Women in computational intelligence : key advances and perspectives on emerging topics / / edited by Alice E. Smith

• Record Nr.: UNINA9910561299403321
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2022]; ©2022
• ISBN: 9783030790929; 9783030790912
• Descrizione fisica: 1 online resource (440 pages)
• Collana: Women in Engineering and Science
• Disciplina: 006.3082
• Soggetti: Dones en la ciència; Women in computer science
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Contents -- Amazing Grace - Computer Pioneer Admiral Grace Murray Hopper -- 1 Introduction -- 2 Early Years -- 3 Education and Early Career -- 4 World War II and Harvard -- 5 Moving into Industry -- 6 The First Compiler -- 7 The US Navy -- 8 Teaching Career -- 9 Awards and Honors -- 10 Lasting Influences -- References -- Part I Intelligence -- XAI: A Natural Application Domain for Fuzzy Set Theory -- 1 Introduction -- 2 Computing with Words -- 2.1 Formal Tools -- 2.1.1 Fuzzy Sets -- 2.1.2 Linguistic Variables and Fuzzy Partitions -- 2.1.3 Fuzzy Quantifiers -- 2.2 Examples of Methodological Utilisation -- 2.2.1 Fuzzy Databases -- 2.2.2 Fuzzy Linguistic Summaries -- 2.2.3 Extended Linguistic Summaries -- 2.2.4 Computing with Words Conclusion -- 3 Fuzzy Approximate Reasoning -- 3.1 General Principles -- 3.2 Generalised Modus Ponens -- 3.2.1 Modus Ponens -- 3.2.2 Fuzzy Extension -- 3.3 Other Reasoning Forms -- 3.3.1 Gradual Reasoning -- 3.3.2 Analogical Reasoning -- 3.3.3 Interpolative Reasoning -- 3.3.4 Fuzzy Reasoning Conclusion -- 3.4 Examples of Methodological Utilisation -- 3.4.1 Fuzzy Ontologies -- 3.4.2 Fuzzy Control -- 4 Fuzzy Machine Learning -- 4.1 Fuzzy Rule-Based Systems -- 4.2 Fuzzy Decision Trees -- 4.3 Fuzzy Clustering -- 5 Conclusion -- References -- Adaptive Psychological Profiling from Nonverbal Behavior - Why Are Ethics Just Not Enough to Build Trust? -- ; 1 Introduction -- 2 Machine-Based Automated Psychological Profiling -- 2.1 Nonverbal Behavior -- 2.2 Automated Psychological Profiling Systems -- 2.3 Ethical and Legal Implications of Automated Profiling -- 2.4 The Rise of Ethical Charters -- 3 Case Study 1: Deception -- 3.1 Overview of iBorderCtrl -- 3.2 Privacy and Security by Design -- 3.3 Project Ethics -- 3.4 Automated Deception Detection Tool -- 3.5 Media and Public Discourse.; 4 Case Study 2: Comprehension -- 4.1 FATHOM -- 4.2 COMPASS -- 5 Empowering the General Public Through Education -- 6 Conclusions -- References -- Conversational Intelligent Tutoring Systems: The State of the Art -- 1 Introduction -- 2 Conversational Intelligent Tutoring Systems -- 2.1 Design Challenges for CITS -- 3 Oscar CITS -- 3.1 Automatic Profiling of Learning Styles -- 3.1.1 Learning Styles Knowledge Engineering -- 3.1.2 Capturing CITS Behaviour Dataset -- 3.1.3 Learning Styles Prediction Approaches -- 4 Hendrix 2.0 CITS -- 4.1 Profiling Comprehension: Comprehension Assessment and Scoring System (COMPASS) -- 5 Ethical Use of `AI' in Education -- 6 Scalability Challenges for CITS -- 7 Conclusion and Future Directions -- References -- Design and Validation of a Mini-Game for Player Motive Profiling -- 1 Introduction -- 2 Game Design -- 2.1 Storyline -- 2.2 Game Mechanics -- 2.3 Non-player Characters -- 2.4 Gameplay -- 3 Experimental Validation -- 3.1 Analysing Play Strategies of Non-player Characters for Assessing Motivation -- 3.2 Analysing Features of Non-player Characters for Assessing Motivation -- 3.3 Analysing Individual Play and Social Network Phases for Assessing Motivation -- 4 Conclusion and Future Work -- References -- When AI Meets Digital Pathology -- Acronyms -- 1 Evolution of Digital Pathology -- 2 Data Issues and Preprocessing -- 3 Multiscale Convolutional Neural Networks for Tumor Detection -- 4 Cell Detection and Segmentation -- 4.1 Dilemmas Associated with Annotation Loading and Data Imbalance -- 4.2 Lessening the Burden of Manual Annotation -- 5 System Integration and Interoperability -- References -- Linguistic Intelligence as a Base for Computing Reasoning -- 1 Introduction -- 2 Language as a Tool for Communication -- 2.1 MLW -- 2.2 Sounds and Utterances Behavior -- 2.2.1 Spoken Language and L-Systems -- 2.2.2 Dragon Curves.; 2.2.3 Dialogue Content and Its Relation with Energy Distribution -- 2.2.4 Spoken Language Analyses -- 3 Conclusions and Future Work -- References -- Part II Learning -- Intrusion Detection: Deep Neural Networks Versus Super Learning -- 1 Introduction -- 2 Related Work -- 3 Approaches -- 3.1 Deep Neural Networks -- 3.2 Super Learner -- 3.3 Gradient Boosting Estimator -- 3.4 Random Forest Estimator -- 3.5 XGBoost -- 4 Data Set and Environment -- 4.1 Data Set -- 4.2 Evaluation Measures -- 4.3 H2O Environment -- 5 Experiments and Results -- 5.1 Parameter Setup -- 5.2 Experiments with DNN -- 5.3 Experiments with Superlearner -- 6 Conclusion -- References -- Lifelong Learning Machines: Towards Developing Optimisation Systems That Continually Learn -- 1 Introduction -- 2 Background and Motivation -- 2.1 Related Work -- 3 An Immune-Inspired Approach to Lifelong Learning -- 3.1 A Brief Primer on the Immune System -- 3.2 NELLI: Mapping to a Lifelong Learning Optimiser -- 4 Implementation -- 4.1 Application: Bin-Packing -- 4.2 Heuristic Generation -- 4.3 Network -- 5 Demonstration -- 6 Conclusion -- References -- Reinforcement Learning Control by Direct Heuristic Dynamic Programming -- 1 Introduction -- 2 The direction Heuristic Dynamic Programming (dHDP) as an Actor-Critic Type Reinforcement Learning Controller -- 2.1 Building Blocks of the dHDP -- 2.2 Properties of dHDP Learning Controller -- 3 Applications of the dHDP for Automatic Tuning ; of Prosthesis Control Parameters -- 3.1 The Robotic Knee Prosthesis Control Problem and Current Approaches -- 3.2 The dHDP for Automatic Tuning of Robotic Prosthesis -- 4 Conclusion -- References -- Distributed Machine Learning in Energy Management and Control in Smart Grid -- 1 Introduction -- 2 Emerging Trends in Distributed Energy Management and Control in Smart Grids -- 3 Distributed Control Algorithms in Smart Grids.; 3.1 Consensus-Based Approach -- 3.2 Dual Decomposition -- 3.3 Optimality Condition Decomposition -- 3.4 Population-Based Distributed Algorithms -- 4 Energy Management and Control in Grid-Connected Microgrids -- 4.1 Operation Cost -- 4.2 Power Losses -- 4.3 Network Voltage Regulation -- 4.4 Line Limits -- 4.5 Power Flow Modeling Constraints -- 4.6 Reactive Power Limit Constraints -- 4.7 Reactive Power Ramp-Rate Constraints -- 5 Proposed Distributed Algorithm for Optimal Control of Smart Grids -- 5.1 Consensus Algorithm -- 5.2 Choice of PSO as Basis for the Proposed Distributed CI Algorithm -- 5.3 Proposed Consensus-Based and Dimension-Distributed PSO-Based Algorithm -- 6 Simulation Results and Discussion -- 6.1 Simulation Studies on 30-Node Test System -- 6.1.1 Case Study 1-Convergence, Adaptability, and Performance Benchmark for the 30-Node Test System -- 6.1.2 Case Study 2-Performance Benchmark with Centralized Controller for 30-Node Test System -- 6.2 Simulation Studies on the 119-Node Test System -- 6.2.1 Case Study 1-Convergence, Adaptability, and Performance Benchmark for 119-Node Test System -- 6.2.2 Case Study 2-Performance Benchmark with Centralized Controller for 119-Node Test System -- 7 Conclusions -- References -- Part III Modeling -- Fuzzy Multilayer Perceptrons for Fuzzy Vector Regression -- 1 Introduction -- 2 Fuzzy Multilayer Perceptron with Cuckoo Search -- 3 Experiment Results -- 3.1 Yacht Hydrodynamics Data Set -- 3.2 Energy Efficiency Data Set -- 3.3 Ping River Data Set -- 4 Conclusion -- References -- Generalisation in Genetic Programming for Symbolic Regression: Challenges and Future Directions -- 1 Genetic Programming for Symbolic Regression -- 1.1 Symbolic Regression -- 1.2 Genetic Programming-An Evolutionary Computation Technique -- 1.2.1 Representation -- 1.2.2 Initialisation -- 1.2.3 Evaluation -- 1.2.4 Selection.; 1.2.5 Genetic Operators -- 1.3 GP for Symbolic Regression -- 2 Generalisation in GPSR -- 2.1 Concepts Related to Generalisation -- 2.1.1 Overfitting -- 2.1.2 Bias-Variance Decomposition -- 3 Enhancing the Generalisation Ability of GPSR -- 3.1 Data Sampling and Feature Selection -- 3.1.1 Data Sampling -- 3.1.2 Feature Selection for GPSR -- 3.2 Estimating Generalisation Errors -- 3.2.1 Validation Methods -- 3.2.2 Estimating Variance Errors -- 3.2.3 Model Complexity and Generalisation Error -- 3.3 Improving Selection and Genetic Operators for Better Generalisation -- 3.4 Ensemble Learning -- 4 Conclusions and Future Directions -- References -- Neuroevolutionary Models Based on Quantum-Inspired Evolutionary Algorithms -- 1 Introduction -- 2 Quantum-Inspired Evolutionary Algorithms -- 2.1 Quantum-Inspired Evolutionary Algorithm with Real Representation (QIEA-R) -- 2.2 Quantum-Inspired Evolutionary Algorithm with Binary-Real Representation (QIEA-BR) -- 2.3 Quantum-Inspired Evolutionary Algorithm with Categorical Representation -- 3 Neuroevolutionary Models Based on QIEA -- 3.1 Multi-layer Perceptrons -- 3.2 Fully Recurrent Neural Networks -- 3.3 Echo States Networks -- 3.3.1 Phase 1: Global Parameter Optimisation -- 3.3.2 Phase 2: Reservoir Optimisation -- 3.4 Convolutional Neural Networks -- 4 Case Studies -- 4.1 Classification: Concept Drift Environment -- 4.1.1 Classification in Concept Drift Scenarios Using Quantum-Inspired Neuroevolution -- ; 4.1.2 Experiments -- 4.2 System Identification -- 4.2.1 Robot Arm Results -- 4.3 Deep Learning with Neural Architecture Search (Q-NAS) -- 5 Conclusions and Future Work -- References -- Weightless Neural Models: An Overview -- 1 Introduction -- 2 Weightless Neural Networks -- 3 Quantum Weightless Networks -- 4 Conclusions and Future Directions -- References -- Part IV Optimization.; Challenges Applying Dynamic Multi-objective Optimisation Algorithms to Real-World Problems.