Kooperativer Bibliotheksverbund

UID:

Format: XXI, 485 p. 141 illus., 107 illus. in color. , online resource.

Edition: 1st ed. 2024.

ISBN: 9789819998364

Series Statement: Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications,

Content: This groundbreaking proceedings volume explores the integration of Artificial Intelligence (AI) across key domains-healthcare, finance, education, robotics, industrial and other engineering applications -unveiling its transformative potential and practical implications. With a multidisciplinary lens, it transcends technical aspects, fostering a comprehensive understanding while bridging theory and practice. Approaching the subject matter with depth, the book combines theoretical foundations with real-world case studies, empowering researchers, professionals, and enthusiasts with the knowledge and tools to effectively harness AI. Encompassing diverse AI topics-machine learning, natural language processing, computer vision, data analytics and supervisory control - the volume showcases state-of-the-art techniques propelling AI advancements. Structured into four parts: Part 1: Artificial Intelligence (AI), explores evolving deep neural networks, reinforcement learning, and explainable AI, providing a deep dive into the technical foundations of AI advancements. Part 2: Robotics and Control Systems, delves into the integration of AI in robotics and automatic control, addressing supervisory control, automated robotic movement coordination, anomaly detection, dynamic programming, and fault tolerance, offering insights into the evolving landscape of intelligent automation. Part 3: AI and Society, examines the societal impact of AI through chapters on ethical considerations, economic growth, environmental engagements, and hazard management, providing a holistic perspective on AI's role in shaping society. Part 4: PhD Symposium, presents the future of AI through cutting-edge research, covering legal and ethical dimensions, privacy considerations, and computationally efficient solutions, offering a glimpse into the next generation of AI advancements. Catering to a diverse audience-from industry leaders to students-the volume consolidates the expertise of renowned professionals, serving as a comprehensive resource for navigating the ever-evolving AI landscape. An essential reference for those staying at the forefront of AI developments. .

Note: Advances in AI - NLP, Vision, Voice, and Self-taught AI Systems -- Soft Computing and Evolutionary Computing -- Neuro Evolutionary Techniques -- Innovations in AI methods, techniques, and algorithms -- Explainable AI -- AI and Robotics -- AI and Automatic Control -- AI and Cloud -- AI and IoT -- Intelligent Engineering Systems, Expert Systems -- Knowledge-Based Systems.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9789819998357

Additional Edition: Printed edition: ISBN 9789819998371

Additional Edition: Printed edition: ISBN 9789819998388

Language: English

DOI: 10.1007/978-981-99-9836-4

URL: https://doi.org/10.1007/978-981-99-9836-4

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (489 pages)

Edition: First edition.

ISBN: 981-9998-36-0

Series Statement: Frontiers of Artificial Intelligence, Ethics and Multidisciplinary Applications Series

Note: Intro -- Preface -- Keynotes and Industrial Talks -- Contents -- Editors and Contributors -- Part I Artificial Intelligence (AI) -- 1 Evolving Deep Neural Networks for Continuous Learning -- 1.1 Introduction -- 1.2 Related Work -- 1.3 Designing Evolutionary Neural Networks -- 1.4 Proposed Approach -- 1.5 Experimental Setup -- 1.6 Evaluation Metrics -- 1.6.1 Confusion Matrix -- 1.6.2 Accuracy -- 1.7 Results -- 1.8 Conclusion -- 1.8.1 Future Work -- References -- 2 LLM Cognitive Judgements Differ from Human -- 2.1 Introduction -- 2.2 Previous Work -- 2.3 Methods -- 2.4 Results -- 2.5 Conclusion -- References -- 3 Exploring Affinity-Based Reinforcement Learning for Designing Artificial Virtuous Agents in Stochastic Environments -- 3.1 Introduction -- 3.2 Related Works -- 3.2.1 Machine Ethics -- 3.2.2 Regularization in Reinforcement Learning -- 3.3 Methodology -- 3.3.1 Environment -- 3.3.2 Affinity-Based Reinforcement Learning -- 3.3.3 Experimental Parameters -- 3.4 Results -- 3.4.1 Proximal Policy Optimization (PPO) -- 3.4.2 Performance of Ab-RL -- 3.4.3 Impact of Hyperparameters -- 3.5 Discussion and Future Work -- References -- 4 A Novel Hybrid Model of Word Embedding and Deep Learning to Identify Hate and Abusive Content on Social Media Platform -- 4.1 Introduction -- 4.2 Literature Survey -- 4.3 Machine Learning Approaches -- 4.4 Research Methodology and Model -- 4.4.1 Evaluation -- 4.5 Results and Discussion -- 4.6 Conclusion -- 4.7 Declaration -- References -- 5 The Machine Learning Pod (MLPod™) Canvas: An End-To-End Methodology to Design, Build, and Deploy ML Applications -- 5.1 Introduction -- 5.2 The MLPod™ Canvas -- 5.3 How to Complete the MLPod™ Canvas -- 5.4 Conclusion -- References -- 6 Explainable AI for Intelligent Tutoring Systems -- 6.1 Introduction -- 6.1.1 The Importance of XAI in Educational Applications. , 6.2 Explainable Adaptation and Content Generation for Language Learning -- 6.2.1 Leveraging XAI in the Reader Application -- 6.3 Evaluation -- 6.3.1 Evaluation of the Adaptive Components -- 6.3.2 Learning Outcomes -- 6.4 Conclusions -- References -- 7 Multi-objective Reward-Based Algorithms for the Complete Coverage Path Planning Problem on Arbitrary Grids -- 7.1 Introduction -- 7.2 Statement of the Problem -- 7.3 Heuristics and the Reward Function -- 7.3.1 Reward Function -- 7.3.2 Checkpoint System -- 7.3.3 Parameters -- 7.4 Simulations and Results -- 7.4.1 Performance Metrics -- 7.4.2 Results -- 7.5 Conclusions and Future Work -- References -- 8 Patching Security Vulnerabilities Using Stackelberg Security Games on Attack Graphs -- 8.1 Introduction -- 8.1.1 Motivation -- 8.1.2 Contribution and Article Structure -- 8.1.3 Related Work -- 8.2 The Model -- 8.2.1 Model Assumptions -- 8.2.2 Game Model -- 8.2.3 Optimizing Probabilities -- 8.2.4 Maximizing the Shortest Path (MXSP) Formulation -- 8.2.5 Adapting the MXSP-Formulation to an Instance of -- 8.3 Use Case: MiR-100 Attack Graph -- 8.3.1 Attack Graph Processing -- 8.3.2 Experimental Results -- 8.4 Conclusion and Future Work -- References -- 9 An Interpretability Evaluation Framework for Decision Tree Surrogate Model-Based XAIs -- 9.1 Introduction -- 9.2 Related Work -- 9.2.1 Subjective Evaluation for Interpretability -- 9.2.2 Objective Evaluation for Interpretability -- 9.3 Evaluation Framework -- 9.4 Quantification Method for Metrics -- 9.4.1 Complexity -- 9.4.2 Clarity -- 9.4.3 Consistency -- 9.4.4 Stability -- 9.4.5 Sufficiency -- 9.4.6 Causality -- 9.5 Experiments -- 9.5.1 Experiment Setup -- 9.5.2 Experiment Results -- 9.5.3 Experiment Summary -- 9.6 Case Study -- 9.7 Conclusion -- References. , 10 Optimizing Offshore Wind Turbine Reliability and Costs Through Predictive Maintenance and SCADA Data Analysis -- 10.1 Introduction -- 10.1.1 Maintenance Optimization in Wind Industry -- 10.1.2 Outline -- 10.2 Literature Review -- 10.2.1 Regression-Based Research Works -- 10.2.2 Related Works on Dataset -- 10.3 Methodology -- 10.3.1 Data Acquisition and Data Pre-processing -- 10.3.2 Feature Selection -- 10.3.3 Regression-Based Model -- 10.3.4 Evaluation Metrics of Models -- 10.3.5 Post-processing -- 10.4 Results -- 10.5 Effectiveness of Our Fault Detection Approach -- 10.6 Conclusion -- References -- 11 Faster Complex Lyapunov Equation Solution Selection -- 11.1 Introduction -- 11.2 Augmented (Widely Linear) Model -- 11.3 Time-Invariant Augmented Complex Kalman Filter -- 11.4 Per Step Algorithms for the Solution of the Complex Lyapunov Equation -- 11.4.1 Direct Per Step Algorithm -- 11.4.2 Analytical Per Step Algorithm -- 11.4.3 Dual Per Step Algorithm -- 11.5 Doubling Algorithms for the Solution of the Complex Lyapunov Equation -- 11.5.1 Direct Doubling Algorithm -- 11.5.2 Analytical Doubling Algorithm -- 11.5.3 Dual-Doubling Algorithm -- 11.6 Computational Requirements -- 11.7 Conclusions -- References -- 12 Multi-objective Optimization of Federated Learning Systems in the Computing Continuum -- 12.1 Introduction -- 12.2 Related Work -- 12.3 Model -- 12.3.1 Resource Model -- 12.3.2 Offloading Model -- 12.3.3 Optimization Objectives -- 12.3.4 Problem Formulation -- 12.4 Architecture -- 12.5 Evaluation Testbed and Metrics -- 12.6 Evaluation Results -- 12.7 Conclusions and Future Direction -- References -- 13 One-Day-Ahead Wind Speed Forecasting Based on Advanced Deep and Hybrid Quantum Machine Learning -- 13.1 Introduction -- 13.2 The Proposed Deep Learning Model Framework -- 13.2.1 Dataset Presentation. , 13.2.2 Presentation of the Proposed Deep Learning Models -- 13.3 Wind Data Preprocessing and Forecasting Model Configurations -- 13.3.1 Wind Velocity Data Preprocessing -- 13.3.2 Forecasting Strategies -- 13.3.3 ML Forecasting Models Architecture and Configuration -- 13.4 Deep Learning Forecasting Models Performance and Comparison -- 13.4.1 Deep Learning Forecasting Performance Error Metrics -- 13.4.2 Deep Machine Learning Forecasting Performance for Wind Speed -- 13.5 Conclusions -- References -- Part II Robotics and Control Systems -- 14 An Advanced Robotic System Utilizing Convolutional Neural Networks for Recycling -- 14.1 Introduction -- 14.2 Related Work -- 14.3 Methodology -- 14.4 The Proposed System -- 14.5 Discussion and Conclusions -- References -- 15 Anomaly Detection in Multi-robot Systems Exploiting Self-Awareness -- 15.1 Introduction -- 15.2 Related Work -- 15.3 Hierarchical DBN Modeling -- 15.3.1 Offline Training -- 15.3.2 Online Testing -- 15.4 Coupled DBNs for Multi-robot Systems -- 15.5 Experimental Setup and Results -- 15.6 Conclusion -- References -- 16 A Mixed Analytic/Metaheuristic Dual Stage Control Scheme Toward I/O Decoupling for a Differential Drive Mobile Robot -- 16.1 Introduction -- 16.2 Dynamics of the Differential Drive Mobile Robot -- 16.2.1 Mobile Robot Non-linear Dynamics -- 16.2.2 Linear Approximant -- 16.3 Controller Design -- 16.3.1 Inner PI Controller Design -- 16.3.2 A Multivariable PID Controller Toward Independent Control of the Performance Variables of the Robot -- 16.4 Controller Parameter Tunning Through a Metaheuristic Algorithm -- 16.5 Performance of the Control Scheme -- 16.6 Conclusions -- References -- 17 A Reconfigurable Supervisory Control Algorithm for the Parametric Model of Multi-elevator Systems in Mines -- 17.1 Introduction -- 17.2 Mathematical Model of the i-th Mine Elevator System. , 17.2.1 The States of the i-th Elevator -- 17.2.2 The Events of the i-th Elevator -- 17.2.3 The Transitions of the i-th Elevator -- 17.3 Modeling of the n + m Elevators System -- 17.4 Desired Behavior of the System -- 17.4.1 Desired Behavior of Material/Wagon Elevator -- 17.4.2 Desired Behavior of Personnel Elevators -- 17.5 Regular Languages -- 17.5.1 Regular Languages of Material/Wagon Elevator -- 17.5.2 Regular Languages of Personnel Elevators -- 17.6 Supervisory Control Scheme -- 17.6.1 Supervisor of the First Rule -- 17.6.2 Supervisor of the Second Rule -- 17.6.3 Supervisor of the Third Rule -- 17.6.4 Supervisor of the Fourth Rule -- 17.6.5 Supervisor of the Fifth Rule -- 17.6.6 Supervisor of the Sixth Rule -- 17.7 The Controlled Elevator System -- 17.8 Conclusion -- References -- 18 Design and Implementation of a 3D Printed Robotic Vision System Connected to an Ontology-Based Editor for Manuscript Transcription and Annotation -- 18.1 Introduction -- 18.2 Manuscripts and Ontologies -- 18.2.1 Stroke, the Fundamental Unit for Handwritten Text Creation -- 18.3 Design of a Low-Cost Robotic System via 3D Printing Technology -- 18.4 Manuscript Processing via Machine Vision -- 18.5 Connecting the System to an Ontology-Based Editor -- 18.6 Conclusions -- References -- 19 Predictive Dynamic Programming Heuristic Approach for Inventory Control -- 19.1 Introduction -- 19.2 Configuration of the Case Example -- 19.3 Optimal Policy Design -- 19.4 Simulation Setup -- 19.5 Simulation Results -- 19.6 Conclusions and Directions for Future Work -- References -- 20 Modeling and Supervisor Design for a Baggage Handling System -- 20.1 Introduction -- 20.2 The Analytic Model of the Baggage Handling System -- 20.2.1 A DES Description of the Motors -- 20.2.2 A DES Description of the Sensors -- 20.2.3 A DES Description of the TRS Systems -- 20.2.4 TRS Nodes in a BHS. , 20.3 Desired Behavior and Supervisor Design Toward TRS Overflow Avoidance.

Additional Edition: ISBN 981-9998-35-2

Language: English