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Wiley-ISTE 2026.11.10.

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시리즈 태그 업체 공지사항 구성 소개 특별 구성 MD 한마디 카드뉴스 상세 이미지 소개 관련 동영상 목차 미리듣기 저자 소개 관련분류 품목정보 만든이 코멘트 예스24 리뷰 책 속으로 줄거리 캐스팅 관련 자료 부가 영상 출판사 리뷰 전문가 리뷰 추천평

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소개

<i>Artificial Intelligence in Decision Science</i> investigates the impact of artificial intelligence (AI) on decision-making. The book is a well-balanced mix of theory, practice and examples from the field to show how AI-based techniques can help in making data-driven decisions across a range of sectors, including business, healthcare, finance, manufacturing, education and public administration. This book explores the fundamental concepts of AI, such as machine learning, deep learning, natural language processing, generative AI and multi-agent systems, and their role in addressing modern decision-making problems. Practical insights are given on the adoption of AI, on how to implement AI and the impact of AI in case-study organization, which makes concepts accessible for researchers and practitioners alike. <br /><br />This book is an excellent reference guide for academics, students, industry and decision makers interested in effective application of AI techniques in the current context of decision sciences to facilitate smart, effective and informative decision making.

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1.1. Introduction 1
1.2. The problem of market noise 2
1.3. Foundations of AI in finance 3
1.4. AI tools for clarifying financial signals 5
1.5. Evidence from empirical studies 8
1.6. Practical considerations and implementation challenges 8
1.7. Ethical and regulatory considerations 9
1.8. Case studies 9
1.9. Future directions 10
1.10. Conclusion 10
1.11. References 11

Chapter 2. Neuromorphic Computing: Brain-Inspired Intelligence and Next-Generation AI Systems 13
Samiksha WALIA, Samdisha WALIA, Law Kumar SINGH and Rekha SINGH

2.1. Introduction 13
2.2. Biological inspiration and theoretical foundations 15
2.3. Core principles of neuromorphic computing 21
2.4. Neuromorphic hardware implementations 25
2.5. Software frameworks and simulation tools 30
2.6. Prospects and challenges 32
2.7. Conclusion 34
2.8. References 34

Chapter 3. EduFAITH: A Requirements Engineering Framework for Ethical and Human-Centered AI in Educational Decision Systems 37
Tanu SINGH and Aashna SUMAN

3.1. Introduction 38
3.2. Current practices and related work 40
3.3. Limitations in current RE for AI in education 42
3.4. The FAITH architecture 43
3.5. Methodology and outcomes. 44
3.6. EduFAITH 47
3.7. Solutions, conclusion and future directions 47
3.8. References 50

Chapter 4. Applications of AI in Healthcare Decision Support: A Systematic Review 53
Sakshi, Sakshi GUPTA and Hilal Ahmad SHAH

4.1. Introduction 53
4.2. ML algorithms: a new frontier in medical diagnostics 55
4.3. Contemporary approaches in ML 62
4.4. ML in the identification, diagnosis, treatment and control of TB 72
4.5. Ethics, bias, privacy and clinical decision consequences 74
4.6. Future prospects and conclusion 75
4.7. References 76

Chapter 5. Systematic Review Synthesizes Recent Advances in Computer Vision Decision Systems 81
Neelam KUMARI, Sunil Kumar CHAWLA and Shivani WADHWA

5.1. Introduction 81
5.2. Literature survey 90
5.3. Conclusion 96
5.4. References 97

Chapter 6. Machine Learning Insights Regarding the Impact of Weather and Region on Crops 101
Nidhi Bansal GARG, Atul GARG and Anurag JAIN

6.1. Introduction 101
6.2. Literature review 103
6.3. Methodology 105
6.4. Results and discussion 106
6.5. Conclusion 111
6.6. References 111

Chapter 7. AI-Enabled IoT Decision Support in Smart Agriculture: Strategies, Challenges and Case Studies 115
Tarun KUMAR, Sonia SUNEJA, Jyoti SAINI, Kanwal GARG, Rutuja Subhash MAGAR and Dolly RANI

7.1. Introduction 116
7.2. Literature review 120
7.3. AI-enabled decision support systems in smart agriculture 129
7.4. Challenges and limitations of AI-enabled IoT decision support systems in smart agriculture 135
7.5. Sustainability and energy optimization in AI-enabled IoT decision support systems for smart agriculture 141
7.6. Case studies, implementation frameworks and future directions 145
7.7. Conclusion and policy implications 150
7.8. References 151

Chapter 8. AI in Manufacturing and Supply Chain Management 159
Neetika JAIN

8.1. Introduction 159
8.2. Literature review 161
8.3. Discussion – AI transformation for manufacturing and supply chain 173
8.4. Conclusion 177
8.5. References 178

Chapter 9. AI in Educational Decision Systems 185
Anik ACHARJEE

9.1. Introduction 185
9.2. Literature review 188
9.3. Conclusion 192
9.4. References 193

Chapter 10. Reinforcement Learning-Driven Frameworks for Adaptive and Explainable Decision Intelligence 195
Basant Kumar VERMA, Parveen Kumar SHARMA, Shekhar SINGH, Dinesh Kumar GARG and Hemlata

10.1. Introduction 196
10.2. Literature review 199
10.3. Theoretical background 202
10.4. Methodology 205
10.5. Proposed reinforcement learning-driven framework 208
10.6. Experimental implementation and results 210
10.7. Discussion. 213
10.8. Applications and case studies 214
10.9. Challenges and future work 219
10.10. Conclusion 220
10.11. References 221

Chapter 11. Explainable AI for High-Stakes Decision-Making in Healthcare and Finance 225
Abdul Malik ANSARI

11.1. Introduction 226
11.2. Literature review 228
11.3. Research gap 231
11.4. Proposed methodology/framework 232
11.5. Results and discussions 235
11.6. Conclusion and future work 240
11.7. References 241

Chapter 12. AlzCare: Smart Assistance System for Daily Activities in Alzheimer's Patients 243
Riya KANSAL, Jappanjot KAUR, Parampreet KAUR, Law Kumar SINGH and Rekha SINGH

12.1. Introduction 243
12.2. Literature review 246
12.3. System architecture 249
12.4. Proposed methodology. 253
12.5. Results and discussion 260
12.6. Conclusion 271xii Artificial Intelligence in Decision Sciences
12.7. Acknowledgments 272
12.8. Declarations 272
12.9. References 273

Chapter 13. A Comprehensive Survey on Sentiment Analysis: Evolution from Machine Learning and Deep Learning to Large Language Models 275
Wesam AHMED

13.1. Introduction 275
13.2. Background 277
13.3. Real-world applications 284
13.4. Challenges and limitations 286
13.5. Key future directions 288
13.6. Conclusion 289
13.7. References 290

Chapter 14. Generative AI in Creative Industries 295
Gautam DAS, Prachi MISHRA, Shailja SAINI and Avneet KAUR

14.1. Introduction 295
14.2. Technical generative AI 299
14.3. Generative AI 303
14.4. Creativity, ethical and legal issues 306
14.5. Case studies in industry 310
14.6. Future directions 320
14.7. Conclusion 325
14.8. References 327

List of Authors 329
Index 333

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저자 소개

Jeff Saltz is an Associate Professor and Program Director for Applied Data Science, Information Systems, and Artificial Intelligence at Syracuse University, USA. His research focuses on data science, AI, collaborative analytics and experiential learning.

Shamik Tiwari is a Professor and Dean at IILM University, Gurugram, India. His research focuses on AI, machine learning, the Internet of Things and data analytics.

Anurag Jain is a Professor at IILM University, Gurugram, India. His research focuses on AI, machine learning, deep learning and explainable AI.

Prasenjit Chatterjee is a Professor of Mechanical Engineering and Dean (Research & Consultancy) at MCKV Institute of Engineering, West Bengal, India. His research focuses on multi-criteria decision-making, supply chain analytics, circular economy, and production and operations management.

Tanupriya Choudhury is a Professor at the University of Petroleum and Energy Studies (UPES), Dehradun, India. His research interests include AI, machine learning, the Internet of Things, healthcare informatics and smart computing.

Jasmina Tacheva is an Assistant Professor at Syracuse University, USA. Her research focuses on human-centered AI, computational social science, natural language processing and responsible AI, emphasizing trustworthy and socially informed intelligent systems.