
1.5 Benefits, Limitations, and Challenges 15
1.6 Conclusion and Future Scope 16
2 Unveiling the Power of Causal AI: From Structural Causal Model to Bug Resolution Analysis 19
Reshmi Maulik, Subhajit Datta, Subhashis Majumder and Soumen Santra
2.1 Introduction 20
2.2 Literature Review 22
2.3 Methodology 23
2.4 Model Construction 27
2.5 Discussion 37
2.6 Sensitivity Analysis of Treatment Variables 38
2.7 Threats to Validity 39
2.8 Conclusion 41
2.9 Future Scope 41
3 Exploring Causal AI through Deep Learning: A CNN-Based Framework for Signal Detection in Climate and Space Sciences 45
Aditya Sharma and Inderdeep Kaur
3.1 Introduction 46
3.2 Literature Review 50
3.3 Gravitational Waves and Their Detection 54
3.4 Results and Discussion 57
3.5 Performance Evaluation 62
3.6 Conclusion 68
4 Predictive Health Mate: An Explainable AI for Disease Prognosis and Prevention on Android Device 73
Abhishek Roushan, Gurmeet Kaur, Inderdeep Kaur, Vatsala Singh, Harsh Yadav and Yash Srivastava
4.1 Introduction 74
4.2 Literature Review 75
4.3 Model Architecture 81
4.4 Conclusion 92
4.5 Future Scope 93
Part II: Applications 99
5 A Machine Learning-Based Approach for Identifying Ayurvedic Medicinal Raw Materials via Image Processing 101
Ankit Raj, Subhajit Chanda, Ashutosh Dubey, Dyuti Mitra and Debraj Chatterjee
5.1 Introduction 102
5.2 Literature Survey 104
5.3 Methodology 117
5.4 Results and Discussions 118
5.5 Key Findings 119
5.6 Future Scope 119
6 AI and Machine Learning in Healthcare: Transforming Treatment, Ethics, and Resource Optimization 123
Aakansha Khanna, Inderdeep Kaur, Inzimam Ul Hassan and Pratyansh Singh
6.1 Introduction 124
6.2 What is Machine Learning? 126
6.3 Categories of Machine Learning 127
6.4 Challenges and Architecture of the I-Healthcare System 130
6.5 Deep Learning 132
6.6 Case Study and Applications 132
6.7 Conclusion 138
7 Real-Time Driver Fatigue Monitoring Using AI: An Ethical and Non-Intrusive Approach to Road Safety 141
Sumanta Chatterjee and Amit Majumder
7.1 Introduction 141
7.2 Leveraging Artificial Intelligence for Safer Roads through Real-Time Fatigue Monitoring 144
7.3 AI and ML Technologies for Drowsiness Detection 149
7.4 Challenges in Driver Drowsiness Detection 160
7.5 Future Directions and Innovations 166
7.6 Conclusion 172
8 AI-Powered Collision Detection: A Neural Network System for Automotive Safety 175
Sumanta Chatterjee and Amit Majumder
8.1 Introduction 175
8.2 Background and Literature Review 179
8.3 Proposed Framework 184
8.4 Neural Network-Based Collision Detection: Architecture Framework 191
8.5 Components of Neural Network Architecture 193
8.6 Training the Neural Network 196
8.7 Neural Network-Based Collision Detection: Collision Prediction Framework 197
8.8 Neural Network Architecture for Collision Prediction 199
8.9 Collision Prediction Models 202
8.10 Training the Collision Prediction Model 202
8.11 Neural Network-Based Collision Detection with System Integration Framework 204
8.12 Neural Network Deployment 206
8.13 Real-Time Inference 207
8.14 Control and Feedback Loop 208
8.15 Testing and Validation 209
8.16 Applications and Benefits of Neural Network-Based Collision Detection Systems 210
8.17 Benefits of Neural Network-Based Collision Detection 211
8.18 Conclusion 213
9 Facial Emotion Detection Using Causal AI 217
Esha Kundu and Arijit Ghosal
9.1 Introduction 218
9.2 Literature Survey 219
9.3 Proposed Approach 221
9.4 Experimental Results: Deep Learning Methods 228
9.5 Conclusion 237
10 Causal and Data-Driven Machine Learning Algorithm for Predicting Temperature Profiles in Friction Stir Additive Manufacturing of Aluminum Alloy A16061 241
MD Masud Rana
10.1 Introduction 242
10.2 Friction Stir Additive Manufacturing Process 245
10.3 Problem Statement Related to Friction Stir Additive Manufacturing 246
10.4 Proposed Machine Learning Algorithm for Parameter Predictions of the FSAM Process 247
10.5 Results and Discussions 248
10.6 Conclusion 252
11 Enhancing Cybersecurity in EVs: Causal and Resilient ML-Based Detection of False Data Injection and Denial of Service Attacks 255
MD Masud Rana, Amir Shahirinia, Amer Dawoud and Ahad Ali
11.1 Introduction 256
11.2 Problem Statement 259
11.3 Proposed Methodology 263
11.4 Simulation Results and Discussions 264
11.5 EV Cyberattack CVSS Assessment 266
11.6 Conclusion and Future Work 268
12 Cyber Resilience in Industry 4.0: Causal and Ethical ML-Based Detection of Cyberattacks in Smart Manufacturing Systems 273
MD Masud Rana
12.1 Introduction 274
12.2 Proposed Methodology and Algorithm 276
12.3 Simulation Results and Discussions 277
12.4 End-to-End Software Development Life Cycle 286
12.5 Action Recommendations 287xvi Contents
12.6 Intellectual Merit and Broader Impacts 287
12.7 Conclusions 288
13 Explainable Privacy-Preserving Federated LLM Fine-Tuning on Distributed Edge Healthcare Infrastructure 293
Amit Chakraborty, Chirantana Mallick, Saptarshi Das, Madhvi Chakraborty and Abhijnan Chakraborty
13.1 Introduction 294
13.2 Literature Review and Gap Identification 295
13.3 Prerequisites 296
13.4 Privacy Enhancement Technique 299
13.5 Dataset 301
13.6 Proposed Architecture 301
13.7 Architectural Telemetry 310
13.8 Explainability 314
13.9 Conclusion 315
13.10 Future Work 317
14 Enhancing the Robustness of Causal AI Systems against Adversarial Threats 323
Sakshi Thakur, Mohit Singh Bisht and Dinesh Singh Dhakar
14.1 Introduction 324
14.2 Fundamentals of Causal AI 326
14.3 Nature of Adversarial Threats in Causal AI 329
14.4 Challenges in Ensuring Robustness 331
14.5 Strategies for Enhancing Robustness 332
14.6 Case Studies and Experiments 335
14.7 Future Directions 339
14.8 Conclusion 340
References 341
Index 343