Artificial Intelligence: Theory and Applications: 973 (Studies in Computational Intelligence, 973)

Description

This book is an up-to-date collection, in AI and environmental research, related to the project ATLAS. AI is used for gaining an understanding of complex research phenomena in the environmental sciences, encompassing heterogeneous, noisy, inaccurate, uncertain, diverse spatio-temporal data and processes. The first part of the book covers new mathematics in the field of AI: aggregation functions with special classes such as triangular norms and copulas, pseudo-analysis, and the introduction to fuzzy systems and decision making. Generalizations of the Choquet integral with applications in decision making as CPT are presented.  The second part of the book is devoted to AI in the geo-referenced air pollutants and meteorological data, image processing, machine learning, neural networks, swarm intelligence, robotics, mental well-being and data entry errors. The book is intended for researchers in AI and experts in environmental sciences as well as for Ph.D. students. Preface Contents Theory Mathematical Foundation of Artificial Intelligence 1 Introduction 2 Aggregation Functions 2.1 General Definition 2.2 Triangular Norm 2.3 Copulas 3 Fuzzy Systems 3.1 Fuzzy Logics 3.2 Fuzzy Sets 4 Pseudo-analysis 4.1 Pseudo-calculus 4.2 Applications 5 Fuzzy Measures and Corresponding Integrals 6 Decision Making Based on Non-additive Measures and Integrals 6.1 Integral-Based Premium Principles 6.2 General CPT-Like Integral-Based Premium Principle 7 Recent Generalizations of the Choquet Integral 7.1 Fuzzy Integral with Pseudo-operations 7.2 Choquet Integral Based on Sublinear Means 7.3 Superdecomposition Integral 7.4 Choquet Integral Based on Two Fuzzy Measures References Collection and Decomposition Integrals in Multicriteria-Decision Support 1 Introduction 2 Preliminaries 3 Examples and Basic Properties of Decomposition Integrals 4 Decomposition Integrals Extending Lebesgue Integral 5 Decomposition Integrals for Interval-Valued Functions 6 Applications of Decomposition Integrals 7 Concluding Remarks References A Refinement of the Jensen Type Inequality for the Pseudo-integral 1 Introduction 2 Preliminaries 3 The Jensen Inequality for the Pseudo-integral 4 The Main Results 5 Conclusions References Convolutional Neural Networks Hyperparameters Tuning 1 Introduction 2 Convolutional Neural Networks 2.1 Architecture of Convolutional Neural Networks 3 Hyperparameters Tuning 3.1 Swarm Intelligence Algorithms 4 Conclusion References The Case for Quantifying Artificial General Intelligence with Entropy Semifields 1 Introduction: The Need for Bio-inspiration in AI 2 The Case for Modelling Explicit Information Flows in Neural Tissue 2.1 Intelligence is Embodied, Situated 2.2 Neural Level Hierarchies: The Multi-scale Brain 2.3 The Predictive Mind 2.4 Free Energy Models 2.5 Complex Networks of the Brain 2.6 Algebras for Entropy Flows in the Brain 3 Discussion: The Way Forward 3.1 Entropic Models Using Tropical Maths 3.2 Test Neural System: The Importance of a Lowly Worm References Fuzzy Metrics and Its Applications in Image Processing 1 Introduction 2 Preliminaries 3 Fuzzy Metrics 4 Image Filtering 4.1 Measures of Image Quality 4.2 Experiments 5 Copy-Move Forgery Detection in Images 5.1 Metaheuristics 5.2 The Experiment 6 Conclusion References Aggregation Operators and Distributivity Equations 1 Introduction 2 Preliminaries 2.1 Uninorms 2.2 Associative a-CAOA 2.3 Distributivity Equations 3 Distributivity on the Complete Domain 3.1 Distributivity for T-Uninorms in Umax 3.2 Distributivity for Nullnorms 4 Distributivity on the Restricted Domain 4.1 Conditional Distributivity for T-Uninorms in Umax 4.2 Conditional Distributivity for Nullnorms 5 Conclusions References The Use of Fuzzy Logic in Various Combinatorial Optimization Problems 1 Introduction 1.1 Particle Swarm Optimization Metaheuristic 2 Covering Location Problems 2.1 Location Set Covering Problem 2.2 Maximal Covering Location Problem 2.3 Minimal Covering Location Problem 3 Fuzzy Covering Location Problems 3.1 Definitions and Preliminaries 3.2 Fuzzy Location Set Covering Problem 3.3 Fuzzy Maximal Covering Location Problem 3.4 Fuzzy Minimal Covering Location Problem 4 Computational Experiments 5 Conclusion References An Improved BAT Algorithm for Solving Job Scheduling Problems in Hotels and Restaurants 1 Introduction 2 Literature Review 3 The Bat Algorithm 4 The Modified Bat Algorithm 5 Implementation 5.1 Benchmark Test Functions 6 Results and Discussion 6.1 The Average Value and the Standard Deviation 6.2 Statistical Tests 6.3 The MBA Verses the BA 7 Conclusion References Applications Patterns of PCB-138 Bioaccumulation in Small Pelagic Fish from the Eastern Mediterranean Sea Using Explainable Machine Learning Prediction 1 Introduction 2 Materials and Methods 2.1 Sampling 2.2 Chemical Analyses of POPs 2.3 Chemical Analyses of Elements 2.4 Chemical Analyses of Fatty Acids 2.5 Data Analysis 3 Results and Discussion 3.1 Fatty Acid Content 3.2 Pollutant Toxicological Profile 3.3 PCB-138 Patterns 4 Conclusion References Patterns of PCB-138 Occurrence in the Breast Milk of Primiparae and Multiparae Using SHapley Additive exPlanations Analysis 1 Introduction 2 Materials and Methods 2.1 Sampling 2.2 Chemical Analysis of PCBs and OCPs 2.3 Data Analysis 3 Results and Discussion 3.1 Pollutant Profiles 3.2 Interrelations of Pollutant Patterns 4 Conclusion References What Information on Volatile Organic Compounds Can Be Obtained from the Data of a Single Measurement Site Through the Use of Artificial Intelligence? 1 Introduction 2 Materials and Methods 3 Results and Discussion 3.1 BTEX Levels Surrounding the Receptor Site 3.2 Seasonal and Daily BTEX Variations 3.3 BTEX Forecasting Based on Meteorological Variables as Predictors 3.4 The Importance of Other Pollutants as Predictors for BTEX Levels 3.5 The Interdependence of BTEX Level Predictors 3.6 Pollutant Correlations and BTEX Origin 3.7 BTEX Ratios 4 Conclusions References The Linear Fuzzy Space: Theory and Applications 1 Introduction 2 Linear Fuzzy Space-Based Models of Planar Spatial Objects, Their Relations, and Topology 2.1 The Basics 2.2 Basic Fuzzy Plane Geometry Objects 2.3 Basic Spatial Relations Between Fuzzy Plane Geometry Objects 2.4 Measurement in the Linear Fuzzy Space 3 Applications of the Linear Fuzzy Space in Spatial and Temporal Modelling 3.1 Spatial Modelling—Model of 2D Fuzzy Spatial Relations 3.2 Temporal Modelling—Linear Fuzzy Space-Based Time Series 4 Examples of Linear Fuzzy Space Applications 4.1 2D Image Segmentation and Feature Extraction Based on Linear Fuzzy Space 4.2 Forecasting Concentrations of Particles Using Linear Fuzzy Space-Based Time Series 5 Conclusion References Image Fuzzy Segmentation Using Aggregated Distance Functions and Pixel Descriptors 1 Introduction 2 Distance Functions and Aggregation Functions 2.1 Distance Functions 2.2 Aggregation Functions 3 Construction of New Distance Functions 3.1 Minimum and Maximum 3.2 Generalized Means 3.3 Ordered Weighted Average 3.4 Weighted Arithmetic Mean of Powers 3.5 Product of Powers 4 Image Segmentation 4.1 Pixel Descriptors 4.2 FCM Algorithm 5 Experimental Results 5.1 Image Segmentation Using OWA Constructed Distance Function 5.2 Image Segmentation Using WAMP Constructed Distance Function 5.3 Image Segmentation Using Distance Function Constructed with Product-Type Aggregation Function 6 Conclusions and Additional Remarks References A Generative Model for the Creation of Large Synthetic Image Datasets Used for Distance Estimation 1 Introduction 2 Scene Creation 3 Neural Network 4 Training Process 5 Result 6 Conclusion References Appraisal of Apartments in Belgrade Using Hedonic Regression: Model Specification, Predictive Performance, Suitability for Mass Appraisal, and Comparison with Machine Learning Methods 1 Introduction 2 Data and Methods 2.1 Data 2.2 Method 3 Results 3.1 Regression Output 3.2 Model's Predictive Performance and Suitability for Mass Appraisal 3.3 Supplementary Analysis: Comparison with Three Widely Used Machine Learning Methods 4 Discussion and Conclusions References The Role of Chatbots in Foreign Language Learning: The Present Situation and the Future Outlook 1 Introduction 2 Technology for Learning Languages 2.1 Web and Mobile Technologies 2.2 Gamified Technologies 3 Dimensions for Analysis 4 Chatbots for Learning Languages 5 Implications for Future Research 5.1 Implications for Knowledge and Education 5.2 Implications for the Economy 5.3 Implications for People and Society 6 The Reference Architecture 7 Limitations 8 Conclusion References Intelligent Interactive Technologies for Mental Health and Well-Being 1 Introduction 2 Related Work 2.1 Robotic Technologies 2.2 Video Games 2.3 Conversational Agents 3 Analytical Framework 3.1 Transparency and Explainability 3.2 Privacy 3.3 Error Management 3.4 Context Awareness 3.5 Learning and Personalization 3.6 Empathy and Social Behavior 3.7 Healthcare Provision 4 Discussion and Outlook 4.1 Discussion 4.2 Transparency and Explainability 4.3 Privacy 4.4 Error Management 4.5 Context Awareness 4.6 Learning and Personalization 4.7 Empathy and Social Behavior 4.8 Healthcare Provision 4.9 The Outlook 5 Conclusion and Limitations References