Comprehensive guide offering actionable strategies for enhancing human-centered AI, efficiency, and productivity in industrial and systems engineering through the power of AI. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is the first book in the Advances in Industrial and Systems Engineering series, offering insights into AI techniques, challenges, and applications across various industrial and systems engineering (ISE) domains. Not only does the book chart current AI trends and tools for effective integration, but it also raises pivotal ethical…mehr
Comprehensive guide offering actionable strategies for enhancing human-centered AI, efficiency, and productivity in industrial and systems engineering through the power of AI. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is the first book in the Advances in Industrial and Systems Engineering series, offering insights into AI techniques, challenges, and applications across various industrial and systems engineering (ISE) domains. Not only does the book chart current AI trends and tools for effective integration, but it also raises pivotal ethical concerns and explores the latest methodologies, tools, and real-world examples relevant to today's dynamic ISE landscape. Readers will gain a practical toolkit for effective integration and utilization of AI in system design and operation. The book also presents the current state of AI across big data analytics, machine learning, artificial intelligence tools, cloud-based AI applications, neural-based technologies, modeling and simulation in the metaverse, intelligent systems engineering, and more, and discusses future trends. Written by renowned international contributors for an international audience, Advances in Artificial Intelligence Applications in Industrial and Systems Engineering includes information on: * Reinforcement learning, computer vision and perception, and safety considerations for autonomous systems (AS) * (NLP) topics including language understanding and generation, sentiment analysis and text classification, and machine translation * AI in healthcare, covering medical imaging and diagnostics, drug discovery and personalized medicine, and patient monitoring and predictive analysis * Cybersecurity, covering threat detection and intrusion prevention, fraud detection and risk management, and network security * Social good applications including poverty alleviation and education, environmental sustainability, and disaster response and humanitarian aid. Advances in Artificial Intelligence Applications in Industrial and Systems Engineering is a timely, essential reference for engineering, computer science, and business professionals worldwide.
WALDEMAR KARWOWSKI is a Pegasus Professor and Chair in the Department of Industrial Engineering and Management Systems at the University of Central Florida. He is an elected member of The Academy of Science, Engineering and Medicine of Florida (ASEMFL). VINCENT DUFFY is a Professor of Industrial Engineering and Agricultural & Biological Engineering at Purdue University and a Fulbright Senior Scholar. GAVRIEL SALVENDY is a University Distinguished Professor at the University of Central Florida, a member of the National Academy of Engineering, and founding Department Head of Industrial Engineering at Tsinghua University in China.
Inhaltsangabe
About the Editors xxiii Preface xxv 1 Introduction to Industrial Artificial Intelligence 1 Dai-Yan Ji, Hanqi Su, Takanobu Minami, and Jay Lee, USA 1.1 Fundamental Problems in Industry 1 1.2 The Purpose of Industrial AI 2 1.3 Difference Between AI and Industrial AI 4 1.4 Definition and Meaning of Industrial AI 5 1.5 Key Elements in Industrial AI: ABCDE 7 1.6 CPS Framework for Industrial AI 8 1.7 Technological Elements of CPS Framework 9 1.8 Developing Industrial AI Talents 10 1.9 Training Industrial AI Talents Using Open-source Datasets 10 1.10 Issues in Industrial AI 14 1.11 Conclusion 16 2 Autonomous Systems and Intelligent Agents 19 Babak Ebrahimi Soorchaei, Arash Raftari, and Yaser Fallah, USA 2.1 Definitions and Scopes 19 2.2 Core Concepts and Components 21 2.3 Applications and Case Study: Autonomous Vehicle 27 2.4 Challenges and Future Directions 37 3 Natural Language Processing for Industrial and Systems Engineering 43 Daniel Braun, Germany 3.1 Introduction 43 3.2 Advances and Trends in NLP 44 3.3 Domain-specific Challenges in ISE 47 3.4 Applications of NLP in ISE 50 3.5 Outlook 52 4 Smart Manufacturing, Robotics, and AI Systems 61 Xifan Yao, Huifeng Yan, Jiajun Zhou, Yongxiang Li, and Hongnian Yu, China/UK 4.1 Introduction to Smart Manufacturing 61 4.2 Smart Manufacturing System Integration and Interoperability 63 4.3 Robotics in Manufacturing 67 4.4 AI in Manufacturing 70 5 Artificial Intelligence in Healthcare 79 Vinita Gangaram Jansari, USA 5.1 History of Artificial Intelligence in Healthcare 79 5.2 New Age of Healthcare with the Use of AI 81 5.3 AI-enabled Medical Devices 85 5.4 Explainable AI for Healthcare 86 5.5 Medical Decision Support Systems 87 5.6 Precision/Personalized Medicine Using AI 88 5.7 Smart Healthcare 89 5.8 Healthcare 5.0 90 5.9 Ethics, Bias, and Fairness Constraints 94 5.10 Concluding Remarks 96 5.11 Future Directions 96 6 Artificial Intelligence in Cybersecurity for Industrial and Systems Engineering 111 Robin Yeman, Hasan Yasar, Suzette Johnson, and Tracy Bannon, USA 6.1 Introduction to Cybersecurity and Artificial Intelligence for Industrial and Systems Engineering 111 6.2 Cyber Threat Landscape for CPS 113 6.3 AI in Cybersecurity for CPS 113 6.4 Risk Assessment, Compliance, and Regulatory Considerations 115 6.5 Threat Detection and Prevention 116 6.6 Incident Response and Management 118 6.7 Anti-phishing 120 6.8 Dependable Authentication 120 6.9 Behavior Analytics 121 6.10 Conclusion 121 7 Artificial Intelligence in Defense 125 Dylan Schmorrow, Robert Sottilare, Jack Zaientz, John Sauter, Randolph Jones, Charles Newton, Joseph Cohn, Jon Sussman-Fort, Robert Bixler, Brice Colby, Victor Hung, Jeffrey Craighead, Le Nguyen, and Ullice Pelican, USA 7.1 Introduction 125 7.2 Ethical Considerations and Challenges 126 7.3 AI-driven Innovations in C2 Systems 129 7.4 AI Applications in Uncrewed Systems 132 7.5 Application of AI to Cyber Operations 134 7.6 AI-enabled Training and Simulation Systems 137 7.7 AI-enabled HMI Technologies 143 7.8 Integrating Machine Reasoning and Explanation for Dynamic Decision-making 146 7.9 Responsible AI in Predictive Systems and Medical/Defense Health Readiness 148 7.10 Future Directions 150 7.11 Conclusion 154 8 AI-Driven Management and Modeling Decision Optimization as a Timely Opportunity at the US Department of Defense 159 Link Parikh, USA 8.1 Why Act Now and Why Engineering Lifecycle and AI? 159 8.2 Who Needs to Make Changes in the Ecosystem? 163 8.3 How to Implement the AI-driven Ecosystems Management and Modeling Regime 167 8.4 Key Elements of AI-driven Ecosystem Management and Modeling 169 8.5 Enhance Workforce Development and Mentorship 178 8.6 When Can We Acquire Dramatic Speed and Precision? 179 8.7 Which Elements Exist in "AI Ecosystem Management and Modeling?" 179 8.8 Sample Applications of Dramatic Speed and Precision 191 8.9 AI-driven Ecosystem Management and Modeling Solution and Toolset 192 8.10 Summary 194 9 Enhancing Cryptocurrency Market Forecasting: Advanced Machine Learning Techniques and Industrial Engineering Contributions 197 Jannatun Nayeem Pinky and Ramya Akula, USA 9.1 Introduction 197 9.2 Background 199 9.3 Methods 201 9.4 Dataset 224 9.5 Evaluation 236 9.6 Limitations 253 9.7 Future Recommendations 254 9.8 Conclusion 257 10 Artificial Intelligence in Aviation 263 Dr. Dimitrios Ziakkas, USA 10.1 Introduction to Artificial Intelligence in Aviation 263 10.2 AI in Flight Operations and Training 264 10.3 AI in Air Traffic Management 267 10.4 AI in Airport Operations 268 10.5 AI in Customer Experience and Service 270 10.6 AI in Maintenance and Technical Support 272 10.7 Human Factors and AI Integration 274 10.8 Ethical and Regulatory Challenges 275 10.9 AI Case Studies and Future Prospects 276 10.10 The Future of AI in Aviation 278 11 Enhancing Engineering Education: A Multimodal Approach to Personalization and Adaptation Using Artificial Intelligence in Game-based Learning 281 Roger Azevedo, Daryn Dever, and Megan Wiedbusch, USA 11.1 Context: Challenges in Engineering Education 281 11.2 GBLEs for Engineering Education: Are They Effective? 283 11.3 Personalization and Adaptivity in GBLEs 284 11.4 Personalization and Adaptivity in GBLEs for Engineering Education: Are They Effective? 284 11.5 Augmenting Personalization and Adaptivity in GBLEs in Engineering Education with Multimodal Trace Data 286 11.6 AI Techniques for Handling Multimodal Approaches to Individualization and Adaptation 288 11.7 Essential SRL Processes from Multimodal Trace Data with GBLEs in Engineering Education 289 11.8 Open Questions, Future Directions, and Conclusions 297 12 Securing Artificial Intelligence Systems in the Era of Large Language Models 307 Carmen-Gabriela Stefanita, USA 12.1 The Need for an Artificial Intelligence Risk Management Framework in an Evolving Artificial Intelligence Landscape 307 12.2 Security for AI Threat Model 313 12.3 Implementing a Security for AI Framework 317 12.4 Conclusion 323 13 Responsible Artificial Intelligence Applications for Social Good 327 Ozlem Garibay and Brent Winslow, USA 13.1 Introduction 327 13.2 Ethical Aspects of AI for Social Good Applications 328 13.3 AI Applications for Healthcare 331 13.4 AI for Environmental Sustainability 335 13.5 AI for Education and Accessibility 338 13.6 AI in Humanitarian Efforts and Disaster Response 340 13.7 Conclusion 342 14 Future Directions and Applications of Artificial Intelligence 355 Ivan Garibay, Clayton Barham, Sina Abdidizaji, Chathura Jayalath, USA 14.1 Introduction 355 14.2 Emerging Trends of AI for Industrial Engineering 356 14.3 Recent Applications 360 14.4 Future Directions: Explainable AI for Industrial Engineering 361 14.5 Case Study 365 References 366 Index 371
About the Editors xxiii Preface xxv 1 Introduction to Industrial Artificial Intelligence 1 Dai-Yan Ji, Hanqi Su, Takanobu Minami, and Jay Lee, USA 1.1 Fundamental Problems in Industry 1 1.2 The Purpose of Industrial AI 2 1.3 Difference Between AI and Industrial AI 4 1.4 Definition and Meaning of Industrial AI 5 1.5 Key Elements in Industrial AI: ABCDE 7 1.6 CPS Framework for Industrial AI 8 1.7 Technological Elements of CPS Framework 9 1.8 Developing Industrial AI Talents 10 1.9 Training Industrial AI Talents Using Open-source Datasets 10 1.10 Issues in Industrial AI 14 1.11 Conclusion 16 2 Autonomous Systems and Intelligent Agents 19 Babak Ebrahimi Soorchaei, Arash Raftari, and Yaser Fallah, USA 2.1 Definitions and Scopes 19 2.2 Core Concepts and Components 21 2.3 Applications and Case Study: Autonomous Vehicle 27 2.4 Challenges and Future Directions 37 3 Natural Language Processing for Industrial and Systems Engineering 43 Daniel Braun, Germany 3.1 Introduction 43 3.2 Advances and Trends in NLP 44 3.3 Domain-specific Challenges in ISE 47 3.4 Applications of NLP in ISE 50 3.5 Outlook 52 4 Smart Manufacturing, Robotics, and AI Systems 61 Xifan Yao, Huifeng Yan, Jiajun Zhou, Yongxiang Li, and Hongnian Yu, China/UK 4.1 Introduction to Smart Manufacturing 61 4.2 Smart Manufacturing System Integration and Interoperability 63 4.3 Robotics in Manufacturing 67 4.4 AI in Manufacturing 70 5 Artificial Intelligence in Healthcare 79 Vinita Gangaram Jansari, USA 5.1 History of Artificial Intelligence in Healthcare 79 5.2 New Age of Healthcare with the Use of AI 81 5.3 AI-enabled Medical Devices 85 5.4 Explainable AI for Healthcare 86 5.5 Medical Decision Support Systems 87 5.6 Precision/Personalized Medicine Using AI 88 5.7 Smart Healthcare 89 5.8 Healthcare 5.0 90 5.9 Ethics, Bias, and Fairness Constraints 94 5.10 Concluding Remarks 96 5.11 Future Directions 96 6 Artificial Intelligence in Cybersecurity for Industrial and Systems Engineering 111 Robin Yeman, Hasan Yasar, Suzette Johnson, and Tracy Bannon, USA 6.1 Introduction to Cybersecurity and Artificial Intelligence for Industrial and Systems Engineering 111 6.2 Cyber Threat Landscape for CPS 113 6.3 AI in Cybersecurity for CPS 113 6.4 Risk Assessment, Compliance, and Regulatory Considerations 115 6.5 Threat Detection and Prevention 116 6.6 Incident Response and Management 118 6.7 Anti-phishing 120 6.8 Dependable Authentication 120 6.9 Behavior Analytics 121 6.10 Conclusion 121 7 Artificial Intelligence in Defense 125 Dylan Schmorrow, Robert Sottilare, Jack Zaientz, John Sauter, Randolph Jones, Charles Newton, Joseph Cohn, Jon Sussman-Fort, Robert Bixler, Brice Colby, Victor Hung, Jeffrey Craighead, Le Nguyen, and Ullice Pelican, USA 7.1 Introduction 125 7.2 Ethical Considerations and Challenges 126 7.3 AI-driven Innovations in C2 Systems 129 7.4 AI Applications in Uncrewed Systems 132 7.5 Application of AI to Cyber Operations 134 7.6 AI-enabled Training and Simulation Systems 137 7.7 AI-enabled HMI Technologies 143 7.8 Integrating Machine Reasoning and Explanation for Dynamic Decision-making 146 7.9 Responsible AI in Predictive Systems and Medical/Defense Health Readiness 148 7.10 Future Directions 150 7.11 Conclusion 154 8 AI-Driven Management and Modeling Decision Optimization as a Timely Opportunity at the US Department of Defense 159 Link Parikh, USA 8.1 Why Act Now and Why Engineering Lifecycle and AI? 159 8.2 Who Needs to Make Changes in the Ecosystem? 163 8.3 How to Implement the AI-driven Ecosystems Management and Modeling Regime 167 8.4 Key Elements of AI-driven Ecosystem Management and Modeling 169 8.5 Enhance Workforce Development and Mentorship 178 8.6 When Can We Acquire Dramatic Speed and Precision? 179 8.7 Which Elements Exist in "AI Ecosystem Management and Modeling?" 179 8.8 Sample Applications of Dramatic Speed and Precision 191 8.9 AI-driven Ecosystem Management and Modeling Solution and Toolset 192 8.10 Summary 194 9 Enhancing Cryptocurrency Market Forecasting: Advanced Machine Learning Techniques and Industrial Engineering Contributions 197 Jannatun Nayeem Pinky and Ramya Akula, USA 9.1 Introduction 197 9.2 Background 199 9.3 Methods 201 9.4 Dataset 224 9.5 Evaluation 236 9.6 Limitations 253 9.7 Future Recommendations 254 9.8 Conclusion 257 10 Artificial Intelligence in Aviation 263 Dr. Dimitrios Ziakkas, USA 10.1 Introduction to Artificial Intelligence in Aviation 263 10.2 AI in Flight Operations and Training 264 10.3 AI in Air Traffic Management 267 10.4 AI in Airport Operations 268 10.5 AI in Customer Experience and Service 270 10.6 AI in Maintenance and Technical Support 272 10.7 Human Factors and AI Integration 274 10.8 Ethical and Regulatory Challenges 275 10.9 AI Case Studies and Future Prospects 276 10.10 The Future of AI in Aviation 278 11 Enhancing Engineering Education: A Multimodal Approach to Personalization and Adaptation Using Artificial Intelligence in Game-based Learning 281 Roger Azevedo, Daryn Dever, and Megan Wiedbusch, USA 11.1 Context: Challenges in Engineering Education 281 11.2 GBLEs for Engineering Education: Are They Effective? 283 11.3 Personalization and Adaptivity in GBLEs 284 11.4 Personalization and Adaptivity in GBLEs for Engineering Education: Are They Effective? 284 11.5 Augmenting Personalization and Adaptivity in GBLEs in Engineering Education with Multimodal Trace Data 286 11.6 AI Techniques for Handling Multimodal Approaches to Individualization and Adaptation 288 11.7 Essential SRL Processes from Multimodal Trace Data with GBLEs in Engineering Education 289 11.8 Open Questions, Future Directions, and Conclusions 297 12 Securing Artificial Intelligence Systems in the Era of Large Language Models 307 Carmen-Gabriela Stefanita, USA 12.1 The Need for an Artificial Intelligence Risk Management Framework in an Evolving Artificial Intelligence Landscape 307 12.2 Security for AI Threat Model 313 12.3 Implementing a Security for AI Framework 317 12.4 Conclusion 323 13 Responsible Artificial Intelligence Applications for Social Good 327 Ozlem Garibay and Brent Winslow, USA 13.1 Introduction 327 13.2 Ethical Aspects of AI for Social Good Applications 328 13.3 AI Applications for Healthcare 331 13.4 AI for Environmental Sustainability 335 13.5 AI for Education and Accessibility 338 13.6 AI in Humanitarian Efforts and Disaster Response 340 13.7 Conclusion 342 14 Future Directions and Applications of Artificial Intelligence 355 Ivan Garibay, Clayton Barham, Sina Abdidizaji, Chathura Jayalath, USA 14.1 Introduction 355 14.2 Emerging Trends of AI for Industrial Engineering 356 14.3 Recent Applications 360 14.4 Future Directions: Explainable AI for Industrial Engineering 361 14.5 Case Study 365 References 366 Index 371
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