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• Part I. Introduction;
• Chapter 1. Semantic Web; 1.1 Description Logics and Semantic Web; 1.2 The Current Vision of the Semantic Web;
• Chapter 2. Probability; 2.1 Probabilistic Inference; 2.2 Probabilistic Learning;
• Chapter 3. Aims of the Thesis;
• Chapter 4. Structure of the Thesis; Part II. Description Logics;
• Chapter 5. Foundations of Description Logics;
• Chapter 6. Description Logics' Characteristics; 6.1 Concept and Role Constructors; 6.2 Family of DLs; 6.3 Knowledge Base; 6.3.1 TBox; 6.3.2 RBox; 6.3.3 ABox; 6.4 Semantics.

• Chapter 7. Significant Examples of Description Logics;
• Chapter 8. OWL: the Web Ontology Language;
• Chapter 9. Inference in Description Logics; 9.1 Approaches to Compute Explanations; 9.1.1 Solving min-a-enum: The Standard Definition; 9.1.2 Resolving min-a-enum: Pinpointing Formula; Part III. A Probabilistic Semantics for Description Logics;
• Chapter 10. Distribution Semantics; 10.1 Formal Definition; 10.2 PLP Languages under the Distribution Semantics; 10.2.1 Logic Programming; 10.2.2 LPAD; 10.2.3 ProbLog; 10.3 Inference in Probabilistic Logic Programming; 10.3.1 ProbLog Inference System.

• 10.3.2 PITA; 10.4 Learning in Probabilistic Logic Programming;
• Chapter 11. DISPONTE;
• Chapter 12. Probabilistic Description Logics; Part IV. Inference in Probabilistic DLs;
• Chapter 13. Inference; 13.1 Splitting Algorithm; 13.2 Binary Decision Diagrams;
• Chapter 14. BUNDLE;
• Chapter 15. TRILL; 15.1 TRILL on SWISH;
• Chapter 16. TRILL P;
• Chapter 17. Complexity of Inference;
• Chapter 18. Related Inference Systems;
• Chapter 19. Experiments; 19.1 BUNDLE: Comparison with PRONTO; 19.2 BUNDLE: Not Entailed Queries; 19.3 BUNDLE: Inference with Limited Number of Explanations; 19.4 BUNDLE: Scalability.

• 19.5 TRILL, TRILL P & BUNDLE: Comparing Different Approaches; 19.6 Discussion; Part V. Learning in Probabilistic DLs;
• Chapter 20. Learning;
• Chapter 21. EDGE: Parameter Learning; 21.1 Expectation Maximization Algorithm; 21.2 EDGE;
• Chapter 22. LEAP: Structure Learning; 22.1 CELOE; 22.2 LEAP;
• Chapter 23. Distributed Learning; 23.1 Map Reduce Approach; 23.2 The Message Passing Interface Standard; 23.3 EDGE MR; 23.4 LEAP MR;
• Chapter 24. Related Learning Systems;
• Chapter 25. Experiments; 25.1 EDGE: Comparison with Association Rules; 25.2 LEAP & EDGE: a Comparison Between Different Learning Problems.

• 25.3 EDGE MR: Parallelization Speedup; 25.4 EDGE MR: Memory Consumption; 25.5 LEAP MR: Parallelization Speedup; 25.6 Discussion; Part VI. Summary and Future Work;
• Chapter 26. Conclusion;
• Chapter 27. Future Work.

• Machine generated contents note: Survey on Applying Machine Learning Techniques for Behavioral Awareness / Wouter Joosen
• Modelling Spatial and Temporal Context to Support Activity Recognition / Stephen Marsland
• Affect Aware Ambient Intelligence: Current and Future Directions / Anne James
• Behavioral Biometrics and Ambient Intelligence: New Opportunities for Context-Aware Applications / Paulo Novais
• Energy and Environmental Long-Term Monitoring System for Inhabitants' Weil-Being / Filippo Palumbo
• Behavioural Patterns from Cellular Data Streams and Outdoor Lighting as Strong Allies for Smart Urban Ecosystems / Adam Sedziwy
• Learning Daily Routines in Smart Office Environments / Stefano Ferilli
• EKRUCAmI Architecture -- Applications in Healthcare Domain / Hyun Yoe
• Qualitative Image Descriptor QIDL+N to Obtain Logics and Narratives Applied to Ambient Intelligent Systems / Zoe Falomir.

Annotation We are moving towards a future where environments respond to human preferences and needs. In this world, smart devices equipped with intelligent features and the capability to sense, communicate with and support humans in daily activities will be unremarkable. We already expect our cars to warn us of hazards, track our location and provide timely route advice, and in future we will speak to simple machines and hold conversations with more complex systems, such as intelligent homes, which will help us to monitor conditions, track routine tasks, and program the heating, lighting, garden watering and entertainment centre. But questions have been raised in recent years as to how intelligent these so called smart systems or ambient intelligence environments really are. This book, State of the Art in AI Applied to Ambient Intelligence, part of the outcome of the Workshop on Artificial Intelligence Techniques for Ambient Intelligence (AITAmI) which has now run for 10 consecutive editions, aims to provide a clear picture of what has been achieved after a decade of discussion. It is representative of the diversity of approaches and issues which are currently being considered, and also indicates those avenues which are the most promising for exploration in the next decade. The book provides all those working in the field with an up-to-date reference where they will find inspiration to create better systems for the society of tomorrow.

• Part 1: Historical Review of Professor Zdzislaw Pawlak and Rough Set.- The Born and Growing of Chinese Rough Set Community with Help of Professor Zdzislaw Pawlak.- Zdzislaw Pawlak as I Saw Him and Remember Him Now.- Recent Development of Rough Computing: A Scientometrics View.-
• Part 2: Review of Rough Set Research.- Rough Sets, Rough Mereology and Uncertainty.- Rough Sets in Machine Learning: A Review.- Application of Tolerance Rough Sets in Structured and Unstructured Text Categorization: A Survey.- Medical Diagnosis: Rough Set View.- Rough Set Analysis of Imprecise Classes.- Pawlak's Many Valued Information System, Non-deterministic Information System, and a Proposal of New Topics on Information Incompleteness Toward the Actual Application.-
• Part 3: Rough Set Theory.- From Information Systems to Interactive Information Systems.- Back to the Beginnings: Pawlak's Definitions of the Terms Information System and Rough Set.- Knowledge and Consequence in AC Semantics for General Rough Sets.- Measuring Soft Roughness of Soft Rough Sets Induced by Covering.- Rough search of vague knowledge.- Vagueness and Uncertainty: An F-Rough Set Perspective.- Directions of Use of the Pawlak's Approach to Conflict Analysis.- Lattice Structure of Variable Precision Rough Sets.-
• Part 4: Rough Set Based Data Mining.- Mining for Actionable Knowledge in Tinnitus Datasets.- Rough-Granular Computing for Relational Data.- The Boosting and Bootstrap Ensembles for the Pair Classifier Based on the Dual Indiscernibility Matrix.
• (source: Nielsen Book Data)

This special book is dedicated to the memory of Professor Zdzislaw Pawlak, the father of rough set theory, in order to commemorate both the 10th anniversary of his passing and 35 years of rough set theory. The book consists of 20 chapters distributed into four sections, which focus in turn on a historical review of Professor Zdzislaw Pawlak and rough set theory; a review of the theory of rough sets; the state of the art of rough set theory; and major developments in rough set based data mining approaches. Apart from Professor Pawlak's contributions to rough set theory, other areas he was interested in are also included. Moreover, recent theoretical studies and advances in applications are also presented. The book will offer a useful guide for researchers in Knowledge Engineering and Data Mining by suggesting new approaches to solving the problems they encounter.
(source: Nielsen Book Data)

• Introduction
• A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-spider.-A States of Matter Algorithm for Global Optimization
• An Algorithm for Global Optimization Inspired by Collective Animal Behavior
• A Bio-inspired Evolutionary Algorithm: Allostatic Optimization
• Optimization Based on the Behavior of Locust Swarms.

The goal of this book is to present advances that discuss alternative Evolutionary Computation (EC) developments and non-conventional operators which have proved to be effective in the solution of several complex problems. The book has been structured so that each chapter can be read independently from the others. The book contains nine chapters with the following themes: 1) Introduction, 2) the Social Spider Optimization (SSO), 3) the States of Matter Search (SMS), 4) the collective animal behavior (CAB) algorithm, 5) the Allostatic Optimization (AO) method, 6) the Locust Search (LS) algorithm, 7) the Adaptive Population with Reduced Evaluations (APRE) method, 8) the multimodal CAB, 9) the constrained SSO method.

• Introduction and Related Work
• Optimal State Change Attempts
• Different Kinds of Effect Estimators
• A Comparison with Planning under Uncertainty
• Experimental Evaluation
• Conclusions.

This Springer Brief presents a basic algorithm that provides a correct solution to finding an optimal state change attempt, as well as an enhanced algorithm that is built on top of the well-known trie data structure. It explores correctness and algorithmic complexity results for both algorithms and experiments comparing their performance on both real-world and synthetic data. Topics addressed include optimal state change attempts, state change effectiveness, different kind of effect estimators, planning under uncertainty and experimental evaluation. These topics will help researchers analyze tabular data, even if the data contains states (of the world) and events (taken by an agent) whose effects are not well understood. Event DBs are omnipresent in the social sciences and may include diverse scenarios from political events and the state of a country to education-related actions and their effects on a school system. With a wide range of applications in computer science and the social sciences, the information in this Springer Brief is valuable for professionals and researchers dealing with tabular data, artificial intelligence and data mining. The applications are also useful for advanced-level students of computer science.

• Title Page
• Foreword
• Contents
• List of Contributors
• Part I. Software Engineering Meets Semantic Web: Concepts and Theories
• Motivation and Introduction
• Close Encounters of the Semantic Web and MDA Kinds
• Generating Model Transformations Using Ontology Engineering Space
• Part II. Realize Software Engineering by Semantic Web Technologies
• Towards a Consistent Feature Model using OWL
• Using Semantic Web Technologies for Management Application Integration
• Semantic Web Enabled Software Analysis
• Semantically Enabling Web Service Repositories

• ABC: A methodology for Semantic Web Application DevelopmentModel-driven Design Frameworks for Semantic Web Applications
• Part III. Design Ontologies for Software Engineering
• A Software Process Ontology and Its Application
• Enriching SE Ontologies with Bug Quality
• Learning Ontologies from Software Artifacts: Exploring and Combining Multiple Choices
• References

Over the last decade, ontology has become an important modeling component in software engineering. Semantic Web Enabled Software Engineering presents some critical findings on opening a new direction of the research of Software Engineering, by exploiting Semantic Web technologies. Most of these findings are from selected papers from the Semantic Web Enabled Software Engineering (SWESE) series of workshops starting from 2005. Edited by two leading researchers, this advanced text presents a unifying and contemporary perspective on the field. The book integrates in one volume a unified perspectiv.

• Making the Computer 'Brained'.- The Biology of Brain:An insight into the Human Brain.- Evolutionary Computation.- Artificial Neural Network (ANNs).- Structure and Operation of Cartesian Genetic Programming Developmental Network (CGPDN) Model.- Wumpus World.- Checkers.- Concluding Remarks and Future Directions.
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This book presents recent research on the evolution of artificial neural development, and searches for learning genes. It is fascinating to see how all biological cells share virtually the same traits, but humans have a decided edge over other species when it comes to intelligence. Although DNA decides the form each particular species takes, does it also account for intelligent behaviour in living beings? The authors explore the factors that are perceived as intelligent behaviour in living beings and the incorporation of these factors in machines using genetic programming, which ultimately provides a platform for exploring the possibility of machines that can learn by themselves, i.e. that can "learn how to learn". The book will be of interest not only to the specialized scientific community pursuing machine intelligence, but also general readers who would like to know more about the incorporation of intelligent behaviour in machines, inspired by the human brain.
(source: Nielsen Book Data)

• RFID-based Multilevel Sensing Network for Industrial Internet of Things.- Convey Intelligence to Edge Aggregation Analytics.- Publish-Subscribe Based Monitoring Model For Wireless Sensor Networks.- Towards Data governance in the Internet of Things.- Advancing Cognitive Cities with the Web of Things.- Transferring Wireless High Update Rate Supermedia Streams Over IoT.- Smart Connectivity for Internet of Things (IoT) Applications.- A Blockchain-based Storage System for Data Analytics in the Internet of Things.- Ensuring Quality of Service in the Internet of Things.- Wireless Sensor Network-IoT Infrastructure.
• (source: Nielsen Book Data)

This book presents a selection of highly relevant works concerning the Internet of Things, including: IoT Architectures, Standardization, Smart Cities, Smart Health, the communication of Smart Things, and outstanding IoT use cases. Some of these works present important future lines for the evolution of IoT systems. Thanks to its unique structure, the book allows authors to describe their works at an unprecedented level of detail. The special chapters contain extended versions of prominent and/or high-impact IoT projects that have laid the foundations of many subsequent IoT systems due to their level of innovation, and reflect highly relevant and innovative trends in the development of a new generation of IoT Systems.
(source: Nielsen Book Data)

• Learning automata theory.- Cellular learning automata.- Learning automata for wireless sensor networks.- Learning automata for cognitive Peer-to-peer networks.- Learning automata for Complex Social Networks.- Adaptive petri net based on learning automata.- Summary and future directions.
• (source: Nielsen Book Data)

This book collects recent theoretical advances and concrete applications of learning automata (LAs) in various areas of computer science, presenting a broad treatment of the computer science field in a survey style. Learning automata (LAs) have proven to be effective decision-making agents, especially within unknown stochastic environments. The book starts with a brief explanation of LAs and their baseline variations. It subsequently introduces readers to a number of recently developed, complex structures used to supplement LAs, and describes their steady-state behaviors. These complex structures have been developed because, by design, LAs are simple units used to perform simple tasks; their full potential can only be tapped when several interconnected LAs cooperate to produce a group synergy. In turn, the next part of the book highlights a range of LA-based applications in diverse computer science domains, from wireless sensor networks, to peer-to-peer networks, to complex social networks, and finally to Petri nets. The book accompanies the reader on a comprehensive journey, starting from basic concepts, continuing to recent theoretical findings, and ending in the applications of LAs in problems from numerous research domains. As such, the book offers a valuable resource for all computer engineers, scientists, and students, especially those whose work involves the reinforcement learning and artificial intelligence domains.
(source: Nielsen Book Data)

• Part I: From Machine Learning to Data Mining.- Analyzing Accident Prone Regions by Clustering.- Analyzing Life Insurance Data with Different Classification Techniques for Customers' Behavior Analysis.- Classification of Product Rating Using Data Mining Techniques.- MASS: a Semi-Supervised Multi-Label Classification Algorithm with Specific Feature.- Parallel Self-organizing Map Using Shared Virtual Memory Buffers.- Parametric Optimization of the Selected Classifiers in Binary Classification.- Prediction of Academic Performance During Adolescence Based on Socioeconomic, Psychological and Academic Factors.- A Super-Vector Deep Learning Coprocessor with High Performance-Power Ratio.- Part II: Big Data and Collaborative Decision Support Systems.- Bi-temporal Database Model for Legal Merger Transactions and Late-arriving Information Problem: the Case of Polish Merger Market.- Breathing Movement Analysis for Adjustment of Radiotherapy Planning.- Measuring Improvement in Access to Complete Data in Healthcare Collaborative Dabase Systems.- Rough Set Theory for Supporting Decision Making on Relevance in Browsing Multilingual Digital Resources.- The Social Influence on the Behavioral Intention to Use Mobile Electronic Medical Records.- Stock Prices Growth Pattern by the Emergency Demand After the Great East-Japan Earthquake.- Structure-Based Virtual Screening for Novel Modulators of Human Orexin 2 Receptor with Cloud Systems and Supercomputers.- Supply Chains of Cross-Border E-Commerce.- Towards Personalized Virtual Customer Experience.- Towards Big Management.- Part III: Computer Vision Analysis, Detection, Tracking and Recognition.- Combination of Collision Detection and Visibility Algorithms in Simulation of Effective Placement of Anti-Air Elements.- A Combination of Deep Learning and Hand-Designed Feature for Plant Identification based on Leaf and Flower Images.- An Efficient Defect Classification Algorithm for Ceramic Tiles.- Parking Assistant - Prediction of an Empty Parking Space in Time.- Recent Advances in the Field of Foreground Detection: An Overview.- Tracking of Bone Reparation Process with Using of Periosteal Callus Extraction based on Fuzzy C-Means Algorithm.- Part IV: Data-Intensive Text Processing.- Automatic Post-Editing of Kazakh Sentences Machine Translated from English.- Complex Technology of Machine Translation Resources Extension for the Kazakh Language.- Enhancing Latent Semantic Analysis by Embedding Tagging Algorithm in Retrieving Malay Text Documents.- Exploiting Distance Graph and Hidden Topic Models for Multi-label Text Classification.- Financial Reports and Financial News - An Information Content Gap Analysis.- The Great National Photocorpus of 20th-Century Vietnamese. Origins, assumptions and goals.- The Impact of User Sentiment Aroused by The-day-of-the-week on the Recommendation Effectiveness in Microblog.- Multi-Sentence Compression Using Word Graph and Integer Linear Programming.- A Performance Comparison of Feature Extraction Methods for Sentiment Analysis.- A Study of Plagiarism Checker System Based on Chinese Word Segmentation and SQL Intersection Operation Technique.- Text Summarization Based on Classification Using ANFIS.- Part V: Innovations in Web and Internet Technologies.- Analysis of Indoor Positioning Based on BLE.- A Fusion Technique of Schema and Syntax Rules for Validating Open Data.- Job Description Language for a Browser-Based Computing Platform - A Preliminary Report.- Measuring the Effectiveness of Knowledge Driven Web Applications.- Responsive Data Table Solution with New Scrolling Control Gesture for Better User Experience.- Part VI: New Methods and Applications in Software Engineering.- Adaptation of an ANN-Based Air Quality Forecasting Model to a New Application Area.- Checking Compliance of Program with SecureUML Model.- Generation of Test Data using Genetic Algorithm and Constraint Solver.- Investigating the Issues of using Agile Methods in Offshore Software Development in Sri Lanka.- A Method for Automated Test Cases Generation from UML Models with String ConstraintS.- Mobile Application for Calculation of Optimal Route Between Searched Points of Interest.- On Implementation of the Assumption Generation Method for Component-Based Software Verification.
• (source: Nielsen Book Data)

This book presents recent research in intelligent information and database systems. The carefully selected contributions were initially accepted for presentation as posters at the 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2017) held from to 5 April 2017 in Kanazawa, Japan. While the contributions are of an advanced scientific level, several are accessible for non-expert readers. The book brings together 47 chapters divided into six main parts: * Part I. From Machine Learning to Data Mining.* Part II. Big Data and Collaborative Decision Support Systems, * Part III. Computer Vision Analysis, Detection, Tracking and Recognition, * Part IV. Data-Intensive Text Processing, * Part V. Innovations in Web and Internet Technologies, and* Part VI. New Methods and Applications in Information and Software Engineering. The book is an excellent resource for researchers and those working in algorithmics, artificial and computational intelligence, collaborative systems, decision management and support systems, natural language processing, image and text processing, Internet technologies, and information and software engineering, as well as for students interested in such research areas.
(source: Nielsen Book Data)

• Data Mining and Machine Learning.- Planning and Combinatorial Optimization.- AI Applications.- Natural Language Processing.- Uncertainty and Preference Reasoning.- Agent Systems.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, held in Edmonton, AB, Canada, in May 2017. The 19 regular papers and 24 short papers presented together with 6 Graduate Student Symposium papers were carefully reviewed and selected from 62 submissions. The focus of the conference was on the following subjects: Data Mining and Machine Learning; Planning and Combinatorial Optimization; AI Applications; Natural Language Processing; Uncertainty and Preference Reasoning; and Agent Systems.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Intelligence computer data analysis techniques
• 3. The fundamentals of Cognitive Informatics
• 4. Cognitive information systems
• 5. The fundamentals of management sciences
• 6. Intelligent cognitive information systems in management application
• 7. Summary.
• (source: Nielsen Book Data)

Cognitive Information Systems in Management Sciences summarizes the body of work in this area, taking an analytical approach to interpreting the data, while also providing an approach that can be used for practical implementation in the fields of computing, economics, and engineering. Using numerous illustrative examples, and following both theoretical and practical results, Dr. Lidia Ogiela discusses the concepts and principles of cognitive information systems, the relationship between intelligent computer data analysis, and how to utilize computational intelligent approaches to enhance information retrieval. Real world implantation use cases round out the book, with valuable scenarios covering management science, computer science, and engineering. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS.
(source: Nielsen Book Data)

• Analysis and Control of a Novel 4-D Hyperchaotic System.- Analysis, Control and Synchronization of a Nine-Term Novel 3-D Chaotic System.- Backstepping Controller Design for the Global Chaos Synchronization of Sprott's Jerk Systems.- Multi-Scroll Chaotic Oscillator Based on a First-Order Delay Differential Equation.- Projective Synchronization Scheme Based on Fuzzy Controller for Uncertain Multivariable Chaotic Systems.- Deadbeat Control for for Multivariable Discrete Time Systems with Time Varying Delay.- Control of Smart Grid Residential Buildings with Demand Response.- Application of Some Modern Techniques in Load Frequency Control in Power Systems.- Investigating Metaheuristics Applications for Capacitated Location Allocation Problem in Logistics Networks.- Classification of Heart Disorders Based on Tunable-Q Wavelet Transform of Cardiac Sound Signals.- Reliability-Constrained Optimal Distribution System Reconfiguration.- Machine Learning aided Efficient Tools for Risk Evaluation and Operational Planning of Multiple Contingencies.- Goal Directed Synthesis of Serial Manipulators Based on Task Descriptions.- Intelligent Tracking Control System for Fast Image Scanning of Atomic Force Microscopes.- Fault Diagnosis Algorithms by Combining Structural Graphs and PCA Approaches for Chemical Processes.
• (source: Nielsen Book Data)

The development of computational intelligence (CI) systems was inspired by observable and imitable aspects of intelligent activity of human being and nature. The essence of the systems based on computational intelligence is to process and interpret data of various nature so that that CI is strictly connected with the increase of available data as well as capabilities of their processing, mutually supportive factors. Developed theories of computational intelligence were quickly applied in many fields of engineering, data analysis, forecasting, biomedicine and others. They are used in images and sounds processing and identifying, signals processing, multidimensional data visualization, steering of objects, analysis of lexicographic data, requesting systems in banking, diagnostic systems, expert systems and many other practical implementations. This book consists of 15 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought out in the broad areas of Control Systems, Power Electronics, Computer Science, Information Technology, modeling and engineering applications. Special importance was given to chapters offering practical solutions and novel methods for the recent research problems in the main areas of this book, viz. Control Systems, Modeling, Computer Science, IT and engineering applications. This book will serve as a reference book for graduate students and researchers with a basic knowledge of control theory, computer science and soft-computing techniques. The resulting design procedures are emphasized using Matlab/Simulink software.
(source: Nielsen Book Data)

• Consequence
• Logics
• Classical Belief Revision
• AGM Contraction in Non-Classical Logics
• AGM Revision in Logics Without Negation
• Base Revision in Logics Without Negation
• Algorithms for Belief Bases
• Conclusion.

Since the advent of the Semantic Web, interest in the dynamics of ontologies (ontology evolution) has grown significantly. Belief revision presents a good theoretical framework for dealing with this problem; however, classical belief revision is not well suited for logics such as Description Logics. Belief Revision in Non-Classical Logics presents a framework which can be applied to a wide class of logics that include - besides most Description Logics such as the ones behind OWL - Horn Logic and Intuitionistic logic, amongst others. The author also presents algorithms for the most important constructions in belief bases. Researchers and practitioners in theoretical computing will find this an invaluable resource.
(source: Nielsen Book Data)

• Swarm and Evolutionary Computation.- Particle Swarm Optimization.- Artificial Bee Colony Algorithm Variants and Its Application to Colormap Quantization.- Spider Monkey Optimization Algorithm.- Genetic Algorithm and Its Advances in Embracing Memetics.- Constrained Multi-Objective Evolutionary Algorithm.- Genetic Programming for Classification and Feature Selection.- Genetic Programming for Job Shop Scheduling.- Evolutionary Fuzzy Systems: A Case Study for Intrusion Detection Systems.
• (source: Nielsen Book Data)

This book is a delight for academics, researchers and professionals working in evolutionary and swarm computing, computational intelligence, machine learning and engineering design, as well as search and optimization in general. It provides an introduction to the design and development of a number of popular and recent swarm and evolutionary algorithms with a focus on their applications in engineering problems in diverse domains. The topics discussed include particle swarm optimization, the artificial bee colony algorithm, Spider Monkey optimization algorithm, genetic algorithms, constrained multi-objective evolutionary algorithms, genetic programming, and evolutionary fuzzy systems. A friendly and informative treatment of the topics makes this book an ideal reference for beginners and those with experience alike.
(source: Nielsen Book Data)

• Intercriteria Analysis and Arithmetic Functions.- Fuzzy Harmony Search Algorithm Using an Interval type- 2 Fuzzy Logic Applied to Benchmark Mathematical Functions.- Mixture Initialization Based on Prior Data Visual Analysis.- Spatiotemporal Parameter Estimation of Thermal Treatment Process via Initial Condition Reconstruction Using Neural Networks.- Interval type-2 Fuzzy Logic Dynamic Mutation and Cross-over Parameter Adaptation in a Fuzzy Differential Evolution Method.- Intuitionistic Fuzzy Evaluations for the Analysis of a Student's Knowledge in University e-Learning Courses.- S-logic with first and second imaginary states.- Generalized Net Model of the Processes in a Center of Transfusion Haematology.- Image to Sound Encryption Using a Self-Organizing Map Neural Network.- On Different Algorithms for InterCriteria Relations Calculation.- Defining Consonance Thresholds in InterCriteria Analysis: An Overview.- Design and Comparison of ECG Arrhythmias Classifiers Using Discrete Wavelet Transform, Neural Network and Principal Component Analysis.
• (source: Nielsen Book Data)

This book gathers extended versions of the best papers presented at the 8th IEEE conference on Intelligent Systems, held in Sofia, Bulgaria on September 4-6, 2016, which are mainly related to theoretical research in the area of intelligent systems. The main focus is on novel developments in fuzzy and intuitionistic fuzzy sets, the mathematical modelling tool of generalized nets and the newly defined method of intercriteria analysis. The papers reflect a broad and diverse team of authors, including many young researchers from Australia, Bulgaria, China, the Czech Republic, Iran, Mexico, Poland, Portugal, Slovakia, South Korea and the UK.
(source: Nielsen Book Data)

• PART 1, Data mining, machine learning, knowledge discovery, Tensor Networks for Dimensionality Reduction, Big Data and Deep Learning.- Local Data Characteristics in Learning Classifiers from Imbalanced Data.- Similarity dimensions of semantic ontologies.- Some interesting phenomenon occurring during self-learning process with its psychological interpretation.- PART 2, Neural networks and connectionist systems, On the interpretation and characterization of echo state networks dynamics: A complex systems perspective.- Optimization of Ensemble Neural Networks with Type-1 and Interval Type-2 Fuzzy Integration for Forecasting the Taiwan Stock Exchange.- Deep Neural Networks - A Brief History.- PART 3, Intelligent technologies in systems modeling, Techniques for Construction and Integration of Rule Bases.- New Aspects of Interpretability of Fuzzy Systems for Nonlinear Modeling.- On the intuitionistic fuzzy sets of n-th type.-
• PART 4. Intelligent technologies in decision making, optimization and control, MCTS/UCT in solving real-life problems.- Interactive cone contraction for evolutionary mutliple objective optimization.- A review of fuzzy and mathematic methods for dynamic parameter adaptation in the firefly algorithm.-
• PART 5. Applications of intelligent technologies Computational Intelligence Methods in Personalized Pharmacotherapy.- Embodying Intelligence in Autonomous and Robotic Systems with the Use of Cognitive Psychology and Motivation Theories.- Evolutionary Approach for Automatic Design of PID Controllers.- Fuzzy-genetic approach to identity verification using a handwritten signature.- A method of design and optimization for SiC-based grid-connected AC-DC converters.
• (source: Nielsen Book Data)

This book is a tribute to Professor Jacek Zurada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek Zurada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, J.B. Speed School of Engineering, University of Louisville, Kentucky, USA, as a token of appreciation for his scientific and scholarly achievements, and for his longstanding service to many communities, notably the computational intelligence community, in particular neural networks, machine learning, data analyses and data mining, but also the fuzzy logic and evolutionary computation communities, to name but a few. At the same time, the book recognizes and honors Professor Zurada's dedication and service to many scientific, scholarly and professional societies, especially the IEEE (Institute of Electrical and Electronics Engineers), the world's largest professional technical professional organization dedicated to advancing science and technology in a broad spectrum of areas and fields. The volume is divided into five major parts, the first of which addresses theoretic, algorithmic and implementation problems related to the intelligent use of data in the sense of how to derive practically useful information and knowledge from data. In turn, Part 2 is devoted to various aspects of neural networks and connectionist systems. Part 3 deals with essential tools and techniques for intelligent technologies in systems modeling and Part 4 focuses on intelligent technologies in decision-making, optimization and control, while Part 5 explores the applications of intelligent technologies.
(source: Nielsen Book Data)

• Introduction.- The metaheuristic algorithm of the social-spider.- Calibration of Fractional Fuzzy Controllers by using the Social-spider method.- The metaheuristic algorithm of the Locust-search.- Identification of fractional chaotic systems by using the Locust Search Algorithm.- The States of Matter Search (SMS).- Multimodal States of Matter search.- Metaheuristic algorithms based on Fuzzy Logic.
• (source: Nielsen Book Data)

This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.
(source: Nielsen Book Data)

• Proposed Framework Application for a Quality Mobile Application Measurement and Evaluation.- Proposal and Development of Artificial Personality (AP) application using the "Requesting" Mechanism.- Load Experiment of the vDACS Scheme in case of the 300 Simultaneous Connection.- Hearing-Dog Robot to wake People up using its Bumping Action.- Implementation of Document Production Support System with Obsession Mechanism.- Detecting Outliners in Terms of Errors in Embedded Software Development Projects Using Imbalance Data Classification.- Development of Congestion State Guiding System for University Cafeteria.- Analog Learning Neural Circuit with Switched Capacitor and the Design of Deep Learning Model.- Study on Category Classification of Conversation Document in Psychological Counseling with Machine Learning.- Improvement of "Multiple Sightseeing Spot Scheduling System".- Advertising in the Webtoon of Cosmetics Brand -Focusing on 'tn' Youth Cosmetics Brands
• Testing Driven Development of Mobile Application using Automatic Bug Management Systems.- Shape Recovery of Polyp from Endoscope Image Using Blood Vessel Information.- Design of Agent Development Framework for RoboCupRescue Simulation.
• (source: Nielsen Book Data)

This book presents the scientific outcome of the 4th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2017), which was held on July 9-13, 2017 in Hamamatsu, Japan. The aim of this conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science, to share their experiences and to exchange new ideas and information in a meaningful way. The book includes research findings concerning all aspects (theory, applications and tools) of computer and information science, and discusses the practical challenges encountered and the solutions adopted to address them. The book features 16 of the conference's most promising papers, written by researchers who are expected to make significant contributions in the field of computer and information science.
(source: Nielsen Book Data)

• Testing, Prediction, and Cause in Econometric Models
• Information Criteria for Statistical Modeling in Data-Rich Era
• An invitation to quantum econometrics
• GL+ and GL- Regressions
• What If We Do Not Know Correlations?
• Markowitz Portfolio Theory Helps Decrease Medicines' Side Effect and Speed Up Machine Learning.

This book addresses both theoretical developments in and practical applications of econometric techniques to finance-related problems. It includes selected edited outcomes of the International Econometric Conference of Vietnam (ECONVN2018), held at Banking University, Ho Chi Minh City, Vietnam on January 15-16, 2018. Econometrics is a branch of economics that uses mathematical (especially statistical) methods to analyze economic systems, to forecast economic and financial dynamics, and to develop strategies for achieving desirable economic performance. An extremely important part of economics is finances: a financial crisis can bring the whole economy to a standstill and, vice versa, a smart financial policy can dramatically boost economic development. It is therefore crucial to be able to apply mathematical techniques of econometrics to financial problems. Such applications are a growing field, with many interesting results? and an even larger number of challenges and open problems.