Kurzfassung: Forcing the Network to use Human Explanations in its Inference Process -- Fuzzy Inference System-based Collision Avoidance of Unmanned Aerial Vehicles Optimized using Genetic Algorithm -- Fuzzy Logic-aided Inverse Kinematics Control for Redundant Manipulators -- Growth Kinetics of Gold Nanoparticles via p-fuzzy systems -- Optimization of Artificial Potential Field using Genetic Algorithm for Human-aware Navigation of Autonomous Mobile Robots -- Genetic Fuzzy Threat Assessment for Asteroids 2600 Derived Game -- Deep Learning ANFIS Architectures -- A Fuzzy Inference System for an Optimal Spacecraft Attitude State Trajectory -- Faster Algorithms for Estimating the Mean of a Quadratic Expression under Uncertainty -- People Prefer More Information About Uncertainty, But Perform Worse When Given This Information: An Explanation of the Paradoxical Phenomenon.

Kurzfassung: This book is an overview of latest successes and applications of fuzzy techniques—techniques that use expert knowledge formulated by natural-language words like "small". Engineering applications deal with aerospace (control of spacecrafts and unmanned aerial vehicles, air traffic control, airport passenger flow predictions), materials (designing gold nano-structures for medicine, catalysis, and sensors), and robot navigation and manipulation. Other application areas include cosmology, demographics, finances, wine production, medicine (diagnostics, epidemics control), and predicting human behavior. In many cases, fuzzy techniques are combined with machine learning AI. Due to natural-language origin of fuzzy techniques, such combination adds explainability (X) to AI. This book is recommended to students and practitioners interested in the state-of-the-art fuzzy-related XAI and to researchers willing to take on numerous remaining challenges.

Kurzfassung: Part 1. Soft Computing and Fuzzy Logic -- 1. Lotfi A. Zadeh’s Life and His Scientific Legacy -- 2. From Fuzzy Optimization to Possibilistic-Probabilistic Optimization with our Teacher Professor Lotfi Zadeh -- 3. Ideas of Lotfi Zadeh in Explainable Artificial Intelligence -- 4. Different Concepts, Similar Computational Complexity: Nguyen’s Results about Fuzzy and Interval Computations 35 Years. .

Kurzfassung: This book is a collection of papers presented during the 8th World Conference on Soft Computing in February 2022. The papers cover multiple areas important for soft computing. Some papers are dedicated to fundamental aspects of soft computing, i.e., fuzzy mathematics, type-2 fuzzy sets, evolutionary-based optimization, aggregation, and neural networks. Others emphasize the application of soft computing methods to data analysis, image processing, decision-making, classification, series prediction, economics, control, and modeling.

Kurzfassung: Part 1. Fuzzy Recognition -- 01. Difficulties in recognizing flat shapes based on Fuzzy logic -- 2. The problem of parsing and recognizing individual objects of information on base of fuzzy Logic -- 3. Call Center and Robot Call Center How to Reduce the Cost -- 4. An intelligent method for optimizing calculations in the recognition of sound information.

Kurzfassung: This book is a collection of papers presented during the 8th World Conference on Soft Computing in February 2022. The papers cover multiple areas important for soft computing. Some papers are dedicated to fundamental aspects of soft computing, i.e., fuzzy mathematics, type-2 fuzzy sets, evolutionary-based optimization, aggregation, and neural networks. Others emphasize the application of soft computing methods to data analysis, image processing, decision-making, classification, series prediction, economics, control, and modeling.

Kurzfassung: Preface -- directory Preface -- 1. Applications to Biology and Medicine -- Hunting Habits of Predatory Birds: Theoretical Explanation of an Empirical Formula -- Why Rectified Power (RePU) Activation Functions Are Efficient in Deep Learning: A Theoretical Explanation -- Aquatic Ecotoxicology: Theoretical Explanation of Empirical Formulas -- How Hot Is Too Hot -- How Order and Disorder Affect People's Behavior: An Explanation -- Shape of an Egg: Towards a Natural Simple Universal Formula -- A General Commonsense Explanation of Several Medical Results -- Why Immunodepressive Drugs Often Make People Happier -- Systems Approach Explains Why Low Heart Rate Variability Is Correlated with Depression (and Suicidal Thoughts) -- 2. Applications to Economics and Politics -- How to Make Inflation Optimal and Fair -- Why Seneca Effect? -- Why Rarity Score Is a Good Evaluation of a Non-Fungible Token -- Resource Allocation for Multi-Tasking Optimization: Explanation of an Empirical Formula -- Everyone Is Above Average: Is It Possible? Is It Good? -- How Probable is a Revolution? A Natural ReLU-Like Formula that Fits the Historical Data -- Why Should Exactly 1/4 Be Returned to the Original Owner: An Economic Explanation of an Ancient Recommendation -- Why Would Anyone Invest in a High-Risk Low-Profit Enterprise? -- Which Interval-Valued Alternatives Are Possibly Optimal If We Use Hurwicz Criterion -- How to Solve the Apportionment Paradox -- In the Absence of Information, the Only Reasonable Negotiation Scheme Is Offering a Certain Percentage of the Original Request: A Proof -- 3. Applications to Education -- How to Make Quantum Ideas Less Counter-Intuitive: A Simple Analysis of Measurement Uncertainty Can Help -- Physical Meaning Often Leads to Natural Derivations in Elementary Mathematics: On the Examples of Solving Quadratic and Cubic Equations -- Towards Better Ways to Compute the Overall Grade for a Class -- Why Some Theoretically Possible Representations of Natural Numbers Were Historically Used and Some Were Not: An Algorithm-Based Explanation -- 4. Applications to Engineering -- Dielectric Barrier Discharge (DBD) Thrusters -- Aerospace Engines of the Future: Invariance-Based Analysis -- Need for Optimal Distributed Measurement of Cumulative Quantities Explains the Ubiquity of Absolute and Relative Error Components -- Over-Measurement Paradox: Suspension of Thermonuclear Research Center and Need to Update Standards -- How to Get the Most Accurate Measurement-Based Estimates -- How to Estimate The Present Serviceability Rating of a Road Segment: Explanation of an Empirical Formula -- 5. Applications to Linguistics -- Word Representation: Theoretical Explanation of an Empirical Fact -- Why Menzerath's Law? -- 6. Applications to Machine Learning -- One More Physics-Based Explanation for Rectified Linear Neurons -- Why Deep Neural Networks: Yet Another Explanation -- 7. Applications to Mathematics -- Really Good Theorems Are Those That End Their Life as Definitions: Why -- 8. Applications to Physics -- How to Describe Hypothetic Truly Rare Events (with Probability 0) -- Spiral Arms Around a Star: Geometric Explanation -- Why Physical Power Laws Usually Have Rational Exponents -- Freedom of Will, Non-Uniqueness of Cauchy Problem, Fractal Processes, Renormalization, Phase Transitions, and Stealth Aircraft -- How Can the Opposite to a True Theory Be Also True? A Similar Talmudic Discussion Helps Make This Famous Bohr's Statement Logically Consistent -- How to Detect (and Analyze) Independent Subsystems of a Black-Box (or Grey-Box) System -- 9. Applications to Psychology and Decision Making -- Why Decision Paralysis -- Why Time Seems to Pass Slowly for Unpleasant Experiences and Quickly for Pleasant Experiences: An Explanation Based on Decision Theory -- How to Deal with Conflict of Interest Situations When Selecting the Best Submission -- Why Aspirational Goals: Geometric Explanation -- Why Hate: Analysis Based on Decision Theory -- Why Self-Esteem Helps to Solve Problems: An Algorithmic Explanation -- Why Five Stages of Solar Activity, Why Five Stages of Grief, Why Seven Plus Minus Two: A General Geometric Explanation -- 10. Applications to Software Engineering -- Anomaly Detection in Crowdsourcing: Why Midpoints in Interval-Valued Approach -- Unexpected Economic Consequence of Cloud Computing: A Boost to Algorithmic Creativity -- Unreachable Statements Are Inevitable In Software Testing: Theoretical Explanation -- 11. General Computational Techniques -- Why Constraint Interval Arithmetic Techniques Work Well: A Theorem Explains Empirical Success" -- "How to Describe Relative Approximation Error? A New Justification for Gustafson's Logarithmic Expression" -- Search Under Uncertainty Should be Randomized: A Lesson From the 2021 Nobel Prize in Medicine" -- Why Convex Combination Is an Effective Crossover Operation in Continuous Optimization: A Theoretical Explanation" -- "Why Optimization Is Faster than Solving Systems of Equations: A Qualitative Explanation" -- Estimating Skewness and Higher Central Moments of an Interval-Valued -- Fuzzy Set" -- How to Detect the Fundamental Frequency: Approach Motivated by Soft -- Computing and Computational Complexity" -- What If There Are Too Many Outliers?" -- What Is a Natural Probability Distribution on the Class of All Continuous Functions: Maximum Entropy Approach Leads to Wiener Measure" -- An Argument in Favor of Piecewise-Constant Membership Functions" -- "Data Processing under Fuzzy Uncertainty: Towards More Accurate Algorithms" -- "Epistemic vs. Aleatory: Case of Interval Uncertainty" -- "Standard Interval Computation Algorithm Is Not Inclusion-Monotonic: Examples" -- "Monotonic Bit-Invariant Permutation-Invariant Metrics on the Set of All Infinite Binary Sequences" -- Computing the Range of a Function-of-Few-Linear-Combinations Under Linear Constraints: A Feasible Algorithm" -- "How to Select a Representative Sample for a Family of Functions?.

Kurzfassung: In the first approximation, decision making is nothing else but an optimization problem: We want to select the best alternative. This description, however, is not fully accurate: it implicitly assumes that we know the exact consequences of each decision, and that, once we have selected a decision, no constraints prevent us from implementing it. In reality, we usually know the consequences with some uncertainty, and there are also numerous constraints that needs to be taken into account. The presence of uncertainty and constraints makes decision making challenging. To resolve these challenges, we need to go beyond simple optimization, we also need to get a good understanding of how the corresponding systems and objects operate, a good understanding of why we observe what we observe – this will help us better predict what will be the consequences of different decisions. All these problems – in relation to different application areas – are the main focus of this book.

Kurzfassung: Baudelaire's Ideas of Vagueness and Uniqueness in Art: Algorithm-Based Explanations -- Selfish Gene Theory Explains Oedipus Complex -- How to Teach Advanced Highly Motivated Students: Teaching Strategy of Iosif Yakovlevich Verebeichik -- Why 70/100 Is Satisfactory? Why Five Letter Grades? Why Other Academic Conventions? -- Shall We Ignore All Intermediate Grades? -- Why ∞ is a Reasonable Symbol for Infinity -- What is 1/0 from the Practical Viewpoint: A Pedagogical Note -- Historical Diversity Through base-10 Representation of Mayan Maths.

Kurzfassung: This book shows, on numerous examples, how to make decisions in realistic situations when we have both uncertainty and constraints. In most these situations, the book's emphasis is on the why-question, i.e., on a theoretical explanation for empirical formulas and techniques. Such explanations are important: they help understand why these techniques work well in some cases and not so well in others, and thus, help practitioners decide whether a technique is appropriate for a given situation. Example of applications described in the book ranges from science (biosciences, geosciences, and physics) to electrical and civil engineering, education, psychology and decision making, and religion—and, of course, include computer science, AI (in particular, eXplainable AI), and machine learning. The book can be recommended to researchers and students in these application areas. Many of the examples use general techniques that can be used in other application areas as well, so it is also useful for practitioners and researchers in other areas who are looking for possible theoretical explanations of empirical formulas and techniques.

Kurzfassung: This book is of interest to practitioners, researchers and graduate students seeking to apply existing techniques, to learn about the state of the art, or to explore novel concepts, in the theory and application of fuzzy sets and logic. Human knowledge and judgement are essential in both designing technological systems and in evaluating their outcomes. However, humans think and communicate in imprecise concepts, not numbers. Fuzzy sets and logic are well-known, widely used approaches to bridging this gap, which have been studied for nearly 60 years. NAFIPS 2022 brought together researchers studying both the theoretical foundations of fuzzy logic and its application to real-world problems. Their work examined fuzzy solutions to problems as diverse as astronomy, chemical engineering, economics, energy engineering, health care, and transportation engineering. Many papers combined fuzzy logic with interval or probabilistic computing, neural networks, and genetic algorithms.

Kurzfassung: Question-Answering System over Knowledge Graphs using Analogical-Problem-Solving Approach -- Fuzzy Transform on Manifolds -- Fuzzy Transform on Manifolds -- Freedom of Will, Physics, and Human Intelligence: An Idea -- SDN delay prediction using machine learning algorithms -- Stochastic Simulations of Airborne Particles in a Fibre Matrix -- Similar Vietnamese Document Detection in Online Assignment Submission System -- A Study of Causal Modeling with Time Delay for Frost Forecast Using Machine Learning from Data.

Kurzfassung: This book describes current and potential use of artificial intelligence and computational intelligence techniques in biomedicine and other application areas. Medical applications range from general diagnostics to processing of X-ray images to e-medicine-related privacy issues. Medical community understandably prefers methods that have been successful other on other application areas, where possible mistakes are not that critical. This book describes many promising methods related to deep learning, fuzzy techniques, knowledge graphs, and quantum computing. It also describes the results of testing these new methods in communication networks, education, environmental studies, food industry, retail industry, transportation engineering, and many other areas. This book helps practitioners and researchers to learn more about computational intelligence methods and their biomedical applications—and to further develop this important research direction.

Kurzfassung: Chapter 1: Semantic Modeling of Predicate Calculus Based on N-Tuple Algebra -- Chapter 2: Method of Iterative-Order Optimization of Multicriteria Problems Using the Local Importance of Criteria -- Chapter 3: Neural Network Representation for Ordinary Differential Equations -- Chapter 4: Modeling Situations in Spatial Analysis.

Kurzfassung: This book is based on the accepted research papers presented in the International Conference "Artificial Intelligence in Engineering & Science" (AIES-2022). The aim of the AIES Conference is to bring together researchers involved in the theory of computational intelligence, knowledge engineering, fuzzy systems, soft computing, machine learning and related areas and applications in engineering, bioinformatics, industry, medicine, energy, smart city, social spheres and other areas. This book presents new perspective research results: models, methods, algorithms and applications in the field of Artificial Intelligence (AI). Particular emphasis is given to the medical applications - medical images recognition, development of the expert systems which could be interesting for the AI researchers as well for the physicians looking for the new ideas in medicine. The central audience of the book are researchers, industrial practitioners, students specialized in the Artificial Intelligence.

Kurzfassung: Motivation and Outline -- A General Description of Measuring Devices: Plan -- A General Description of Measuring Devices: First Step – Finite Set of Possible Outcomes -- A General Description of Measuring Devices: Second Step – Pairs of Compatible Outcomes -- A General Description of Measuring Devices: Third Step – Subsets of Compatible Outcomes -- A General Description of Measuring Devices: Fourth Step – Conditional Statements about Possible Outcomes -- A General Description of Measuring Devices: Fifth Step – Disjunctive Conditional Statements about the Possible Outcomes -- A General Description of Measuring Devices: Summary -- Physical Quantities: A General Description -- Properties of Physical Quantities -- Future Work.

Kurzfassung: This book is about methodological aspects of uncertainty propagation in data processing. Uncertainty propagation is an important problem: while computer algorithms efficiently process data related to many aspects of their lives, most of these algorithms implicitly assume that the numbers they process are exact. In reality, these numbers come from measurements, and measurements are never 100% exact. Because of this, it makes no sense to translate 61 kg into pounds and get the result—as computers do—with 13 digit accuracy. In many cases—e.g., in celestial mechanics—the state of a system can be described by a few numbers: the values of the corresponding physical quantities. In such cases, for each of these quantities, we know (at least) the upper bound on the measurement error. This bound is either provided by the manufacturer of the measuring instrument—or is estimated by the user who calibrates this instrument. However, in many other cases, the description of the system is more complex than a few numbers: we need a function to describe a physical field (e.g., electromagnetic field); we need a vector in Hilbert space to describe a quantum state; we need a pseudo-Riemannian space to describe the physical space-time, etc. To describe and process uncertainty in all such cases, this book proposes a general methodology—a methodology that includes intervals as a particular case. The book is recommended to students and researchers interested in challenging aspects of uncertainty analysis and to practitioners who need to handle uncertainty in such unusual situations.

Kurzfassung: Introduction -- (Rational) Individual Decision Making: Main Ideas -- (Rational) Group Decision Making: General Formulas and a New Simplified Derivation of These Formulas -- How We Can Control Group Decision Making By Modifying the Proposed Options -- The Fact That We Can Only Have Approximate Estimates Explains Why Buying and Selling Prices Are Different -- The “No Trade Theorem” Paradox -- People Make Decisions Based on Clusters Containing Actual Values -- When Revolutions Succeed -- How People Combine Utility Values -- Biased Perception of Time -- Few-Parametric Spatial Models and How They Explain Bhutan Landscape Anomaly -- Dynamic Triggering of Earthquakes -- Applications to Computing: Representing Functions in Quantum -- and Reversible Computing.

Kurzfassung: This book describes new techniques for making decisions in situations with uncertainty and new applications of decision-making techniques. The main emphasis is on situations when it is difficult to decrease uncertainty. For example, it is very difficult to accurately predict human economic behavior, so in economics, it is very important to take this uncertainty into account when making decisions. Other areas where it is difficult to decrease uncertainty are geosciences and teaching. The book analyzes the general problem of decision making and shows how its results can be applied to economics, geosciences, and teaching. Since all these applications involve computing, the book also shows how these results can be applied to computing, including deep learning and quantum computing. The book is recommended to researchers, practitioners, and students who want to learn more about decision making under uncertainty—and who want to work on remaining challenges.

Kurzfassung: Method for the Computation of the Interval Hull to Solutions of Interval and Fuzzy Interval Linear Systems -- Fuzzy-Petri-Networks in Supervisory Control of Markov Processes in Robotized FMS and Robotic Systems -- Using Fuzzy Set Approaches for Linguistic Data Summaries -- Fuzzy or Neural, Type-1 or Type-2 – When Each Is Better: First-Approximation Analysis.

Kurzfassung: This book presents an authoritative collection of contributions reporting on computational intelligence, fuzzy systems as well as artificial intelligence techniques for modeling, optimization, control and decision-making together with applications and case studies in engineering, management and economic sciences. Dedicated to the Academician of the Polish Academy of Sciences, Professor Janusz Kacprzyk in recognition of his pioneering work, the book reports on theories, methods and new challenges in artificial intelligence, thus offering not only a timely reference guide but also a source of new ideas and inspirations for graduate students and researchers alike. The book consists of the 18 chapters, presented by distinguished and experienced authors from 16 different countries (Australia, Brazil, Canada, Chile, Germany, Hungary, Israel, Italy, China, R.N.Macedonia, Saudi Arabia, Spain, Turkey, United States, Ukraine, and Vietnam). All chapters are grouped into three parts: Computational Intelligence and Fuzzy Systems, Artificial Intelligence Techniques in Modelling and Optimization, and Computational Intelligence in Control and Decision Support Processes. The book reflects recent developments and new directions in artificial intelligence, including computation method of the interval hull to solutions of interval and fuzzy interval linear systems, fuzzy-Petri-networks in supervisory control of Markov processes in robotic systems, fuzzy approaches for linguistic data summaries, first-approximation analysis for choosing fuzzy or neural systems and type-1 or type-2 fuzzy sets, matrix resolving functions in game dynamic problems, evolving stacking neuro-fuzzy probabilistic networks and their combined learning in online pattern recognition tasks, structural optimization of fuzzy control and decision-making systems, neural and granular fuzzy adaptive modeling, state and action abstraction for search and reinforcement learning algorithms. Among the most successful and perspective implementations in practical areas of human activity are tentative algorithms for neurological disorders, human-centric question-answering system, OWA operators in pensions, evaluation of the perception of public safety through fuzzy and multi-criteria approach, a multicriteria hierarchical approach to investment location choice, intelligent traffic signal control and generative adversarial networks in cybersecurity.

Kurzfassung: This book focuses on the use of artificial intelligence (AI) and computational intelligence (CI) in medical and related applications. Applications include all aspects of medicine: from diagnostics (including analysis of medical images and medical data) to therapeutics (including drug design and radiotherapy) to epidemic- and pandemic-related public health policies. Corresponding techniques include machine learning (especially deep learning), techniques for processing expert knowledge (e.g., fuzzy techniques), and advanced techniques of applied mathematics (such as innovative probabilistic and graph-based techniques). The book also shows that these techniques can be used in many other applications areas, such as finance, transportation, physics. This book helps practitioners and researchers to learn more about AI and CI methods and their biomedical (and related) applications—and to further develop this important research direction.

Kurzfassung: Powerset operators in categories with fuzzy relations dened by monads -- Improved Fuzzy Q-Learning with Replay Memory -- The ulem package: underlining for emphasis -- A Dynamic Hierarchical Genetic-Fuzzy Sugeno Network -- Fuzzy Mathematical Morphology and Applications in Image Processing.

Kurzfassung: This book describes how to use expert knowledge—which is often formulated by using imprecise (fuzzy) words from a natural language. In the 1960s, Zadeh designed special "fuzzy" techniques for such use. In the 1980s, fuzzy techniques started controlling trains, elevators, video cameras, rice cookers, car transmissions, etc. Now, combining fuzzy with neural, genetic, and other intelligent methods leads to new state-of-the-art results: in aerospace industry (from drones to space flights), in mobile robotics, in finances (predicting the value of crypto-currencies), and even in law enforcement (detecting counterfeit banknotes, detecting online child predators and in creating explainable AI systems). The book describes these (and other) applications—as well as foundations and logistics of fuzzy techniques. This book can be recommended to specialists—both in fuzzy and in various application areas—who will learn latest techniques and their applications, and to students interested in innovative ideas.

Kurzfassung: Part I. Theoretical Results -- Why Quantiles Are a Good Description of Volatility in Economics: A Pedagogical Explanation -- An Introduction to Stacking Regression for Economists -- Economics of Reciprocity and Temptation -- The Most Infamous Coronavirus Forecast -- How to Efficiently Store Intermediate Results in Quantum Computing: Theoretical Explanation of the Current Algorithm -- Decompositions in quantum mechanics --- an overview -- A First Look at Quantum Conditional Events for Economics -- Quantum-like Modeling: Projection Postulate and Quantum Nonlocality -- New paradigm of economic thinking under uncertainty -- Reward for Good Performance Works Better Than Punishment for Mistakes: Economic Explanation -- The conjunction fallacy in quantum decision theory -- Predicting (Economic) Trends: Why Signature Method in Machine Learning -- Why Geometric Progression in Selecting the LASSO Parameter: A Theoretical Explanation -- How to Train A-to-B and B-to-A Neural Networks So That the Resulting Transformations Are (Almost) Exact Inverses -- Use Cases of Quantum Optimization for Finance -- Classical Optical Modelling of Social Sciences in a Bohr-Kantian Framework -- Classical Optical Modelling of the 'Prisoner's Dilemma' Game -- The probability of being better or worse off, and by how much, depending on experimental conditions with skew normal populations -- A Priori Procedure (APP) for Estimating the Scale Parameter in Gamma Populations for Known Shape -- Part II. Practical Applications -- Testing CAPM using Markov switching models: Application to ASEAN-6 stock markets -- A Bayesian Approach to Quantile Regression for Interval-Valued Data -- The Asymmetric Effect of Trade, Financial, and Political Globalization on Economic Development in ASEAN+3 -- Interdependence of Macroeconomic Factors and Economic Growth in OECD Countries: Evidence Based on a Bayesian Panel VAR Model -- Economic Policy Uncertainty and Stock-Bond Correlations: Evidence from the Thailand Market -- Revisiting the Determinants of Thai Economic Growth: A mixed frequency approach -- A New Approach For Estimating Probability Density Function With Fuzzy Data -- An Application of Quantum Optimization with Fuzzy Inference System for Stock Index Futures Forecasting -- A Generalize Maximum Renyi Entropy Approach in Kink Regression Model -- How Does Economic Policy Uncertainty Affect Stock Market Returns: Evidence from a Markov-Switching Model with Mixture Distribution -- Analyzing the Influence of Transportation and Macroeconomic Determinants on Chinese Inbound Tourism: a Markov Switching Model Using Ridge and Lasso -- The 〈Im|Possibility⟩ of Quantum Annealing for Maximum Likelihood Estimation -- Effects of Tourism Expenditure Increase in the Tourism Sector: a Computable General Equilibrium Model for Cambodia -- The Nexus between Regional Trade Integration and ASEAN Macroeconomic Indicators: Evidence from Panel ARDL approach -- Impacts of Climate Variability on Rice Production in Thailand -- Herding Behavior during the COVID-19 pandemic and the disposition effect situation in the Stock Exchange of Thailand -- An Analysis of Market Cycle for Thai Cassava Chips -- Maximal predictability portfolio optimization model and applications to Vietnam stock market -- Driving factors for realizing the fully smart transportation system: the case of individual-use autonomous vehicle in Thailand -- TOUS: A New Technique for Imbalanced Data Classification -- Value at Risk Analysis and Investment Portfolio Optimization of Asian Stocks -- Cash-flow volatility and capital structure decisions -- Consumer's Online Shopping in COVID-19 Pandemic: Evidence from Vietnam -- The Role of Bond Yield in Financial Asset Markets: Application of the Regression Kink Model -- Can Cryptocurrency Be A New Safe-Haven Asset? -- Modelling the Relationship Among Telecommunication Infrastructure, Foreign Direct Investment and Economic Growth in ASEAN Countries -- Industry Characteristics and Elder's Labor Demand in Thailand -- Nonlinear forecasting of exchange rate volatility using Google search -- Detection of buy and sell signals using technical indicators with a prediction model based on neural networks -- An analysis of the effects of tourism demand, yield curve, and stock returns on economic growth of Thailand: A comparison between the Bayesian DCC-GARCH and Bayesian change-point methods -- Relationship among International Trade, Financial Development, and Economic Growth: the Case of ASEAN.

Kurzfassung: This book describes state-of-the-art economic ideas and how these ideas can be (and are) used to make economic decision (in particular, to optimally allocate assets) and to gauge the results of different economic decisions (in particular, by using optimal transport methods). Special emphasis is paid to machine learning techniques (including deep learning) and to different aspects of quantum econometrics—when quantum physics and quantum computing models are techniques are applied to study economic phenomena. Applications range from more traditional economic areas to more non-traditional topics such as economic aspects of tourism, cryptocurrencies, telecommunication infrastructure, and pandemic. This book helps student to learn new techniques, practitioners to become better knowledgeable of the state-of-the-art econometric techniques, and researchers to further develop these important research directions .

Kurzfassung: Why Explainable AI? Why Fuzzy Explainable AI? What Is Fuzzy? -- Defuzzification -- Which Fuzzy Techniques? -- So How Can We Design Explainable Fuzzy AI: Ideas -- How to Make Machine Learning Itself More Explainable -- Final Self-Test.

Kurzfassung: Modern AI techniques –- especially deep learning –- provide, in many cases, very good recommendations: where a self-driving car should go, whether to give a company a loan, etc. The problem is that not all these recommendations are good -- and since deep learning provides no explanations, we cannot tell which recommendations are good. It is therefore desirable to provide natural-language explanation of the numerical AI recommendations. The need to connect natural language rules and numerical decisions is known since 1960s, when the need emerged to incorporate expert knowledge -- described by imprecise words like "small" -- into control and decision making. For this incorporation, a special "fuzzy" technique was invented, that led to many successful applications. This book described how this technique can help to make AI more explainable.The book can be recommended for students, researchers, and practitioners interested in explainable AI.

Kurzfassung: Prediction intervals for the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) via the LUBE method -- Analysis and Modeling of Information Security Information Security Systems in Industry 4.0 -- Using Non-linear Integral Models in Automatic Control and Measurement Systems for Sensors’ Input Signals’ Recovery -- Neural Network Method and Algorithm for Document Detection Based on Signaling Analysis -- Using fuzzy probabilistic implication in Z-set based inference -- Accounting experience between fuzzy integral and Z-numbers -- The Impact of In-Store Environment on Purchase Intention in Supermarkets -- A recurrent method for structural-parametric identification of fuzzy neural networks -- Voltage Control System for Electrical Networks Based on Fuzzy Sets -- Algorithms for the Synthesis of Optimal Linear-Quadratic Stationary Controllers.

Kurzfassung: This book provides the ultimate goal of economic studies to predict how the economy develops—and what will happen if we implement different policies. To be able to do that, we need to have a good understanding of what causes what in economics. Prediction and causality in economics are the main topics of this book's chapters; they use both more traditional and more innovative techniques—including quantum ideas -- to make predictions about the world economy (international trade, exchange rates), about a country's economy (gross domestic product, stock index, inflation rate), and about individual enterprises, banks, and micro-finance institutions: their future performance (including the risk of bankruptcy), their stock prices, and their liquidity. Several papers study how COVID-19 has influenced the world economy. This book helps practitioners and researchers to learn more about prediction and causality in economics -- and to further develop this important research direction.

Kurzfassung: Correcting Interval-Valued Expert Estimates: Empirical Formulas Explained -- On the Skill of Influential Predictions -- How to Find the Dependence Based on Measurements with Unknown Accuracy:Towards a Theoretical Justification for Midpoint and Convex-Combination Interval Techniques and Their Generalizations -- An Alternative Extragradient Method for a Vector Quasi-Equilibrium Problem to a Vector Generalized Nash Equilibrium Problem -- Introduction to Rare-Event Predictive Modeling for Inferential Statisticians--A Hands-On Application in the Prediction of Breakthrough Patents -- Logical aspects of quantum structures.

Kurzfassung: This book overviews latest ideas and developments in financial econometrics, with an emphasis on how to best use prior knowledge (e.g., Bayesian way) and how to best use successful data processing techniques from other application areas (e.g., from quantum physics). The book also covers applications to economy-related phenomena ranging from traditionally analyzed phenomena such as manufacturing, food industry, and taxes, to newer-to-analyze phenomena such as cryptocurrencies, influencer marketing, COVID-19 pandemic, financial fraud detection, corruption, and shadow economy. This book will inspire practitioners to learn how to apply state-of-the-art Bayesian, quantum, and related techniques to economic and financial problems and inspire researchers to further improve the existing techniques and come up with new techniques for studying economic and financial phenomena. The book will also be of interest to students interested in latest ideas and results.

Kurzfassung: This book focuses on an overview of the AI techniques, their foundations, their applications, and remaining challenges and open problems. Many artificial intelligence (AI) techniques do not explain their recommendations. Providing natural-language explanations for numerical AI recommendations is one of the main challenges of modern AI. To provide such explanations, a natural idea is to use techniques specifically designed to relate numerical recommendations and natural-language descriptions, namely fuzzy techniques. This book is of interest to practitioners who want to use fuzzy techniques to make AI applications explainable, to researchers who may want to extend the ideas from these papers to new application areas, and to graduate students who are interested in the state-of-the-art of fuzzy techniques and of explainable AI—in short, to anyone who is interested in problems involving fuzziness and AI in general. .