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• Computer Input by Human Eyes Only and it's Applications
• New Ideas for Brain Modelling 2
• Neural-like Growing Networks the Artificial Intelligence Basic Structure
• Detection of Privilege Abuse in RBACAdministered Database
• Learning-based Leaf Image Recognition Frameworks
• Massively Parallel Feature Selection based on Ensemble of Filters and Multiple Robust Consensus Functions for Cancer Gene Identification
• Relationship Discovery and Navigation in Big Graphs
• A Fuzzy System for Three-Factor, Non-textual Authentication
• Efficient Graph-Based Volumetric Segmentation
• A Hybrid Intelligent System in Cultural Intelligence
• Semantic-Based Recommender System with Humain Feeling Relevance Measure
• Alignment of Time Series for Subsequence-to-Subsequence Time Series Matching
• The Effects of Typing Demand on EmotionalStress, Mouse and Keystroke Behaviours
• Friend Recommendation in a Social Bookmarking System: Design and Architecture Guidelines
• Quantum Behaved Genetic Algorithm: Constraints-Handling and GPU Computing
• A Genetic Algorithm Approach for Optimizing a Single-Finger Arabic Keyboard Layout
• Dynamic Well Bottom-Hole Flowing Pressure Prediction Based on Radial Basis Neural Network
• Modeling Energy Consumption in A Educational Building: Comparative Study between Linear Regression, Fuzzy Set Theory and Neural Networks
• Delivering Faster Results Through Parallelisation and GPU Acceleration
• Probabilistic Roadmaps and Hierarchical Genetic Algorithms for Optimal Motion Planning
• Using Mouse and Keyboard Dynamics to Detect Cognitive Stress during Mental Arithmetic
• Towards Using Games Theory to Detect New U2R Attacks
• Development and Evaluation of Virtual Reality Medical Training System for Anatomy Education
• Compression of ECG signal using Hybrid technique
• Performance Analysis of MATLAB Parallel Computing Approaches to Implement Genetic Algorithm for Image Compression.-

The book Intelligent Systems in Science and Information 2014 is ℗ℓthe carefully edited collection of 25 extended chapters from selected papers in the field of Computational Intelligence that, which received highly recommended feedback during the Science and Information Conference (SAI) 2014 review process. All chapters have gone through substantial extension and consolidation and were subject to another round of rigorous review and additional modification and represent the state of the art of the cutting-edge research and technologies in the related areas.

• Preface
• Semi-supervised Learning
• Local Tangent Space Laplacian Eigenmaps
• Multi-Step Model of Switching Reinforcement Learning to Mimic Infants` Motor Development
• Reverse Engineering Networks as Ordinary Differential Equations Systems
• The Relevance of Bayesian Experimental Design for Modeling in Systems Biology
• Topographic Maps for clustering & fast identification of bacteria using 16S housekeeping gene
• Cognitive Modelling of Language Acquisition with Complex Networks
• Process Mining & Visual Analytics: Breathing Life into Business Process Models.
• (source: Nielsen Book Data)

Computational intelligence aims to emulate aspects of biological systems for developing software and/or hardware that learns and adapts. Such systems primarily include neural networks, fuzzy logic systems and evolutionary computation each of them including multiple active and fast growing research directions. This book presents new and ground-breaking research from world-renowned experts focusing on various topics related to computational intelligence. What makes this book particularly interesting is the variety of subjects attained, which offer a global perspective on this central research field in the Knowledge Age.
(source: Nielsen Book Data)

• ADVANCES IN KNOWLEDGE-BASED AND INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS; Frontiers in Artificial Intelligence and Applications; Preface; Contents; Set of Experience and Experiential Decisional DNA; An Estimation of Distribution Algorithm for Solving the Quay Crane Scheduling Problem with Availability Constraints; Performance comparison of non-RNN and RNN in Emergence of Discrete Decision Making through Reinforcement Learning; Genetic Algorithm Solving Orienteering Problem in Large Networks; Optimisation of Ensemble Classifiers using Genetic Algorithm.

• A Learning Based Evolutionary Algorithm For Distributed Multi-Depot VRPPositive Predictive Value based dynamic K-Nearest Neighbor; An Analysis of Clustering Approaches to Distributed Learning on Heterogeneously Distributed Datasets; Unsupervised Discretization Method based on Adjustable Intervals; Evaluation of Random Subspace and Random Forest Regression Models Based on Genetic Fuzzy Syste; A logic for strategies in persuasion dialogue games; Multi-Agent Logic based on Temporary Logic TS4Kn serving Web Search; LMT: A Lightweight Logical Framework for Multi-agent Systems.

• Adaptive organization for cooperative systemsAn optimal tactic for intelligent agents to conduct search & detection operations based on multiple look angles; A complex system approach for a reliable Smart Grid modeling; A Comparison Analysis of Consensus Determining Using One and Two-level Methods; Multi-Agent Logic with Distances, Uncertainty and Interaction Based on Linear Temporal Frames; A framework for handling fuzzy temporal events; On the Continuous Evaluation of the Macrostructure of Sleep.

• Entropic Dimensionality Reduction in Discriminating Between Alzheimer's Disease and Vascular DementiaMeaning Judgment Method for Alphabet Abbreviation Using the Association Mechanism; Method of Constructing the Integral OLAP-model based on Formal Concept Analysis; Semantically Enhanced Text Stemmer (SETS) for Document Clustering; Prosaico: Characterisation of objectives within the scope of an intelligent system for sport advising; Exploiting the Self-Organizing Financial Stability Map; Knowledge-Driven Method for Object Qualification in 3D Point Cloud Data.

• SAC3̂
• A Rule-Based System to Include Context in the Durability Analysis of Civil StructuresPredicting the Final Result of Sporting Events Based on Changes in Bookmaker Odds; A Novel Channel Estimation Scheme Combining Adaptive Supervised and Unsupervised Algorithms; Reference signal cancellation in passive radar using Volterra-Wiener class filter with dynamic structure; Prosodic feature normalization for emotionre cognition by using synthesized speech; Regularity Dimension of Medical Images; Automatic Scoring of Shooting Targets with Tournament Precision.

• Evolutionary computation and its applications. 1. Maximal margin algorithms for pose estimation / Ying Guo and Jiaming Li. 2. Polynomial modeling in a dynamic environment based on a particle swarm optimization / Kit Yan Chan and Tharam S. Dillon. 3. Restoration of half-toned color-quantized images using particle swarm optimization with multi-wavelet mutation / Frank H.F. Leung, Benny C.W. Yeung and Y.H. Chan
• Fuzzy logics and their applications. 4. Hypoglycemia detection for insulin-dependent diabetes mellitus: evolved fuzzy inference system approach / S.H. Ling, P.P. San and H.T. Nguyen
• Neural networks and their applications. 5. Study of limit cycle behavior of weights of perceptron / C.Y.F. Ho and B.W.K. Ling. 6. Artificial neural network modeling with application to nonlinear dynamics / Yi Zhao. 7. Solving Eigen-problems of matrices by neural networks / Yiguang Liu [and others]. 8. Automated screw insertion monitoring using neural networks: a computational intelligence approach to assembly in manufacturing / Bruno Lara, Lakmal D. Seneviratne and Kaspar Althoefer
• Support vector machines and their applications. 9. On the applications of heart disease risk classification and hand-written character recognition using support vector machines / S.R. Alty, H.K. Lam and J. Prada. 10. Nonlinear modeling using support vector machine for heart rate response to exercise / Weidong Chen [and others]. 11. Machine learning-based nonlinear model predictive control for heart rate response to exercise / Yi Zhang [and others]. 12. Intelligent fault detection and isolation of HVAC system based on online support vector machine / Davood Dehestani [and others].

This book focuses on computational intelligence techniques and their applications - fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches.

• Evolutionary computation and its applications. 1. Maximal margin algorithms for pose estimation / Ying Guo and Jiaming Li. 2. Polynomial modeling in a dynamic environment based on a particle swarm optimization / Kit Yan Chan and Tharam S. Dillon. 3. Restoration of half-toned color-quantized images using particle swarm optimization with multi-wavelet mutation / Frank H.F. Leung, Benny C.W. Yeung and Y.H. Chan
• Fuzzy logics and their applications. 4. Hypoglycemia detection for insulin-dependent diabetes mellitus: evolved fuzzy inference system approach / S.H. Ling, P.P. San and H.T. Nguyen
• Neural networks and their applications. 5. Study of limit cycle behavior of weights of perceptron / C.Y.F. Ho and B.W.K. Ling. 6. Artificial neural network modeling with application to nonlinear dynamics / Yi Zhao. 7. Solving Eigen-problems of matrices by neural networks / Yiguang Liu [and others]. 8. Automated screw insertion monitoring using neural networks: a computational intelligence approach to assembly in manufacturing / Bruno Lara, Lakmal D. Seneviratne and Kaspar Althoefer
• Support vector machines and their applications. 9. On the applications of heart disease risk classification and hand-written character recognition using support vector machines / S.R. Alty, H.K. Lam and J. Prada. 10. Nonlinear modeling using support vector machine for heart rate response to exercise / Weidong Chen [and others]. 11. Machine learning-based nonlinear model predictive control for heart rate response to exercise / Yi Zhang [and others]. 12. Intelligent fault detection and isolation of HVAC system based on online support vector machine / Davood Dehestani [and others].

This book focuses on computational intelligence techniques and their applications - fast-growing and promising research topics that have drawn a great deal of attention from researchers over the years. It brings together many different aspects of the current research on intelligence technologies such as neural networks, support vector machines, fuzzy logic and evolutionary computation, and covers a wide range of applications from pattern recognition and system modeling, to intelligent control problems and biomedical applications. Fundamental concepts and essential analysis of various computational techniques are presented to offer a systematic and effective tool for better treatment of different applications, and simulation and experimental results are included to illustrate the design procedure and the effectiveness of the approaches.

• Machine generated contents note: ch. 1 Introduction
• 1.1. Brief History of AI
• 1.2. Cognitive Issues of AI
• 1.3. Hierarchical Model of Thought
• 1.4. Symbolic Intelligence
• 1.5. Research Approaches of Artificial Intelligence
• 1.6. Automated Reasoning
• 1.7. Machine Learning
• 1.8. Distributed Artificial Intelligence
• 1.9. Artificial Thought Model
• 1.10. Knowledge Based Systems
• Exercises
• ch. 2 Logic Foundation of Artificial Intelligence
• 2.1. Introduction
• 2.2. Logic Programming
• 2.3. Nonmonotonic Logic
• 2.4. Closed World Assumption
• 2.5. Default Logic
• 2.6. Circumscription Logic
• 2.7. Nonmonotonic Logic NML
• 2.8. Autoepistemic Logic
• 2.9. Truth Maintenance System
• 2.10. Situation Calculus
• 2.11. Frame Problem
• 2.12. Dynamic Description Logic
• Exercises
• ch. 3 Constraint Reasoning
• 3.1. Introduction
• 3.2. Backtracking
• 3.3. Constraint Propagation
• 3.4. Constraint Propagation in Tree Search
• 3.5. Intelligent Backtracking and Truth Maintenance.

• 3.6. Variable Instantiation Ordering and Assignment Ordering
• 3.7. Local Revision Search
• 3.8. Graph-based Backjumping
• 3.9. Influence-based Backjumping
• 3.10. Constraint Relation Processing
• 3.11. Constraint Reasoning System COPS
• 3.12. ILOG Solver
• Exercise
• ch. 4 Qualitative Reasoning
• 4.1. Introduction
• 4.2. Basic approaches in qualitative reasoning
• 4.3. Qualitative Model
• 4.4. Qualitative Process
• 4.5. Qualitative Simulation Reasoning
• 4.6. Algebra Approach
• 4.7. Spatial Geometric Qualitative Reasoning
• Exercises
• ch. 5 Case-Based Reasoning
• 5.1. Overview
• 5.2. Basic Notations
• 5.3. Process Model
• 5.4. Case Representation
• 5.5. Case Indexing
• 5.6. Case Retrieval
• 5.7. Similarity Relations in CBR
• 5.8. Case Reuse
• 5.9. Case Retainion
• 5.10. Instance-Based Learning
• 5.11. Forecast System for Central Fishing Ground
• Exercises
• ch. 6 Probabilistic Reasoning
• 6.1. Introduction
• 6.2. Foundation of Bayesian Probability
• 6.3. Bayesian Problem Solving
• 6.4. Naive Bayesian Learning Model.

• 6.5. Construction of Bayesian Network
• 6.6. Bayesian Latent Semantic Model
• 6.7. Semi-supervised Text Mining Algorithms
• Exercises
• ch. 7 Inductive Learning
• 7.1. Introduction
• 7.2. Logic Foundation of Inductive Learning
• 7.3. Inductive Bias
• 7.4. Version Space
• 7.5. AQ Algorithm for Inductive Learning
• 7.6. Constructing Decision Trees
• 7.7. ID3 Learning Algorithm
• 7.8. Bias Shift Based Decision Tree Algorithm
• 7.9. Computational Theories of Inductive Learning
• Exercises
• ch. 8 Support Vector Machine
• 8.1. Statistical Learning Problem
• 8.2. Consistency of Learning Processes
• 8.3. Structural Risk Minimization Inductive Principle
• 8.4. Support Vector Machine
• 8.5. Kernel Function
• Exercises
• ch. 9 Explanation-Based Learning
• 9.1. Introduction
• 9.2. Model for EBL
• 9.3. Explanation-Based Generalization
• 9.4. Explanation Generalization using Global Substitutions
• 9.5. Explanation-Based Specialization
• 9.6. Logic Program of Explanation-Based Generalization
• 9.7. SOAR Based on Memory Chunks.

• 9.8. Operationalization
• 9.9. EBL with imperfect domain theory
• Exercises
• ch. 10 Reinforcement Learning
• 10.1. Introduction
• 10.2. Reinforcement Learning Model
• 10.3. Dynamic Programming
• 10.4. Monte Carlo Methods
• 10.5. Temporal-Difference Learning
• 10.6. Q-Learning
• 10.7. Function Approximation
• 10.8. Reinforcement Learning Applications
• Exercises
• ch. 11 Rough Set
• 11.1. Introduction
• 11.2. Reduction of Knowledge
• 11.3. Decision Logic
• 11.4. Reduction of Decision Tables
• 11.5. Extended Model of Rough Sets
• 11.6. Experimental Systems of Rough Sets
• 11.7. Granular Computing
• 11.8. Future Trends of Rough Set Theory
• Exercises
• ch. 12 Association Rules
• 12.1. Introduction
• 12.2. The Apriori Algorithm
• 12.3. FP-Growth Algorithm
• 12.4. CFP-Tree Algorithm
• 12.5. Mining General Fuzzy Association Rules
• 12.6. Distributed Mining Algorithm For Association Rules
• 12.7. Parallel Mining of Association Rules
• Exercises
• ch. 13 Evolutionary Computation
• 13.1. Introduction
• 13.2. Formal Model of Evolution System Theory.

• 13.3. Darwin's Evolutionary Algorithm
• 13.4. Classifier System
• 13.5. Bucket Brigade Algorithm
• 13.6. Genetic Algorithm
• 13.7. Parallel Genetic Algorithm
• 13.8. Classifier System Boole
• 13.9. Rule Discovery System
• 13.10. Evolutionary Strategy
• 13.11. Evolutionary Programming
• Exercises
• ch. 14 Distributed Intelligence
• 14.1. Introduction
• 14.2. The Essence of Agent
• 14.3. Agent Architecture
• 14.4. Agent Communication Language ACL
• 14.5. Coordination and Cooperation
• 14.6. Mobile Agent
• 14.7. Multi-Agent Environment MAGE
• 14.8. Agent Grid Intelligence Platform
• Exercises
• ch. 15 Artificial Life
• 15.1. Introduction
• 15.2. Exploration of Artificial Life
• 15.3. Artificial Life Model
• 15.4. Research Approach of Artificial Life
• 15.5. Cellular Automata
• 15.6. Morphogenesis Theory
• 15.7. Chaos Theories
• 15.8. Experimental Systems of Artificial Life
• Exercises.

Artificial intelligence is a branch of computer science and a discipline in the study of machine intelligence, that is, developing intelligent machines or intelligent systems imitating, extending and augmenting human intelligence through artificial means and techniques to realize intelligent behavior.Advanced Artificial Intelligence consists of 16 chapters. The content of the book is novel, reflects the research updates in this field, and especially summarizes the author's scientific efforts over many years. The book discusses the methods and key technology from theory, algorithm, system and applications related to artificial intelligence. This book can be regarded as a textbook for senior students or graduate students in the information field and related tertiary specialities. It is also suitable as a reference book for relevant scientific and technical personnel.
(source: Nielsen Book Data)

• Mathematical Tools for Machine Intelligence
• Advanced Applications with Machine Intelligence
• Machine Intelligence for High Level Intelligent Systems.
• (source: Nielsen Book Data)

This book brings together the contributions of leading researchers in the field of machine intelligence, covering areas such as fuzzy logic, neural networks, evolutionary computation and hybrid systems.There is wide coverage of the subject - from simple tools, through industrial applications, to applications in high-level intelligent systems which are biologically motivated, such as humanoid robots (and selected parts of these systems, like the visual cortex). Readers will gain a comprehensive overview of the issues in machine intelligence, a field which promises to play a very important role in the information society of the future.
(source: Nielsen Book Data)

• Mathematical Tools for Machine Intelligence
• Advanced Applications with Machine Intelligence
• Machine Intelligence for High Level Intelligent Systems.
• (source: Nielsen Book Data)

This book brings together the contributions of leading researchers in the field of machine intelligence, covering areas such as fuzzy logic, neural networks, evolutionary computation and hybrid systems.There is wide coverage of the subject - from simple tools, through industrial applications, to applications in high-level intelligent systems which are biologically motivated, such as humanoid robots (and selected parts of these systems, like the visual cortex). Readers will gain a comprehensive overview of the issues in machine intelligence, a field which promises to play a very important role in the information society of the future.
(source: Nielsen Book Data)

• Learning and evolution
• Human interaction
• Robotic simulation.

This book constitutes the refereed proceedings of the First International Symposium on Artificial Life and Intelligent Agents, ALIA 2014, held in Bangor, UK, in November 2014. The 10 revised full papers were carefully reviewed and selected from 20 submissions. The papers are organized in topical sections on learning and evolution; human interaction; robotic simulation.

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.
(source: Nielsen Book Data)

Theory, algorithms, and applications of machine learning techniques to overcome "covariate shift" non-stationarity. As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.
(source: Nielsen Book Data)

• Multimodal Human Spacecraft Interaction in Remote Environments
• A Framework for Collaborative Aspects of Intelligent Service Robot
• Piecewise Bezier Curves Path Planning with Continuous Curvature Constraint for Autonomous Driving
• Combined Heuristic Approach to Resource-Constrained Project Scheduling Problem
• A Development of Data-Logger for Indoor Environment
• Multiobjective Evolutionary Optimization and Machine Learning: Application to Renewable Energy Predictions
• Hybriding Intelligent Host-Based and Network-Based Stepping Stone Detections
• Open Source Software Use in City Government
• Pheromone-Balance Driven Ant Colony Optimization with Greedy Mechanism
• Study of Pitchfork Bifurcation in Discrete Hopfield Neural Network
• Grammatical Evolution and STE Criterion
• Data Quality in ANFIS Based Soft Sensors
• The Meccano Method for Automatic Volume Parametrization of Solids
• A Buck Converter Model for Multi-Domain Simulations
• The Computer Simulation of Shaping in Rotating Electrical Discharge Machining
• Parameter Identification of a Nonlinear Two Mass System Using Prior Knowledge
• Adaptive and Neural Learning for Biped Robot Actuator Control
• Modeling, Simulation, and Analysis for Battery Electric Vehicles
• Modeling Confined Jets with Particles and Swril
• Robust Tracking and Control of MIMO Processes with Input Saturation and Unknown Disturbance
• Analysis of Priority Rule-Based Scheduling in Dual-Resource-Constrained Shop-Floor Scenarios
• A Hybrid Framework for Servo-Actuated Systems Fault Diagnosis
• Multigrid Finite Volume Method for FGF-2 Transport and Binding
• Integrated Mining Fuzzy Association Rules For Mineral Processing State Identification
• A Combined Cycle Power Plant Simulator: A Powerful, Competitive, and Useful Tool for Operator's Training
• Texture Features Extraction in Mammograms Using Non-Shannon Entropies
• A Wideband DOA Estimation Method Based on Arbitrary Group Delay
• Spatial Speaker Spatial Positioning of Synthesized Speech in Java
• Commercial Break Detection and Content Based Video Retrieval
• ClusterDAM: Clustering Mechanism for Delivery of Adaptive Multimedia Content in Two-Hop Wireless Networks
• Ranking Intervals in Complex Stochastic Boolean Systems Using Intrinsic Ordering
• Predicting Memory Phases
• Information Security Enhancement to Public-Key Cryptosystem Through Magic Squares
• Resource Allocation for Grid Applications: An Economy Model
• A Free and Didactic Implementation of the SEND Protocol for IPv6
• A Survey of Network Benchmark Tools
• Hybrid Stock Investment Strategy Decision Support System
• Towards Performance Analysis of Ad hoc Multimedia Network
• Towards the Performance Optimization of Public-key Algorithms Using Fuzzy Modular Arithematic and Addition Chain
• RBDT-1 Method: Combining Rules and Decision Tree Capabilities
• Computational and Theoretical Concepts for Regulating Stem Cells Using Viral and Physical Methods
• DFA, a Biomedical Checking Tool for the Heart Control System
• Generalizations in Mathematical Epidemiology
• Review of Daily Physical Activity Monitoring System Based on Single Triaxial Accelerometer and Portable Data Measurement Unit
• A Study of the Protein Folding Problem by a Simulation Model
• Analysing Multiobjective Fitness Function with Finite State Automata.

A large international conference on Advances in Machine Learning and Systems Engineering was held in UC Berkeley, California, USA, October 20-22, 2009, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2009). Machine Learning and Systems Engineering contains forty-six revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization algorithms, Experiment designs, Complex system identification, Computational modeling, and industrial applications. Machine Learning and Systems Engineering offers the state of the art of tremendous advances in machine learning and systems engineering and also serves as an excellent reference text for researchers and graduate students, working on machine learning and systems engineering.

• What Is Computational Intelligence and Where Is It Going?.- New Millennium AI and the Convergence of History.- The Challenges of Building Computational Cognitive Architectures.- Programming a Parallel Computer: The Ersatz Brain Project.- The Human Brain as a Hierarchical Intelligent Control System.- Artificial Brain and OfficeMate TR based on Brain Information Processing Mechanism.- Natural Intelligence and Artificial Intelligence: Bridging the Gap between Neurons and Neuro-Imaging to Understand Intelligent Behaviour.- Computational Scene Analysis.- Brain-, Gene-, and Quantum Inspired Computational Intelligence: Challenges and Opportunities.- The Science of Pattern Recognition. Achievements and Perspectives.- Towards Comprehensive Foundations of Computational Intelligence.- Knowledge-Based Clustering in Computational Intelligence.- Generalization in Learning from Examples.- A Trend on Regularization and Model Selection in Statistical Learning: A Bayesian Ying Yang Learning Perspective.- Computational Intelligence in Mind Games.- Computer Go: A Grand Challenge to AI.- Noisy Chaotic Neural Networks for Combinatorial Optimization.
• (source: Nielsen Book Data)

In recent years computational intelligence has been extended by adding many other subdisciplines and this new field requires a series of challenging problems that will give it a sense of direction in order to ensure that research efforts are not wasted. This book written by top experts in computational intelligence provides such clear directions and a much-needed focus on the most important and challenging research issues.
(source: Nielsen Book Data)

• Prerequisites.- Simple Learning Classifier Systems.- The XCS Classifier System.- How XCS Works: Ensuring Effective Evolutionary Pressures.- When XCS Works: Towards Computational Complexity.- Effective XCS Search: Building Block Processing.- XCS in Binary Classification Problems.- XCS in Multi-Valued Problems.- XCS in Reinforcement Learning Problems.- Facetwise LCS Design.- Towards Cognitive Learning Classifier Systems.- Summary and Conclusions.
• (source: Nielsen Book Data)

Rule-basedevolutionaryonlinelearningsystems, oftenreferredtoasMichig- style learning classi?er systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generali- tion capabilities of genetic algorithms promising a ?exible, online general- ing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with a- mal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in di?erent problem types, problem structures, c- ceptspaces, andhypothesisspacesstayednearlyunpredictable. Thisbookhas the following three major objectives: (1) to establish a facetwise theory - proachforLCSsthatpromotessystemanalysis, understanding, anddesign; (2) to analyze, evaluate, and enhance the XCS classi?er system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding of LCS functioning that enables the successful application of LCSs to diverse problem types and problem domains. The quantitative analysis of XCS shows that the inter- tive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Moreover, the facetwise analysis approach facilitates the successful design of more - vanced LCSs including Holland's originally envisioned cognitive systems. Martin V.
(source: Nielsen Book Data)

• Scott Consequence Relations
• Biconsequence Relations
• Four-Valued Logics
• Nonmonotonic Semantics
• Default Consequence Relations
• Argumentation Theory
• Production and Causal Inference
• Epistemic Consequence Relations
• Modal Nonmonotonic Logics.
• (source: Nielsen Book Data)

Many approaches in the field of nonmonotonic and "commonsense" reasoning are actually different representations of the same basic ideas and constructions. This book gives a logical formalization of the original, explanatory approach to nonmonotonic reasoning. It uses the basic formalism of biconsequence relations, as well as derived systems of default, autoepistemic and causal inference, to cover in a single framework such diverse systems as default logic, autoepistemic and modal nonmonotonic logics, input/output and causal logics, argumentation theory, and semantics of general logic programs with negation as failure. This approach provides a clear separation between logical (monotonic) and nonmonotonic aspects of nonmonotonic reasoning. The separation allows, in particular, to single out the logics underlying modern logic programming and restore thereby the connection between logic programming and logic.
(source: Nielsen Book Data)

• Paper Sessions Applications 1.- Artificial Intelligence Systems in Micromechanics.- Integrating Two Artificial Intelligence Theories in a Medical Diagnosis Application.- Artificial Intelligence and Law.- Virtual Market Environment for Trade.- Neural Networks and Fuzzy Systems.- An Artificial Neural Networks Approach to the Estimation of Physical Stellar Parameters.- Evolutionary Robot Behaviors Based on Natural Selection and Neural Network.- Control of Overhead Crane by Fuzzy-Pid with Genetic Optimisation.- Creative Design of Fuzzy Logic Controller.- On-Line Extraction of Fuzzy rules in a Wastewater Treatment Plant.- Agents.- An Autonomous Intelligent Agent Architecture Based on Constructivist AI.- Finding Manufacturing Expertise Using Ontologies and Cooperative Agents.- Using Agents in the Exchange of Product Data.- Applications 2.- A Pervasive Identification and Adaptation System for the Smart House.- Deductive Diagnosis of Digital Circuits.- Verification of NASA Emergent Systems.- Theory.- Knowledge Base Structure.- Learning Bayesian Metanetworks from Data with Multilevel Uncertainty.- Using Organizational Structures Emergence for Maintaining Functional Integrity in Embedded Systems Networks.- Efficient Attribute Reduction Algorithm.- Using Relative Logic for Pattern Recognition.- Intelligent Tutoring and Collaboration.- Mathtutor: A Multi-Agent Intelligent Tutoring System.- Analysis and Intelligent Support of Learning Communities in Semi-Structured Discussion Environments.- An Adaptive Assessment System to Evaluate Student Ability Level.- Forming the Optimal Team of Experts for Collaborative Work.- Internet.- Impact on Performance of Hypertext Classification of Selective Rich HTML Capture.- Introducing a Star Topology into Latent Class Models for Collaborative Filtering.- Dialoguing with an Online Assistant in a Financial Domain: The VIP-Advisor Approach.- An Agency for Semantic-Based Automatic Discovery of Web-Services.- Genetic Algorithms.- GESOS: A Multi-Objective Genetic Tool for Project Management Considering Technical and Non-Technical Constraints.- Using Genetic Algorithms and Tabu Search Parallel Models to Solve the Scheduling Problem.- Modelling Document Categories by Evolutionary Learning of Text Centroids.- Ontologies and Data Mining.- ODEval: A Tool for Evaluating RDF(S), DAML+OIL, and OWL Concept Taxonomies.- Air - A Platform for Intelligent Systems.- Swissanalyst.- Data Mining by MOUCLAS Algorithm for Petroleum Reservoir Characterization from Well Logging Data.- Reasoning and Scheduling.- Verification of Procedural Reasoning System (PRS) Programs Using Coloured Petri Nets (CPN).- On-Line Possibilistic Diagnosis Based on Expert Knowledge for Engine Dyno Test Benches.- CBR and Micro-Architecture Anti-Patterns Based Software Design Improvement.- A Decision Support System (DSS) for the Railway Scheduling Problem.- An Interactive Multicriteria Optimisation Approach to Scheduling.
• (source: Nielsen Book Data)

Artificial Intelligence and Innovations (AIAI) will interest researchers, IT professionals and consultants by examining technologies and applications of demonstrable value. The conference focused on profitable intelligent systems and technologies. AIAI focuses on real world applications; therefore authors should highlight the benefits of AI technology for industry and services. Novel approaches solving business and industrial problems, using AI, will emerge from this conference.
(source: Nielsen Book Data)

• Chapter 1. GUI Testing for Introductory Object-Oriented Programming Exercises (Ushio Inoue).-
• Chapter 2. Python Deserialization Denial of Services Attacks and their Mitigations (Kousei Tanaka).-
• Chapter 3. A Branch-and-Bound Based Exact Algorithm for the Maximum Edge-Weight Clique Problem (Satoshi Shimizu).-
• Chapter 4. A Software Model for Precision Agriculture Framework Based on Smart Farming System and Application of IoT Gateway (Symphorien Karl Yoki Donzia).-
• Chapter 5. Components of Mobile Integration in Social Business and E-commerce Application (Mechelle Grace Zaragoza).
• (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 outcomes of the 5th ACIS International Conference on Computational Science/Intelligence & Applied Informatics (CSII 2018), which was held on July 10-12, 2018 in Yonago, Japan. The aim of the conference was to bring together researchers and scientists, businesspeople and entrepreneurs, teachers, engineers, computer users, and students to discuss the various fields of computer science, to share their experiences, and to exchange new ideas and information in a meaningful way. All aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them are all explored here. The conference organizers selected the best papers from among those accepted for presentation. The papers were chosen on the basis of review scores submitted by members of the program committee and subsequently underwent further rigorous review. Following this second round of review, 13 of the conference's most promising papers were selected for this Springer (SCI) book. We eagerly await the important contributions that we know these authors will make to the field of computer and information science.
(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)

• Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications
• Part I Deep Learning in Sensing
• Vision to Language: Methods, Metrics and Datasets
• Deep Learning Techniques for Geospatial Data Analysis
• Deep Learning Approaches in Food Recognition
• Part II Deep Learning in Social Media and IOT
• Deep Learning for Twitter Sentiment Analysis: The Effect of Pre-trained Word Embedding
• A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks
• Part III Deep Learning in the Medical Field
• Survey on Deep Learning Techniques for Medical Imaging Application Area
• Deep Learning Methods in Electroencephalography
• Part IV Deep Learning in Systems Control
• The Implementation and the Design of a Hybriddigital PI Control Strategy Based on MISO Adaptive Neural Network Fuzzy Inference System Models-A MIMO Centrifugal Chiller case study
• A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum System
• Part V Deep Learning in Feature Vector Processing
• Stock Market Forecasting by Using Support Vector Machines
• An Experimental Exploration of Machine Deep Learning for Drone Conflict Prediction
• Deep Dense Neural Network for Early Prediction of Failure-Prone Students
• Part VI Evaluation of Algorithm Performance
• Non-parametric Performance Measurement with Artificial Neural Networks
• A Comprehensive Survey on the Applications of Swarm Intelligence and Bio-Inspired Evolutionary Strategies
• Detecting Magnetic Field Levels Emitted by Tablet Computers via Clustering Algorithms.

At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
(source: Nielsen Book Data)

• Part I. Intelligence and decision making system
• A Context-Based Video Compression: A Quantum-Inspired Vector Quantization Approach
• An Enhanced Database Recovery Model Based on Game Theory for Mobile Applications
• Location Estimation of RF Emitting Source Using Supervised Machine Learning Technique
• An Effective Offloading Model Based on Genetic Markov Process for Cloud Mobile Applications
• Toward an Efficient CRWSN Node Based on Stochastic Threshold Spectrum Sensing
• Video Captioning Using Attention Based Visual Fusion with Bi-temporal Context and Bi-modal Semantic Feature Learning
• Matchmoving Previsualization Based on Artificial Marker Detection
• Research Method of Blind Path Recognition Based on DCGAN
• The Impact of the Behavioral Factors on Investment Decision-Making: A Systemic Review on Financial Institutions
• Part II. Deep learning technology and applications (4 papers)
• Part III. Document and sentiment analysis (8 papers)
• Part IV. Blockchain and Cyber Physical System (6 papers)
• Part V Health Informatics and AI Against COVID-19 (7 papers)
• Part VI. Big Data Analytics and Service Quality (4 papers)
• Part VII. Data Mining, Decision Making, and Intelligent Systems (7 papers)
• Part VIII. Power and control systems (10 papers)
• Part IX. Business Intelligence (6 papers)
• Part X. Social media and Digital transformation (5 papers)
• Part XI. Robotic, Control Design and Smart Systems (12 papers).

This book presents the proceedings of the 6th International Conference on Advanced Intelligent Systems and Informatics 2020 (AISI2020), which took place in Cairo, Egypt, from October 19 to 21, 2020. This international and interdisciplinary conference, which highlighted essential research and developments in the fields of informatics and intelligent systems, was organized by the Scientific Research Group in Egypt (SRGE). The book is divided into several sections, covering the following topics: Intelligent Systems, Deep Learning Technology, Document and Sentiment Analysis, Blockchain and Cyber Physical System, Health Informatics and AI against COVID-19, Data Mining, Power and Control Systems, Business Intelligence, Social Media and Digital Transformation, Robotic, Control Design, and Smart Systems.
(source: Nielsen Book Data)

• Introduction The Case-Base Reasoning and Decision Making Learning Formal Aspects Summary and Beyond.
• (source: Nielsen Book Data)

Case-based reasoning is a methodology with a long tradition in artificial intelligence that brings together reasoning and machine learning techniques to solve problems based on past experiences or cases. Given a problem to be solved, reasoning involves the use of methods to retrieve similar past cases in order to reuse their solution for the problem at hand. Once the problem has been solved, learning methods can be applied to improve the knowledge based on past experiences. In spite of being a broad methodology applied in industry and services, case-based reasoning has often been forgotten in both artificial intelligence and machine learning books. The aim of this book is to present a concise introduction to case-based reasoning providing the essential building blocks for the design of case-based reasoning systems, as well as to bring together the main research lines in this field to encourage students to solve current CBR challenges.
(source: Nielsen Book Data)