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• Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm.-Design of Palm Acupuncture Points Indicator
• Low-rank Representation and Locality-constrained Regression for Robust Low-Resolution Face Recognition
• Face Recognition Benchmark with ID Photos
• Scene Relighting using a Single Reference Image through Material Constrained Layer Decomposition
• Applying Adaptive Actor-critic Learning to Human Upper Lime Lifting Motion
• A Demand-based Allocation Mechanism for Virtual Machine
• A Joint Hierarchy Model for Action Recognition Using Kinect
• QoS-Based Medical Program Evolution
• An Improved 3D Surface Reconstruction Method based on Three Wavelength Phase Shift Profilometry
• The Research on the Lung Tumor Imaging based on the Electrical Impedance Tomography
• Combining CNN and MRF for Road Detection
• The Increasing of Discrimination Accuracy of Waxed Apples based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis
• A Diffeomorphic Demons Approach to Statistical Shape Modeling.

This book highlights selected papers presented at the 2nd International Symposium on Artificial Intelligence and Robotics 2017 (ISAIR2017), held in Nakamura Centenary Memorial Hall, Kitakyushu, Japan on November 25-26, 2017. Today, the integration of artificial intelligence and robotic technologies has become a topic of growing interest for both researchers and developers from academic fields and industries worldwide, and artificial intelligence is poised to become the main approach pursued in next-generation robotics research. The rapidly growing number of artificial intelligence algorithms and big data solutions has significantly extended the number of potential applications for robotic technologies. However, it also poses new challenges for the artificial intelligence community. The aim of this symposium is to provide a platform for young researchers to share the latest scientific achievements in this field, which are discussed in these proceedings.

• Introduction - Ethical and Sustainable Human and Artificial AI Artificial Intelligence and Robotics Applying The Benefits of AI and Identifying Challenges and Risks Starting AI - How to Build A Machine Learning Toolbox Algorithms The Management, Roles and Responsibilities of Humans and Machines AI in Use in Industry - Reimagining Everything in the Fourth Industrial Revolution AI Case Studies .
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In alignment with BCS AI Foundation and Essentials certificates, this introductory guide provides the understanding you need to start building artificial intelligence (AI) capability into your organisation. You will learn how AI is being utilised today and how it is likely to be used in the future to balance the talents of humans and machines. You will explore robotics and machine learning within the context of AI, and discover how the challenges AI presents are being addressed.
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• Introduction The Query Process and Barriers to Finding Information Online Online Search: An Evolution Search and Discovery Technologies: An Overview Information Access: A Spectrum of Needs and Uses Future Tense: The Next Era in Information Access and Discovery Answer Machines.
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The Answer Machine is a practical, non-technical guide to the technologies behind information seeking and analysis. It introduces search and content analytics to software buyers, knowledge managers, and searchers who want to understand and design effective online environments. The book describes how search evolved from an expert-only to an end user tool. It provides an overview of search engines, categorization and clustering, natural language processing, content analytics, and visualization technologies. Detailed profiles for Web search, eCommerce search, eDiscovery, and enterprise search contrast the types of users, uses, tasks, technologies, and interaction designs for each. These variables shape each application, although the underlying technologies are the same. Types of information tasks and the trade-offs between precision and recall, time, volume and precision, and privacy vs. personalization are discussed within this context. The book examines trends toward convenient, context-aware computing, big data and analytics technologies, conversational systems, and answer machines. The Answer Machine explores IBM Watson's DeepQA technology and describes how it is used to answer health care and Jeopardy questions. The book concludes by discussing the implications of these advances: how they will change the way we run our businesses, practice medicine, govern, or conduct our lives in the digital age.
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• Introduction The Case-Base Reasoning and Decision Making Learning Formal Aspects Summary and Beyond.
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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.
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• Preface Acknowledgments Introduction Related Learning Paradigms Lifelong Supervised Learning Continual Learning and Catastrophic Forgetting Open-World Learning Lifelong Topic Modeling Lifelong Information Extraction Continuous Knowledge Learning in Chatbots Lifelong Reinforcement Learning Conclusion and Future Directions Bibliography Authors' Biographies.
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Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks-which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning-most notably, multi-task learning, transfer learning, and meta-learning-because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
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• Automating Inquiry Uncertainty Sampling Searching Through the Hypothesis Space Minimizing Expected Error and Variance Exploiting Structure in Data Theory Practical Considerations.
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The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose ""queries, "" usually in the form of unlabeled data instances to be labeled by an ""oracle"" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or ""query selection frameworks."" We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities.
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• Markov Decision Processes Value Prediction Problems Control For Further Exploration.
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Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.
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• Preface Planning and Autonomous Behavior Classical Planning: Full Information and Deterministic Actions Classical Planning: Variations and Extensions Beyond Classical Planning: Transformations Planning with Sensing: Logical Models MDP Planning: Stochastic Actions and Full Feedback POMDP Planning: Stochastic Actions and Partial Feedback Discussion Bibliography Author's Biography.
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Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective.
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• Support Vector Machines for Classification Kernel-based Models Learning with Kernels.
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Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such as prediction with real-valued outputs, novelty detection and the handling of complex output structures such as parse trees. Finally, we give an overview of the main types of kernels which are used in practice and how to learn and make predictions from multiple types of input data.
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• Introduction MDPs Fundamental Algorithms Heuristic Search Algorithms Symbolic Algorithms Approximation Algorithms Advanced Notes.
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Markov Decision Processes (MDPs) are widely popular in Artificial Intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems.
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• Introduction Preference Modeling and Reasoning Uncertainty in Preference Reasoning Aggregating Preferences Stable Marriage Problems.
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Computational social choice is an expanding field that merges classical topics like economics and voting theory with more modern topics like artificial intelligence, multiagent systems, and computational complexity. This book provides a concise introduction to the main research lines in this field, covering aspects such as preference modelling, uncertainty reasoning, social choice, stable matching, and computational aspects of preference aggregation and manipulation. The book is centered around the notion of preference reasoning, both in the single-agent and the multi-agent setting. It presents the main approaches to modeling and reasoning with preferences, with particular attention to two popular and powerful formalisms, soft constraints and CP-nets. The authors consider preference elicitation and various forms of uncertainty in soft constraints. They review the most relevant results in voting, with special attention to computational social choice. Finally, the book considers preferences in matching problems. The book is intended for students and researchers who may be interested in an introduction to preference reasoning and multi-agent preference aggregation, and who want to know the basic notions and results in computational social choice.
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"The first book that provides a systematic and rigorous logical theory of causality"-- Provided by publisher.

"A collection of critical essays dealing with the social and ethical impacts of AI including issues of trust, reliability, and bias"-- Provided by publisher.

• Multi-objective Optimization.- A Parametric Study of Crossover Operators in Pareto-based Multiobjective Evolutionary Algorithm.- Non-dominated Sorting and Crowding Distance Based Multi-objective Chaotic Evolution.- On Performance Improvement based on Restart Meta-Heuristic Implementation for Solving Multi-Objective Optimization Problems.- Using multi-objective evolutionary algorithm to solve dynamic environment and economic dispatch with EVs.- Improved Interval Multi-objective Evolutionary Optimization Algorithm based on Directed Graph.- A Novel Linear Time Invariant Systems Order Reduction Approach based on Cooperative Multi-Objective Genetic Algorithm.- Solving Constrained Multi-objective Optimization Problems with Evolutionary Algorithms.- Portfolio Optimization.- Multi-objective Comprehensive Learning Bacterial Foraging Optimization for Portfolio Optimization.- Metaheuristics for Portfolio Optimization.- Community Detection.- Community Detection under Exponential Random Graph Model: A Metaheuristic Approach.- An Enhanced Particle Swarm Optimization Based on Physarum Model for Community Detection.- The Design and Development of the Virtual Learning Community for Teaching Resources Personalized Recommendation.- The Effect of Event Sentiment on Product Recommendation in a Microblog Platform.- Multi-Agent Systems and Swarm Robotics.- Solar Irradiance Forecasting Based on the Multi-agent Adaptive Fuzzy Neuronet.- Passive Field Dynamics Method: an Advanced Physics-based Approach for Formation Control of Robot Swarm.- Adaptive Potential Fields Model for Solving Distributed Area Coverage Problem in Swarm Robotics.- Swarm-based Spreading Points.- A Survivability Enhanced Swarm Robotic Searching System Using Multi-Objective Particle Swarm Optimization.- Autonomous Coordinated Navigation of Virtual Swarm Bots in Dynamic Indoor Environments by Bat Algorithm.- Building Fractals with a Robot Swarm.- A Stigmergy Based Search Method for Swarm Robots.- Cooperative Control of Multi-Robot System Using Mobile Agent for Multi Source Localization.- Hybrid Optimization Algorithms and Applications.- Evolutionary Fuzzy Control of Three Robots Cooperatively Carrying an Object for Wall Following through the Fusion of Continuous ACO and PSO.- Optimal Operational Planning of Energy Plants by Multi-population Differential Evolutionary Particle Swarm Optimization.- A Review on Hybridization of Particle Swarm Optimization with Artificial Bee Colony.- Study on Greedy Search to Improve Simulated Annealing Algorithm for Large-scale Traveling Salesman Problem.- A Hybrid Swarm based Composition for Chinese Music.- Fuzzy and Swarm Approach.- Fuzzy Logic Controller Design for Tuning the Cooperation of Bio-Inspired Algorithms.- Making Capital Budgeting Decisions for Project Abandonment by Fuzzy Approach.- An Imputation for Missing Data Features based on Fuzzy Swarm Approach in Heart Disease Classification.- Clustering and Forecast.- Total Optimization of Smart City Using Initial Searching Points Generation Based on k-means Algorithm.- Clustering Analysis of ECG Data Streams.- A Novel Multi-cell Multi-Bernoulli Tracking Method Using Local Fractal Feature Estimation.- An Improved Locality Preserving Projection Method for Dimensionality Reduction with Hyperspectral Image.- Applying a Classification Model for Selecting Postgraduate Programs.- University Restaurant Sales Forecast Based on BP neural Network
• InShanghai Jiao Tong University Case.- Classification and Detection.- Swarm ANN/SVR-based modeling method for warfarin dose prediction in Chinese.- A Novel HPSOSA for Kernel Function Type and Parameter Optimization of SVR in Rainfall Forecasting.- An Improved Weighted ELM with Krill Herd Algorithm for Imbalanced Learning.- Fast Pseudo Random Forest using Discrimination Hyper-space.- A Fast Video Vehicle Detection Approach based on Improved Adaboost Classifier.- Detection of Repetitive Forex Chart Patterns.- Damage Estimation from Cues of Image Change.- Identifying Deceptive Review Comments with Rumor and Lie Theories.- Identifying Fake Review Comments for Hostel Industry.- Planning and Routing Problems.- Multi-UAV Cooperative Path Planning for Sensor Placement Using Cooperative Coevolving Genetic Strategy.- Optimal Micro-Siting Planning Considering Long-Term Electricity Demand.- A Hyper-Heuristic Method for UAV Search Planning.- An Efficient MVMO-SH Method for Optimal Capacitor Allocation in Electric Power Distribution Systems.- A Method of New Workers' Accurate Acceptance of Tasks Using Capable Sensing.- A Genetic Mission Planner for Solving Temporal Multi-agent Problems with Concurrent Tasks.- Reformulation and Metaheuristic for the Team Orienteering Arc Routing Problem.- Application of Smell Detection Agent Based Algorithm for Optimal Path Identification by SDN Controllers.- A Comparison of Heuristic Algorithms for Bus Dispatch.- Simulation and Application of Algorithms CVRP to Optimize the Transport of Minerals Metallic and Nonmetallic by Rail for Export.- Dialog System Applications.- User Intention Classification in An Entities Missed In-vehicle Dialog System.- An Exploratory Study of Factors Affecting Number of Fans onFacebook Based on Dialogic Theory.- Assembling Chinese-Mongolian Speech Corpus via Crowdsourcing.- Robotic Control.- Developing Robot Drumming Skill with Listening-Playing Loop.- Evaluation of Parameters of Transactions When Remote Robot Control.- Desktop Gestures Recognition for Human Computer Interaction.- Approach to the Diagnosis and Configuration of Servo Drives in Heterogeneous Machine Control Systems.- Other Applications.- Gravitational Search Algorithm in Recommendation Systems.- A Driver Model Based on Emotions.- A Binaural Signal Synthesis Approach for Fast Rendering of Moving Sound.- Semantic Evolutionary Visualization.

This book and its companion volumes, LNCS volumes 10385 and 10386, constitute the proceedings of the 8th International Conference on Swarm Intelligence, ICSI 2017 held from July 27 to August 1, 2017, in Fukuoka, Japan. ICSI 2017 was held in conjunctionwith the Second International Conference on Data Mining and Big Data (DMBD 2017)at Fukuoka, Japan, to share common mutual ideas, promote transverse fusion, and stimulate innovation. ICSI 2017 received 267 submissions from about 512 authors in 35 countries and regions across six continents. Each submission was reviewed by at least two, and on average 2.9 reviewers. Based on rigorous reviews by the Program Committee members and reviewers, 133 high-quality papers were selected for publication in this proceedings volume, with an acceptance rate of 49.81%. The papers are organized in 24 cohesive sections covering all major topics of swarm intelligence and computational intelligence research and development.

• Architectures.- Mathematical foundations.- Algorithms.- Safety.- Understanding.- Human cognition.- Philosophy.
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This book constitutes the proceedings of the 10th International Conference on Artificial General Intelligence, AGI 2017, held in Melbourne, VIC, Australia, in August 2017. The 24 regular papers presented in this book together with 1 short paper were carefully reviewed and selected from 35 submissions. They cover topics such as architectures; mathematical foundations; algorithms; safety; understanding; human cognition; and philosophy.
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• Intro; Preface; Organization; Contents
• Part I; Contents
• Part II; Neural Networks and Their Applications; Author Profiling with Classification Restricted Boltzmann Machines; 1 Introduction; 2 Author Profile Dimensions; 3 Restricted Boltzmann Machines; 4 Probabilities and Gradients; 4.1 Discriminative Training; 4.2 Generative Training; 5 Evaluation Datasets; 6 Experiments and Results; 6.1 Overall Results; 7 Conclusions; References; Parallel Implementation of the Givens Rotations in the Neural Network Learning Algorithm; 1 Introduction; 2 Givens Elimination Step; 3 Givens QR Decomposition.

• 4 QR Decomposition in Neural Network Weights Update5 Parallel Implementation; 6 Simulation Results; 7 Conclusion; References; Parallel Levenberg-Marquardt Algorithm Without Error Backpropagation; 1 Introduction; 2 Parallel Realisation; 2.1 Calculating the Weight Derivatives Without Error Backpropagation; 2.2 Calculating the A Matrix and the Gradient Vector; 2.3 The QR Decomposition Based on the Householser Reflections; 3 Computational Results; 4 Conclusions; References; Spectral Analysis of CNN for Tomato Disease Identification; Abstract; 1 Introduction; 2 Related Works.

• 3 Spectral Analysis of CNN for Tomato Disease3.1 Deep Visualization of CNN; 3.2 Color Sensitivity of RGB Images; 3.3 Sensitivity to Color with Different Wavelength Values; 3.3.1 Visible Spectrum of Images; 4 Experimental Results; 4.1 Dataset Description; 4.2 CNN Activations and Features Visualization; 4.2.1 Activations of Neurons; 4.2.2 RGB Color Sensitivity; 4.2.3 Feature Maps; 5 Conclusion and Future Work; Acknowledgments; References; From Homogeneous Network to Neural Nets with Fractional Derivative Mechanism; 1 Introduction; 2 Weight Distribution with Fractional Calculus.

• 3 Fractional Derivative Inside Neuron Transfer Function4 The Fractional Mechanism Within 2D Homogeneous Network; 5 Conclusion; References; Neurons Can Sort Data Efficiently; 1 Introduction; 2 Models of Neurons, Receptors, and the Senses; 2.1 Sensory Fields and Sensors; 2.2 Extreme, Sensory and Object Neurons; 3 Simplistic Sequential Neural Associative Sorting; 4 Conclusions and Remarks; References; Avoiding Over-Detection: Towards Combined Object Detection and Counting; 1 Introduction; 2 Related Work; 2.1 Deep Learning Methods for Object Detection; 2.2 Deep Learning Methods for Cell Detection.

• 3 Method3.1 Loss Function; 3.2 Model Architecture; 4 Results; 5 Conclusion; References; Echo State Networks Simulation of SIR Distributed Control; 1 Introduction; 2 Echo State Networks; 3 SIR Model with Delay and Spatial Diffusions; 3.1 Distributed Optimal Control Problem; 4 Discretisation and Adaptive Critic Neural Networks Solution of the Distributed Optimal Control; 4.1 Numerical Simulation; 5 Conclusion; References; The Study of Architecture MLP with Linear Neurons in Order to Eliminate the ``vanishing Gradient'' Problem; 1 Introduction; 2 Nonlinearity capabilities of deep neural networks.

The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the second volume are organized in the following five parts: data mining; artificial intelligence in modeling, simulation and control; various problems of artificial intelligence; special session: advances in single-objective continuous parameter optimization with nature-inspired algorithms; special session: stream data mining.
(source: Nielsen Book Data)
The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the first volume are organized in the following five parts: neural networks and their applications; fuzzy systems and their applications; evolutionary algorithms and their applications; computer vision, image and speech analysis; and bioinformatics, biometrics and medical applications.
(source: Nielsen Book Data)

• Intro; Preface; Organization; Contents
• Part II; Contents
• Part I; Data Mining; Computer Based Stylometric Analysis of Texts in Polish Language; 1 Introduction; 2 Features Used in Stylometry; 3 Performed Stylometric Analysis; 3.1 Data Sets and Classes; 3.2 Feature Generation Methods; 3.3 Classification; 4 Results; 4.1 Stylometric Features; 4.2 Training Set Size; 4.3 Classifiers Accuracy; 4.4 Statistical Features Importance; 4.5 Grammatical Classes Importance; 5 Summary; References; Integration Base Classifiers Based on Their Decision Boundary; 1 Introduction; 2 Basic Concept.

• 3 Proposed Method4 Experimental Studies; 5 Conclusion; References; Complexity of Rule Sets Induced by Two Versions of the MLEM2 Rule Induction Algorithm; 1 Introduction; 2 Incomplete Data Sets; 3 Probabilistic Approximations; 4 Rule Induction; 4.1 True MLEM2; 4.2 Emulated MLEM2; 5 Experiments; 6 Conclusions; References; Spark-Based Cluster Implementation of a Bug Report Assignment Recommender System; 1 Introduction; 2 Related Work; 3 The Recommender System; 3.1 Datasets; 3.2 Cleansing; 3.3 Preprocessing and Feature Reduction; 3.4 Training the Recommender; 3.5 Implementation.

• 4 Results and Discussion4.1 Results of Dimensionality Reduction Techniques; 4.2 Choice of SVM Kernel; 4.3 Comparison with Previous Approaches and Scalability; 5 Conclusion; References; The Bag-of-Words Method with Dictionary Analysis by Evolutionary Algorithm; 1 Introduction; 2 Description of Proposed Methods; 3 Experimental Research; 4 Conclusions; References; The Novel Method of the Estimation of the Fourier Transform Based on Noisy Measurements; 1 Introduction; 2 Orthogonal Series Estimation of Regression Function and Its Spectrum.

• 3 Nonparametric Orthogonal Series Estimation of Fourier Transform4 Simulation Example; 5 Remarks and Extensions; References; A Complete Efficient FFT-Based Algorithm for Nonparametric Kernel Density Estimation; 1 Introduction; 2 Problem Demonstration; 3 The Improved FFT-Based Algorithm for Density Estimation; 4 Binning; 5 Experiments; 6 Conclusions; References; A Framework for Business Failure Prediction; 1 Introduction; 2 Related Work; 3 The Dataset and the Proposed Framework; 3.1 Details of Dataset; 3.2 Proposed Framework; 4 Performance Evaluation Results; 5 Conclusions; References.

• Fuzzy Clustering with -Hyperballs and Its Application to Data Classification1 Introduction; 2 Fuzzy Clustering with -Hyperballs; 3 FCH Based Classification; 4 Results and Discussion; 5 Conclusions; References; Two Modifications of Yinyang K-means Algorithm; 1 Introduction; 2 Yinyang K-means; 3 Modifications of the Algorithm; 3.1 Regrouping of Centroids; 3.2 Initial Grouping and Regrouping Using Same-Size K-means; 4 Experimental Results; 5 Conclusions; References; Detection of the Innovative Logotypes on the Web Pages; 1 Introduction; 2 Related Works vs. Proposed Approach.

The two-volume set LNAI 10245 and LNAI 10246 constitutes the refereed proceedings of the 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017, held in Zakopane, Poland in June 2017. The 133 revised full papers presented were carefully reviewed and selected from 274 submissions. The papers included in the second volume are organized in the following five parts: data mining; artificial intelligence in modeling, simulation and control; various problems of artificial intelligence; special session: advances in single-objective continuous parameter optimization with nature-inspired algorithms; special session: stream data mining.
(source: Nielsen Book Data)

• Artificial Life and Computational Intelligence.- Extending the Delaunay Triangulation Based Density Measurement to Many-objective Optimization.- Emotion, Trustworthiness and Altruistic Punishment in a Tragedy of the Commons Social Dilemma.- Equity Option Strategy Discovery and Optimization Using a Memetic Algorithm.- Co-Evolving Line Drawings with Hierarchical Evolution.- Reliability estimation of individual multi-target regression predictions.- Feedback Modulated Attention Within a Predictive Framework.- A Batch Infill Strategy for Computationally Expensive Optimization Problems.- Automatic Clustering and Summarisation of Microblogs: A Multi-Subtopic Phrase Reinforcement Algorithm.- Generation and exploration of architectural form using a composite Cellular Automata.- Wrapper Feature Construction for Figure-ground Image Segmentation Using Genetic Programming.- Surrogate-assisted Multi-swarm Particle Swarm Optimization of Morphing Airfoils.- Applying Dependency Patterns in Causal Discovery of Latent Variable Models.- An Evolutionary Multi-criteria Journey Planning Algorithm for Multi-modal Transportation Networks.- Estimating Passenger Preferences Using Implicit Relevance Feedback for Personalized Journey.- Quantitative Assessment of Hearts Function: A Hybrid Mechanism for Left Ventricles Segmentation from Cine MRI Sequences.- A Hybrid feature selection scheme based on local compactness and global separability for improving roller bearing diagnostic performance.- Reliable Fault Diagnosis of Bearings Using Distance and Density Similarity on an Enhanced k-NN.- Towards Solving TSPN with Arbitrary Neighborhoods: A Hybrid Solution.- Detectable Genetic Algorithms-based techniques for solving Dynamic Optimisation Problem with Unknown Active Variables.- Neighbourhood analysis: a case study on Google Machine Reassignment Problem.- Optimisation Algorithms and Applications.- Multi-Objective Optimisation with Multiple Preferred Regions.- An Adaptive Memetic Algorithm for the Architecture Optimisation Problem.- Resource Constrained Job Scheduling with Parallel Constraint-based ACO.- An Iterated Local Search with Guided Perturbation for the Heterogeneous Fleet Vehicle Routing Problem with Time Windows and Three-Dimensional Loading Constraints.- A Memetic Cooperative Co-evolution Model for Large Scale Continuous Optimization.- Investigating the Generality of Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling with Machine Breakdown.- Exploratory Analysis of Clustering Problems Using a Comparison of Particle Swarm Optimization and Differential Evolution.- A PSO-based Reference Point Adaption Method for Genetic Programming Hyper-heuristic in Many-Objective Job Shop Scheduling.- Optimal power allocation of wireless sensor networks with multi-operator based constrained differential evolution.- CEMAB: A Cross-Entropy-based Method for Large-Scale Multi-Armed Bandits.- Binary PSO for Web Service Location-Allocation.- A MOEA/D with Non-uniform Weight Vector Distribution Strategy for Solving the Unit Commitment Problem in Uncertain Environment.
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This book constitutes the refereed proceedings of the Third Australasian Conference on Artificial Life and Computational Intelligence, ACALCI 2017, held in Geelong, VIC, Australia, in January/February 2017. The 32 papers presented in this volume were carefully reviewed and selected from 47 submissions. They were organized in topical sections named: artificial life and computational intelligence and optimization algorithms and applications. .
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• Machine learning: online learning, regression, label classification, deep learning, feature selection, recommendation system.- Knowledge discovery: recommendation system, community detection, pattern mining, misc.
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This book constitutes the proceedings of the 20th International Conference on Discovery Science, DS 2017, held in Kyoto, Japan, in October 2017, co-located with the International Conference on Algorithmic Learning Theory, ALT 2017. The 18 revised full papers presented together with 6 short papers and 2 invited talks in this volume were carefully reviewed and selected from 42 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in topical sections on machine learning: online learning, regression, label classification, deep learning, feature selection, recommendation system; and knowledge discovery: recommendation system, community detection, pattern mining, misc.
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• Chapter 1 Comparative Study on Fractional Order PID and PID Controllers on Noise Suppression for Manipulator Trajectory Control.-
• Chapter 2 Control of the Temperature of a Finite Diffusive Interface Medium using the CRONE Controller.-
• Chapter 3 Grey Predictor Assisted Fuzzy and Fractional Order Fuzzy Control of a Moving Cart Inverted Pendulum.-
• Chapter 4 H Design with Fractional-Order PI D Type Controllers.-
• Chapter 5 On the Electronic Realizations of Fractional-Order Phase-Lead-Lag Compensators with OpAmps and FPAAs.-
• Chapter 6 Robust Adaptive Supervisory Fractional Order Controller for Optimal Energy Management in Wind Turbine with Battery Storage.-
• Chapter 7 Robust Adaptive Interval Type-2 Fuzzy Synchronization for a Class of Fractional Order Chaotic Systems.-
• Chapter 8 Optimal Fractional Order Proportional-Integral-Differential Controller for Inverted Pendulum with Reduced Order Linear Quadratic Regulator.-
• Chapter 9 Towards a Robust Fractional Order PID Stabilizer for Electric Power Systems.-
• Chapter 10 Application of Fractional Order Controllers on Experimental and Simulation Model of Hydraulic Servo System.
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The book reports on the latest advances in and applications of fractional order control and synchronization of chaotic systems, explaining the concepts involved in a clear, matter-of-fact style. It consists of 30 original contributions written by eminent scientists and active researchers in the field that address theories, methods and applications in a number of research areas related to fractional order control and synchronization of chaotic systems, such as: fractional chaotic systems, hyperchaotic systems, complex systems, fractional order discrete chaotic systems, chaos control, chaos synchronization, jerk circuits, fractional chaotic systems with hidden attractors, neural network, fuzzy logic controllers, behavioral modeling, robust and adaptive control, sliding mode control, different types of synchronization, circuit realization of chaotic systems, etc. In addition to providing readers extensive information on chaos fundamentals, fractional calculus, fractional differential equations, fractional control and stability, the book also discusses key applications of fractional order chaotic systems, as well as multidisciplinary solutions developed via control modeling. As such, it offers the perfect reference guide for graduate students, researchers and practitioners in the areas of fractional order control systems and fractional order chaotic systems.
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• Classification.- Clustering.- Association rule.- Pattern mining.- Specific data mining methods.- Different multi-media data types.- Image mining.- Text mining.- Video mining.- Web mining.
• .
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017, held in New York, NY, USA in July/August 2017.The 31 full papers presented in this book were carefully reviewed and selected from 150 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.
(source: Nielsen Book Data)

• Knowledge representation and reasoning.- Data mining and machine learning.- Deep learning and its applications.- Document analysis.- Intelligent information systems.- Swarm intelligence.
• (source: Nielsen Book Data)

This book constitutes the refereed conference proceedings of the 11th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2017, held in Gadong, Brunei, in November 2017. The 40 revised full papers presented were carefully reviewed and selected from 82 submissions. They are organized in the following topical sections: knowledge representation and reasoning; data mining and machine learning; deep learning and its applications; document analysis; intelligent information systems; swarm intelligence.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Experimental Evaluation of Graph Classification with Hadamard Code Graph Kernels
• 1 Introduction
• 2 Graph Kernels
• 2.1 Framework of Representative Graph Kernels
• 2.2 Drawbacks of Existing Graph Kernels
• 3 Graph Kernels Based on the Hadamard Code
• 4 Experimental Evaluation
• 4.1 Experiments on Artificial Datasets
• 4.2 Experiments on Real-World Graphs
• 4.3 Effect of Assignment of Initial Labels
• 5 Conclusion
• References
• Document Clustering Games in Static and Dynamic Scenarios
• 1 Introduction
• 2 Game Theory

• 3 Dominant Set Clustering
• 4 Document Clustering Games
• 4.1 Document Representation
• 4.2 Data Preparation
• 4.3 Graph Construction
• 4.4 Clustering
• 4.5 Strategy Space Implementation
• 4.6 Clustering Games
• 5 Experimental Setup
• 5.1 Basic Experiments
• 5.2 Experiments with Feature Selection
• 5.3 Experiments with LSA
• 5.4 Comparison with State-of-the-Art Algorithms
• 5.5 Experiments with No Cluster Number
• 5.6 Experiments on Streaming Data
• 6 Conclusions
• References
• Criteria for Mixture-Model Clustering with Side-Information
• 1 Introduction

• 2 Clustering with Side-Information
• 2.1 Data Description
• 2.2 Mixture Model
• 3 Criteria
• 3.1 Criterion BIC
• 3.2 Criterion AIC
• 3.3 Criterion NEC
• 4 Results
• 4.1 Comparison of the Original and Adapted Versions of the Three Criteria
• 4.2 Influence of the Amount of Side-Information
• 4.3 Iris Data Set
• 4.4 Climatic Data
• 5 Conclusion
• References
• Near-Boolean Optimization: A Continuous Approach to Set Packing and Partitioning
• 1 Introduction
• 2 Set Partitioning: Full-Dimensional Case
• 3 Set Packing: Lower-Dimensional Case
• 4 Local Search
• 5 Near-Boolean Functions

• 5.1 Approximations
• 5.2 Equivalent Polynomials
• 5.3 MLE of Partition Functions
• 6 Near-Boolean Games
• 7 Conclusions
• References
• Approximate Inference in Related Multi-output Gaussian Process Regression
• 1 Introduction
• 2 Gaussian Process Regression
• 3 Multi-output Gaussian Process
• 3.1 Related Work
• 3.2 Multi-output Joint-Covariance Kernels
• 3.3 GP Regression Using Joint-Covariance
• 4 Approximating Inference
• 4.1 Variational Approximation on Multi-output GP
• 4.2 Distributed Inference on Multi-output GP
• 5 Experiments
• 5.1 Experiments on Theoretical Data

• 5.2 Experiments on Flight Test Data
• 6 Conclusions and Future Work
• References
• An Online Data Validation Algorithm for Electronic Nose
• 1 Introduction
• 1.1 Background
• 1.2 Motivation
• 1.3 Data Preparation
• 2 Data Analysis
• 3 Simulation
• 4 Experiment
• 5 Computational Complexity
• 6 Conclusion
• References
• Near-Duplicate Retrieval: A Benchmark Study of Modified SIFT Descriptors
• 1 Introduction
• 2 Near-Duplicate Images
• 3 Related Works
• 3.1 SIFT-128 D Descriptor
• 4 Region Compressed SIFT Descriptor
• 5 Evaluation
• 5.1 Benchmark Datasets

This book contains revised and extended versions of selected papers from the 5th International Conference on Pattern Recognition, ICPRAM 2016, held in Rome, Italy, in February 2016. The 13 full papers were carefully reviewed and selected from 125 initial submissions and describe up-to-date applications of pattern recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance pattern recognition methods.
(source: Nielsen Book Data)

• Low-Level Image Processing Based on Interval-Valued Fuzzy Sets and Scale-Space Smoothing.- Generalized Net Representation of Dataflow Process Networks.- Wireless Sensor Positioning ACO Algorithm.- Time Accounting Artificial Neural Networks for Biochemical Process Models.- Periodic Time-varying Observer-based Learning Control of A/F Ratio in Multi-cylinder IC Engines.- Fuzzy T-S Model Based Design of Min-Max Control for Uncertain Nonlinear Systems.- Modeling Parallel Optimization of the Early Stopping Method of Multilayer Perceptron.- Intelligent Controls for Switched Fuzzy Systems: Synthesis via Non-standard Lyapunov Functions.- A New Architecture for an Adaptive Switching Controller Based on Hybrid Multiple T-S Models.- Optimization of Linear Objective Function under min-Probabilistic Sum Fuzzy Linear Equations Constraint.- Intuitionistic Fuzzy Logic Implementation to Assess Purposeful Model Parameters Genesis.- Dynamic Representation and Interpretation in a Multiagent 3D Tutoring System.- Generalized Net Model of the Scapulohumeral Rhythm.- Method for Interpretation of Functions of Propositional Logic by Specific Binary Markov Processes.- Generalized Net Models of Academic Promotion and Doctoral Candidature.- Modeling Telehealth Services with Generalized Nets.- State-Space Fuzzy-Neural Predictive Control.- Free Search and Particle Swarm Optimisation Applied to Global Optimisation Numerical Tests From Two to Hundred Dimensions.-Intuitionistic Fuzzy Sets Generated by Archimedean Metrics and Ultrametrics.-Production Rule and Network Structure Models for Knowledge Extraction from Complex Processes Under Uncertainty.
• (source: Nielsen Book Data)

This volume is a brief, yet comprehensive account of new development, tools, techniques and solutions in the broadly perceived "intelligent systems". New concepts and ideas concern the development of effective and efficient models which would make it possible to effectively and efficiently describe and solve processes in various areas of science and technology. Special emphasis is on the dealing with uncertainty and imprecision that permeates virtually all real world processes and phenomena, and has to properly be modeled by formal and algorithmic tools and techniques so that they be adequate and useful. The papers in this volume concern a wide array of possible techniques exemplified by, on the one hand, logic, probabilistic, fuzzy, intuitionistic fuzzy, neuro-fuzzy, etc. approaches. On the other hand, they represent the use of such systems modeling tools as generalized nets, optimization and control models, systems analytic models, etc. They concerns a variety of approaches, from pattern recognition, image analysis, education system modeling, biological and medical systems modeling, etc.
(source: Nielsen Book Data)

• Analogical reasoning.- Argumentation.- Bayesian networks.- Belief functions.- Conditionals.- Credal sets, credal networks.- Decision theory, decision making and reasoning under uncertainty.- Fuzzy sets, fuzzy logic.- Logics.-Orthopairs.- Possibilistic networks.- Probabilistic logics, probabilistic reasoning.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2017, held in Lugano, Switzerland, in July 2017. The 44 revised full papers presented together with 5 abstracts of invited talks were carefully reviewed and selected from 63 submissions and cover topics on analogical reasoning; argumentation; Bayesian networks; belief functions; conditionals; credal sets, credal networks; decision theory, decision making and reasoning under uncertainty; fuzzy sets, fuzzy logic; logics; orthopairs; possibilistic networks; and probabilistic logics, probabilistic reasoning.
(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)

• Table of Contents Recognizing traffic signs using Convnets Annotating Images with Object Detection API Caption generation for images Building GANs for Conditional Image Creation Stock Price Prediction with LSTM Create & Train Machine Translation Systems Train and set up a Chatbot, able to discuss like a human Detecting Duplicate Quora Questions Building a TensorFlow Recommender Systems Video Games by Reinforcement learning.
• (source: Nielsen Book Data)

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios Key Features Build efficient deep learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects related to Computer Vision, stock prediction, chatbots and more Book DescriptionTensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. What you will learn Set up the TensorFlow environment for deep learning Construct your own ConvNets for effective image processing Use LSTMs for image caption generation Forecast stock prediction accurately with an LSTM architecture Learn what semantic matching is by detecting duplicate Quora questions Set up an AWS instance with TensorFlow to train GANs Train and set up a chatbot to understand and interpret human input Build an AI capable of playing a video game by itself -and win it! Who this book is forThis book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.
(source: Nielsen Book Data)

• Compressing Bayesian Networks: Swarm-Based Descent, Efficiency, and Posterior Accuracy.- De-Causalizing NAT-Modeled Bayesian Networks for Inference Efficiency.- A Novel Evaluation Methodology for Assessing Off-Policy Learning Methods in Contextual Bandits.- Synthesizing Controllers: On the Correspondence Between LTL Synthesis and Non-Deterministic Planning.- Logic-Based Benders Decomposition for Two-Stage Flexible Flow Shop Scheduling with Unrelated Parallel Machines.- Advice-Based Exploration in Model-Based Reinforcement Learning.- Deep Super Learner: A Deep Ensemble for Classification Problem.- One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data.- MedFact: Towards Improving Veracity of Medical Information in Social Media Using Applied Machine Learning.- Re-ranking Candidate Lists for Improved Lexical Induction.- Analysis of Social Media Posts for Early Detection of Mental Health Conditions.- Motor Bearing Fault diagnosis Using Deep Convolutional Neural Networks with 2D Analysis of Vibration Signal.- Mobile App for Detection of Counterfeit Banknotes.- A Multi-agent Framework for Understanding Addiction.- Infusing Domain Knowledge to Improve the Detection of Alzheimer's Disease from Everyday Motion Behavior.- An Incremental Machine Learning Algorithm for Nuclear Forensics.- MML-Based Approach for Determining the Number of Topics in EDCM Mixture Models.- Constrained Bayesian Optimization for Problems with Piece-wise Smooth Constraints.- Dimensionality Reduction and Visualization by Doubly Kernelized Unit Ball Embedding.- Accelerated Gradient and Block-wise Gradient Methods for Big Data Factorization.- Learning Belief Revision Operators.- Solving Constraint Satisfaction Problems Using Firey Algorithms.- An AI Planning-Based Approach to the Multi-Agent Plan Recognition Problem.- Predicting Transportation Modes of GPS Trajectories Using Feature Engineering and Noise Removal.- Prediction of Container Damage Insurance Claims for Optimized Maritime Port Operations.- Drug-Target Interaction Network Predictions for Drug Repurposing Using LASSO-based Regularized Linear Classification Model.- Optimal Scheduling for Smart Charging of Electric Vehicles Using Dynamic Programming.- Combining MCTS and A3C for Prediction of Spatially Spreading Processes in Forest Wildfire Settings.- Text-based Detection of Unauthorized Users of Social Media Accounts.- N-gram Based Approach for Automatic Prediction of Essay Rubric Marks.- Matching Resumes to Job Descriptions with Stacked Models.- Towards a Comprehensive Evaluation of Recommenders: A Cognition-based Approach.- A Sentence-level Sparse Gamma Topic Model for Sentiment Analysis.- Topic Detection and document Similarity on Financial News.- Software Defect Prediction from Code Quality Measurements via Machine Learning.- Automated Scheduling: Reinforcement Learning Approach to Algorithm Policy Learning.- Estimating Vineyard Grape Yield from Images.- Real-time Deep Learning Pedestrians Classification on a Micro-controller.- A Unified Evaluation Framework for Recommender Systems.- Early Detection of Alzheimer's Disease Using Deep Learning.- Learning with Prior Domain Knowledge and Insufficient Annotated Data.- Predicting Crime Using Spatial Features.- A Tool for Defining and Simulating Storage Strategies on the Smart Grid.- Decision Assist for Self-Driving Cars.- Rule Mining and Prediction Using the Flek Machine
• A New Machine Learning Engine.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 31th Canadian Conference on Artificial Intelligence, Canadian AI 2018, held in Toronto, ON, Canada, in May 2018. The 16 regular papers and 18 short papers presented together with 7 Graduate Student Symposium papers and 4 Industry Track papers were carefully reviewed and selected from 72 submissions. The focus of the conference was on artificial intelligence research and advanced information and communications technology.
(source: Nielsen Book Data)

• Theories and models of swarm intelligence.- ant colony optimization
• particle swarm optimization.- artificial bee colony algorithms.- genetic algorithms.- differential evolution.- fireworks algorithms.- bacterial foraging optimization.- artificial immune system.- hydrologic cycle optimization.- other swarm-based optimization algorithms.- hybrid optimization algorithms.- multi-objective optimization.- large-scale global optimization.- multi-agent systems.- swarm robotics
• fuzzy logic approaches.- planning and routing problems.- recommendation in social media.- prediction.- classification.- finding patterns.- image enhancement.- deep learning.
• (source: Nielsen Book Data)

The two-volume set of LNCS 10941 and 10942 constitutes the proceedings of the 9th International Conference on Advances in Swarm Intelligence, ICSI 2018, held in Shanghai, China, in June 2018. The total of 113 papers presented in these volumes was carefully reviewed and selected from 197 submissions. The papers were organized in topical sections as follows: theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithms; bacterial foraging optimization; artificial immune system; hydrologic cycle optimization; other swarm-based optimization algorithms; hybrid optimization algorithms; multi-objective optimization; large-scale global optimization; multi-agent systems; swarm robotics; fuzzy logic approaches; planning and routing problems; recommendation in social media; prediction, classification; finding patterns; image enhancement; deep learning. .
(source: Nielsen Book Data)

• TECHNICAL STREAM PAPERS.- BEST TECHNICAL PAPER
• Using Constraint Satisfaction Problem Solving to Enable Workflow Flexibility by Deviation.- MACHINE LEARNING AND NEURAL NETWORKS.- Masked Conditional Neural Networks for Environmental Sound Classification.- Ensembles of Recurrent Neural Networks for Robust Time Series Forecasting.- A Blackboard based Hybrid Multi-Agent System for Improving Classification Accuracy using Reinforcement Learning Techniques.- MACHINE LEARNING, SPEECH AND VISION AND FUZZY LOGIC.- Programming Without Program or How To Program in Natural Language Utterances.- Capturing the Dynamics of Cellular Automata, for the Generation of Synthetic Persian Music, Using Conditional Restricted Boltzmann Machines.- Measuring Relations between Concepts in Conceptual Spaces.- Towards a Deep reinforcement learning approach for Tower Line Wars.- Improving Modular Classification Rule Induction with G-Prism using Dynamic Rule Term Boundaries.- SHORT TECHNICAL PAPERS.- Inference and discovery in remote sensing data with features extracted using deep networks.- Quantization Error-based Regularization in Neural Networks.- Knowledge Transfer in Neural Language Models.- Emotion Recognition in Text Using PPM.- An experimental comparison of ensemble classifiers for evolving data streams.- A learning automata Local Contribution Sampling applied to hydropower production optimization.- Toward Component Based Self-Adaptive Multi-Strategic Pedagogical Agent.- APPLICATION STREAM PAPERS.- BEST APPLICATION PAPER.- Cable Belief Networks.- AI FOR HEALTHCARE.- Towards the Integration of Prescription Analytics into Health Policy and General Practice.- An Ontology to support Knowledge Management in Behaviour-based Healthcare.- Using Semantic Web technologies to underpin the SNOMED CT query language.- APPLICATIONS OF MACHINE LEARNING.- Feature Level Ensemble Method for Classifying Multi-media Data.- Reliability Based Bayesian Inference for Probabilistic Classification: An Overview of Sampling Schemes.- Algorithms and Architecture for Real-time Recommendations at News UK.- Customer Contact Journey Prediction.- APPLICATIONS OF NEURAL NETWORKS AND FUZZY LOGIC.- A Better Predictor of Marathon Race Times based on Neural Networks.- Fuzzy Logic Based Personalized Task Recommendation System for Field Services.- Application of Neural Network in Modeling Commuter Choice Behavior with a Novel Fuzzy Access Measure.- CASE-BASED REASONING.- Predicting Fraud in Mobile Money Transfer Using Case-Based Reasoning.- Predicting Dust Storms Using Hybrid Intelligence System.- AI TECHNIQUES.- Decision support system for green real-life field scheduling problems.- Multi-label Classification of Movie Posters into Genres with Rakel ensemble method.- Parameter Search for a small swarm of AUVs using Particle Swarm Optimisation.- SHORT APPLICATION PAPERS.- Deep learning for classification of bi-lingual ads in online classifieds.- Enhancing Symmetry in GAN Generated Fashion Images.- Predicting Service Levels Using Neural Networks.- Using Machine Learning for Dynamic Multicast Capacity Planning.- Asynchronous Population-Based Hill Climbing Applied to SPICE Model Generation from EM Simulation Data.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 37th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2017, held in Cambridge, UK, in December 2017. The 25 full papers and 12 short papers presented in this volume were carefully reviewed and selected from 55 submissions. There are technical and application papers which were organized in topical sections named: machine learning and neural networks; machine learning, speech and vision and fuzzy logic; short technical papers; AI for healthcare; applications of machine learning; applications of neural networks and fuzzy logic; case-based reasoning; AI techniques; and short applications papers. .
(source: Nielsen Book Data)

• Classification.- Meta-Learning.- Reinforcement Learning.- Streams and Time Series.- Subgroup and Subgraph Discovery.- Text Mining.- Applications.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 21st International Conference on Discovery Science, DS 2018, held in Limassol, Cyprus, in October 2018, co-located with the International Symposium on Methodologies for Intelligent Systems, ISMIS 2018. The 30 full papers presented together with 5 abstracts of invited talks in this volume were carefully reviewed and selected from 71 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Classification; meta-learning; reinforcement learning; streams and time series; subgroup and subgraph discovery; text mining; and applications.
(source: Nielsen Book Data)

• Intelligent Systems.- Machine Vision.- Communications.- Security.- e-Learning, e-Business.
• (source: Nielsen Book Data)

This book highlights the emerging field of intelligent computing and developing smart systems. It includes chapters discussing the outcome of challenging research related to distributed computing, smart machines and their security related research, and also covers next-generation communication techniques and the networking technologies that have the potential to build the future communication infrastructure. Bringing together computing, communications and other aspects of intelligent and smart computing, it contributes to developing a roadmap for future research on intelligent systems.
(source: Nielsen Book Data)

• Uncertainty management and decision support.- Clustering and classification.- Machine learning applications.- Statistical methods.- Econometric applications.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 6th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2018, held in Hanoi, Vietnam, in March 2018.The 39 revised full papers presented in this book were carefully reviewed and selected from 76 initial submissions. The papers are organized in topical sections on uncertainty management and decision support; clustering and classification; machine learning applications; statistical methods; and econometric applications.
(source: Nielsen Book Data)

• Page Rank vs. Katz: Is the centrality algorithm choice relevant to measure user influence in Twitter?.- Weighted means based filters for SAR Imagery.- On Combination of Wavelet Transformation and Stabilized KH Interpolation for Fuzzy Inferences Based on High Dimensional Sampled Functions.- Abductive Reasoning on Molecular Interaction Maps.- Efficient unfolding of fuzzy connectives for multi-adjoint logic programs.- On Generalizations of Concept Lattices.- Generating fuzzy attribute rules via Fuzzy Formal Concept Analysis.
• (source: Nielsen Book Data)

This book presents recent research in the field of interaction between computational intelligence and mathematics, ranging from theory to applications. Computational intelligence, or soft computing consists of various bio-inspired methods, especially fuzzy systems, artificial neural networks, evolutionary and memetic algorithms. These research areas were initiated by professionals in various applied fields, such as engineers, economists, and financial and medical experts. Although computational intelligence offered solutions (at least quasi-optimal solutions) for problems with high complexity, vague and undeterministic features, initially little attention was paid to the mathematical models and analysis of the methods successfully applied. A typical example is the extremely successful Mamdani-algorithm, and its modifications and extensions, applied since the mid-1970s, where the first analysis of the simplest cases, showing why this algorithm was so efficient and stable, was not given until the early 1990s. Since the mid-2000s, the authors have organized international conferences annually to focus on the mathematical methodological issues in connection with computational intelligence approaches. These conferences have attracted a large number of submissions with a wide scope of topics and quality. The editors selected several high-quality papers and approached the authors to submit an essentially extended and improved book chapter based on the lectures.This volume is the first contributed book on the subject.
(source: Nielsen Book Data)

• Classification.- Clustering.- Image mining.- Text mining.- Video mining.-Web mining.- Graph mining.- Process mining.
• (source: Nielsen Book Data)

This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.
(source: Nielsen Book Data)

• Classification.- Clustering.- Image mining.- Text mining.- Video mining.-Web mining.- Graph mining.- Process mining.
• (source: Nielsen Book Data)

This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.
(source: Nielsen Book Data)

• Artificial intelligence.- Natural language processing.- Knowledge representation and reasoning.- Planning and scheduling Computer vision.- Machine learning.- Modeling and simulation.- Distributed artificial intelligence.
• (source: Nielsen Book Data)

This book constitutes the refereed conference proceedings of the 12th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2018, held in Hanoi, Vietnam, in November 2018. The 16 full papers presented together with 9 short papers were carefully reviewed and selected from 65 submissions. They are organized in the following topical sections: control, planning and scheduling, pattern recognition, knowledge mining, software applications, strategy games and others.
(source: Nielsen Book Data)

• Chapter 1. Contraction methods for correlation clustering: the order is important (Laszlo Aszalos).-
• Chapter 2. Optimisation of Preparedness and Response of Health services in major crises using the IMPRESS platform (Nina Dobrinkova).-
• Chapter 3. The new approach for dynamic optimization with variability constraints (Pawel Drag).-
• Chapter 4. Intercriteria Analisys of ACO Performance for Workforce Planning Problem (Olympia Roeva).-
• Chapter 5. Is Prufer Code encoding always a bad idea? (Hanno Hildmann).-
• Chapter 6. A model for wireless-access network topology and a PSO-based approach for its optimization (Hanno Hildmann).-
• Chapter 7. InterCriteria Analysis Approach for Comparison of Simple and Multi-population Genetic Algorithms Performance (Maria Angelova).-
• Chapter 8. Structure Optimization and Learning of Fuzzy Cognitive Map with the use of Evolutionary Algorithm and Graph Theory Metrics (Katarzyna Poczeta).-
• Chapter 9. Fuzziness in the Berth Allocation Problem (Flabio Gutierrez).-
• Chapter 10. Identifying Clusters in Spatial Data via Sequential Importance Sampling (Nishanthi Raveendran).-
• Chapter 11. Multiobjective Optimization Grover Adaptive Search (Benjamin Baran.-
• Chapter 12. Discovering Knowledge from Predominantly Repetitive Data by InterCriteria Analysis (Olympia Roeva).
• (source: Nielsen Book Data)

Our everyday lives are practically unthinkable without optimization. We constantly try to minimize our effort and to maximize the reward or progress achieved. Many real-world and industrial problems arising in engineering, economics, medicine and other domains can be formulated as optimization tasks. This volume presents a comprehensive collection of extended contributions from the 2017 Workshop on Computational Optimization. Presenting recent advances in computational optimization, it addresses important concrete applications, e.g. the modeling of physical processes, wildfire modeling, modeling processes in chemical engineering, workforce planning, wireless access network topology, parameter settings for controlling various processes, berth allocation, identification of homogeneous domains, and quantum computing. The book shows how to develop algorithms for them based on new intelligent methods like evolutionary computations, ant colony optimization, constrain programming and others.
(source: Nielsen Book Data)

• Classification and Clustering.- Image annotation using a semantic hierarchy.- Malignant Brain Tumor Classification using the Random Forest Method.- Rotationally Invariant Bark Recognition.- Dynamic voting in multi-view learning for radiomics applications.- Iterative Deep Subspace Clustering.- A scalable spectral clustering algorithm based on landmark-embedding and cosine similarity.- Deep Learning and Neural Networks.- On Fast Sample Preselection for Speeding up Convolutional Neural Network Training.- UAV First View Landmark Localization via Deep Reinforcement Learning.- Context Free Band Reduction Using a Convolutional Neural Network.- Local Patterns and Supergraph for Chemical Graph Classification with Convolutional Networks.- Learning Deep Embeddings via Margin-based Discriminate Loss.- Dissimilarity Representations and Gaussian Processes.- Protein Remote Homology Detection using Dissimilarity-based Multiple Instance Learning.- Local Binary Patterns based on Subspace Representation of Image Patch for Face Recognition.- An image-based representation for graph classification.- Visual Tracking via Patch-based Absorbing Markov Chain.- Gradient Descent for Gaussian Processes Variance Reduction.- Semi and Fully Supervised Learning Methods.- Sparsification of Indefinite Learning Models.- Semi-supervised Clustering Framework Based on Active Learning for Real Data.- Supervised Classification Using Feature Space Partitioning.- Deep Homography Estimation with Pairwise Invertibility Constraint.- Spatio-temporal Pattern Recognition and Shape Analysis.- Graph Time Series Analysis using Transfer Entropy.- Analyzing Time Series from Chinese Financial Market Using A Linear-Time Graph Kernel.- A Preliminary Survey of Analyzing Dynamic Time-varying Financial Networks Using Graph Kernels.- Few-Example Affine Invariant Ear Detection in the Wild.- Line Voronoi Diagram using Elliptical Distances.- Structural Matching.- Modelling the Generalised Median Correspondence through an Edit Distance.- Learning the Graph Edit Distance edit costs based on an embedded model.- Ring Based Approximation of Graph Edit Distance.- Graph Edit Distance in the exact context.- The VF3-Light Subgraph Isomorphism Algorithm: when doing less is more effective.- A Deep Neural Network Architecture to Estimate Node Assignment Costs for the Graph Edit Distance.- Error-Tolerant Geometric Graph Similarity.- Learning Cost Functions for Graph Matching.- Multimedia Analysis and Understanding.- Matrix Regression-based Classification for Face Recognition.- Plenoptic Imaging for Seeing Through Turbulence.- Weighted Local Mutual Information for 2D-3D Registration in Vascular Interventions.- Cross-model Retrieval with Reconstruct Hashing.- Deep Supervised Hashing with Information Loss.- Single Image Super Resolution via Neighbor Reconstruction.- An Efficient Method for Boundary Detection from Hyperspectral Imagery.- Graph-Theoretic Methods.- Bags of Graphs for Human Action Recognition.- Categorization of RNA Molecules using Graph Methods.- Quantum Edge Entropy for Alzheimer's Disease Analysis.- Approximating GED using a Stochastic Generator and Multistart IPFP.- Offline Signature Verification by Combining Graph Edit Distance and Triplet Networks.- On Association Graph Techniques for Hypergraph Matching.- Directed Network Analysis using Transfer Entropy Component Analysis.- A Mixed Entropy Local-Global Reproducing Kernel for Attributed Graphs.- Dirichlet Densifiers: Beyond Constraining the Spectral Gap.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2018, held in Beijing, China, in August 2018. The 49 papers presented in this volume were carefully reviewed and selected from 75 submissions. They were organized in topical sections named: classification and clustering; deep learning and neurla networks; dissimilarity representations and Gaussian processes; semi and fully supervised learning methods; spatio-temporal pattern recognition and shape analysis; structural matching; multimedia analysis and understanding; and graph-theoretic methods. .
(source: Nielsen Book Data)

• Table of Contents An Introduction to Generative AI: "Drawing" Data from Models Setting Up a TensorFlow Lab Building Blocks of Deep Neural Networks Teaching Networks to Generate Digits Painting Pictures with Neural Networks Using VAEs Image Generation with GANs Style Transfer with GANs Deepfakes with GANs The Rise of Methods for Text Generation NLP 2
• .0: Using Transformers to Generate Text Composing Music with Generative Models Play Video Games with Generative AI: GAIL Emerging Applications in Generative AI.
• (source: Nielsen Book Data)

Understand the theory behind deep generative models and experiment with practical examples Key Features Build a solid understanding of the inner workings of generative models Experiment with practical TensorFlow 2.x implementations of state-of-the-art models Explore a wide range of current and emerging use cases for deep generative AI Book DescriptionDeep generative models are powerful tools that rival human creative capabilities. In this book, you'll discover how these models emerged, from restricted Boltzmann machines and deep belief networks to VAEs, GANs, and beyond. You'll develop a foundational understanding of generative AI and learn how to implement models yourself in TensorFlow, supported by references to seminal and current research. After getting to grips with the fundamentals of deep neural networks, you'll set up a scalable code lab in the cloud and begin to explore the huge breadth of potential use cases for generative models. You'll look at Open AI's news generator, networks for style transfer and deepfakes, synergy with reinforcement learning, and more. As you progress, you'll recreate the code that makes these possible, piecing together TensorFlow layers, utility functions, and training loops to uncover links between the different modes of generation. By the end of this book, you will have acquired the knowledge to create and implement your own generative AI models. What you will learn Implement paired and unpaired style transfer with networks like StyleGAN Use facial landmarks, autoencoders, and pix2pix GAN to create deepfakes Build several text generation pipelines based on LSTMs, BERT, and GPT-2, learning how attention and transformers changed the NLP landscape Compose music using hands-on LSTM models, simple GANs, and the intricate MuseGAN Train a deep learning agent to move through a simulated physical environment Discover emerging applications of generative AI, such as folding proteins and creating videos from images Who this book is forThis book will appeal to Python programmers, seasoned modelers, and machine learning engineers who are keen to learn about the creation and implementation of generative models. To make the most out of this book, you should have a basic familiarity with probability theory, linear algebra, and deep learning.
(source: Nielsen Book Data)

• Part I: Artificial Intelligence and Optimization.- Stochastic Proximal Gradient Algorithm with Minibatches Application to Large Scale Learning Models.- The Human Mental Search Algorithm for Solving Optimisation Problems.- Reducing Redundant Association Rules Using Type-2 Fuzzy Logic.- Identifiability of Discrete Concentration Graphical Models with a Latent Variable.- An Automatic Classification of Genetic Mutations by Exploring Different Classifiers.- Towards Artificial Intelligence: Concepts, Applications, and Innovations.- Part II: Big Data and Artificial Intelligence Applications.- In Depth Analysis, Applications and Future Issues of Artificial Neural Network.- Big Data and Deep Learning in Plant Leaf Diseases Classification for Agriculture.- Machine Learning Cancer Diagnosis Based on Medical Image Size and Modalities.
• (source: Nielsen Book Data)

This book provides a detailed overview of the latest developments and applications in the field of artificial intelligence and data science. AI applications have achieved great accuracy and performance with the help of developments in data processing and storage. It has also gained strength through the amount and quality of data which is the main nucleus of data science. This book aims to provide the latest research findings in the field of artificial intelligence with data science.
(source: Nielsen Book Data)

• Faster Result Retrieval from Health Care Product Sales Data Warehouse using Materialized Queries.- Feature Selection using Ant Colony Optimization and Weighted Visibility Graph.- From Generic to Custom: A Survey on Role of Machine Learning in Pharmacogenomics, Its Applications and Challenges.- Personalized Structure Balance Theory Based Movie Recommendation System.- Machine Learning Techniques for the Investigation of Phishing Websites.
• (source: Nielsen Book Data)

This book presents the proceedings of 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), which aims to bring together researchers, scientists, engineers and practitioners to share new ideas and experiences in the domain of intelligent computing theories with prospective applications to various engineering disciplines. The book is divided into two volumes: Evolution in Computational Intelligence (Volume 1) and Intelligent Data Engineering and Analytics (Volume 2). Covering a broad range of topics in computational intelligence, the book features papers on theoretical as well as practical aspects of areas such as ANN and genetic algorithms, computer interaction, intelligent control optimization, evolutionary computing, intelligent e-learning systems, machine learning, mobile computing, and multi-agent systems. As such, it is a valuable reference resource for postgraduate students in various engineering disciplines.
(source: Nielsen Book Data)

• Stochastic Investigation of Two-unit Redundant System with Provision of Different Repair Schemes.- A Workflow Allocation Strategy under Precedence Constraints for IaaS Cloud Environment.- Artificial Neural Network Analysis for Predicting Spatial Patterns of Urbanization in India.- Satellite Radar Interferometry for DEM Generation using Sentinel-1A Imagery.
• (source: Nielsen Book Data)

This book presents high-quality, peer-reviewed papers from the International Conference on "Innovations in Computational Intelligence and Computer Vision (ICICV 2020), " hosted by Manipal University Jaipur, Rajasthan, India, on January 17-19, 2020. Offering a collection of innovative ideas from researchers, scientists, academics, industry professionals and students, the book covers a variety of topics, such as artificial intelligence and computer vision, image processing and video analysis, applications and services of artificial intelligence and computer vision, interdisciplinary areas combining artificial intelligence and computer vision, and other innovative practices.
(source: Nielsen Book Data)

• Ability Study of Proximity Measure for Big Data Mining Context on Clustering.- Not Yet Decided.- An Approach to Implement MapReduce and aggregation pipeline utilizing NoSql Technologies.- Energy Hole Alleviation in Wireless sensor.
• (source: Nielsen Book Data)

This book offers a collection of high-quality peer-reviewed research papers presented at the Second International Conference on Communication and Computational Technologies (ICCCT 2019), held at Rajasthan Institute of Engineering and Technology, Jaipur, Rajasthan, India, on 30-31 August 2019. In contributions prepared by researchers from academia and industry alike, the book discusses a wide variety of industrial, engineering and scientific applications of emerging techniques.
(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)

• Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm.-Design of Palm Acupuncture Points Indicator.- Low-rank Representation and Locality-constrained Regression for Robust Low-Resolution Face Recognition.- Face Recognition Benchmark with ID Photos.- Scene Relighting using a Single Reference Image through Material Constrained Layer Decomposition.- Applying Adaptive Actor-critic Learning to Human Upper Lime Lifting Motion.- A Demand-based Allocation Mechanism for Virtual Machine.- A Joint Hierarchy Model for Action Recognition Using Kinect.- QoS-Based Medical Program Evolution.- An Improved 3D Surface Reconstruction Method based on Three Wavelength Phase Shift Profilometry.- The Research on the Lung Tumor Imaging based on the Electrical Impedance Tomography.- Combining CNN and MRF for Road Detection.- The Increasing of Discrimination Accuracy of Waxed Apples based on Hyperspectral Imaging Optimized by Spectral Correlation Analysis.- A Diffeomorphic Demons Approach to Statistical Shape Modeling.
• (source: Nielsen Book Data)

This book highlights selected papers presented at the 2nd International Symposium on Artificial Intelligence and Robotics 2017 (ISAIR2017), held in Nakamura Centenary Memorial Hall, Kitakyushu, Japan on November 25-26, 2017. Today, the integration of artificial intelligence and robotic technologies has become a topic of growing interest for both researchers and developers from academic fields and industries worldwide, and artificial intelligence is poised to become the main approach pursued in next-generation robotics research. The rapidly growing number of artificial intelligence algorithms and big data solutions has significantly extended the number of potential applications for robotic technologies. However, it also poses new challenges for the artificial intelligence community. The aim of this symposium is to provide a platform for young researchers to share the latest scientific achievements in this field, which are discussed in these proceedings.
(source: Nielsen Book Data)

• Invited Paper.- Technical Session: Workflows.- Technical Session: Applications in Health.- Technical Session: New paradigms.- Technical Session: Explanations.- Technical Session: Deep learning.- Technical Session: Applications.- Technical Session: Retrieval and Adaptation.- Special Track Challenges & Promises.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 28th International Conference on Case-Based Reasoning Research and Development, ICCBR 2020, held in Salamanca, Spain*, in June 2020. The 20 full papers and 2 short papers presented in this book were carefully reviewed and selected from 64 submissions. The theme of ICCBR 2020, "CBR Across Bridges" was highlighted by several activities. These papers, which are included in the proceedings, address many themes related to the theory and application of case-based reasoning and its future direction. *The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Automated Reasoning and Data Mining
• Axiomatization of Classes of Domain Cases Based on FCA
• 1 Introduction
• 2 Preliminaries
• 2.1 Domain Cases
• 2.2 Formal Contexts of Axiomatizable Classes
• 2.3 Relatively Axiomatizable Classes
• 3 Axiomatization of Classes of Domain Cases
• 3.1 Sentences Permissible for Models
• 3.2 Theories of Classes of Models Having Different Signatures
• 3.3 Axiomatizable Classes and Decidability of Theories
• 4 Conclusion
• References

• The Combined Method of Automated Knowledge Acquisition from Various Sources: The Features of Development and Experimental Research of the Temporal Version
• 1 Introduction
• 2 The Evolution of the Combined Method of Knowledge Acquisition and Means of Its Implementation
• 3 Features of the Organization of Software Experiments Based on the Temporal Version of CMKA
• 3.1 General Organization of the Language Experiment
• 3.2 General Organization of Experiments with Temporal Database
• 3.3 Merging of Knowledge Field's Elements
• 4 Conclusion
• References

• Multi-agent Systems, Intelligent Robots and Behavior Planning
• Multi-agent Path Finding with Kinematic Constraints via Conflict Based Search
• 1 Introduction
• 2 Problem Statement
• 3 Suggested Approach
• 3.1 Conflict Based Search Algorithm
• 3.2 Continuous Conflict Based Search Algorithm
• 3.3 Safe Interval Path Planning with Rotations
• 3.4 Implementation Details
• 4 Experimental Evaluation
• 4.1 CCBS vs CCBS-kc
• 4.2 AA-SIPP(m) vs CCBS-kc
• 4.3 Comparison of Heuristic Functions
• 5 Conclusion
• References

• Map-Merging Algorithms for Visual SLAM: Feasibility Study and Empirical Evaluation
• 1 Introduction
• 2 Related Work
• 2.1 vSLAM Algorithms
• 2.2 Map-Merging Algorithms
• 3 Problem Statement
• 4 Methods and Metrics Overview
• 5 Experimental Evaluation and Results
• 5.1 Setup
• 5.2 Results
• 6 Conclusion
• References
• Can a Robot Be a Moral Agent?
• 1 Introduction
• 2 A Moral Agent
• 3 Phenomena and Mechanisms
• 3.1 Emotions and Needs
• 3.2 Model of the World, "I"
• 3.3 Imitative Behavior
• 3.4 Empathy
• 3.5 Characteristic Properties of Moral Agents
• 4 Conclusion

This book constitutes the proceedings of the 18th Russian Conference on Artificial Intelligence, RCAI 2020, held in Moscow, Russia, in October 2020. The 27 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 140 submissions. The conference deals with a wide range of topics, including data mining and knowledge discovery, text mining, reasoning, decisionmaking, natural language processing, vision, intelligent robotics, multi-agent systems, machine learning, AI in applied systems, and ontology engineering.
(source: Nielsen Book Data)

• Fast Texture Image Retrieval Using Learning Copula Model of Multiple DTCWT.- Joint Generic Learning and Multi-SourceDomain Adaptation on Unsupervised FaceRecognition.- Reset Attack: An Attack Against Homomorphic Encryption-based Privacy-Preserving Deep Learning System.- Intelligent Contract for Power Market Transaction Based on the BlockChain.- Survey on Zero-trust Network Security.- A Novel Consensus Algorithm for Alliance Chain.- A Novel Thematic Network Data Collector Based on Simulated Annealing Hybrid Genetic Algorithm.- The Study of Fog Computing Security Situation Prediction in Industrial Internet.- A novel keypoints-based image registration method with fully convolutional neural network.- Image super-resolution reconstruction with dense residual attention module.- GF-2 image blue roof building extraction method based on object-oriented classification technology.- Optimization and Parallelization of the cosmological N-body Simulation on Many-core Processor.- Parallel optimization of stencil computation base on Sunway TaihuLight.- Chinese News Data Extraction System Based On Readability Algorithm.- Parallel Acceleration Improvement of Physical Quantity Gradient Algorithm Based on CFD.- Using Process Visualization and Early Warning Based on Learning Analytics to Enhance Teaching and Learning.- Research Progress of Knowledge Graph Embedding.- A Robust Framework for High-quality Voice Conversion with Conditional Generative Adversarial Network.- Effective Android Malware Detection based on Deep Learning.- Research on Tampering Detection of Material Gene Data Based on Fragile Watermarking.- Quantitative Evaluation Of Promotion Of Traditional National Sports Events Based On Probity Multiple Regression Analysis.- Research on Leisure Sports Activities Based on Decision Tree Algorithm.- Semantic Integrity Analysis Based on Transformer.- An improvement of Coherent Semantic Attention for Image Inpainting.- Formal Verification of the Correctness of Operating System on Assembly Layer in Isabelle/HoL.- Nonlinear Correction Based on Depth Neural Network.- Spatial-temporal co-attention network for action recognition.- Improved SSD for object detection.- Sentiment Analysis on Weibo Platform for Stock Prediction.- Refinement Measurement and Evaluation of Streets in Shanghai Old City.- A Traceability Architecture for the Fresh Food Supply Chain based on Blockchain Technology in China.- Research Progress in Blind Forensics of Digital Image Smooth Filtering.- Research on video violence detection technology of UAV on cloud platform.- A Generative Steganography Method Based on WGAN-GP.- A deep learning network for coarse-to-fine deformable medical image registration.- Research on Cross-lingual Machine Reading Comprehension Technology Based on Non-parallel Corpus.- Research on the data reference model of NMIS.- A Real-time Recommender System Design Based on Spark Streaming.- A Novel Clone Detection Scheme Based on Generative Adversarial Networks.- Design of Vehicle Remote Monitoring System Based on LoRa Technology and QT Platform.- Unifying Domain Adaptation into Simultaneous Discriminant Analysis: a Method for Low-resolution Face Recognition with Single Sample per Person.- An exploratory study of blending Smart classroom in database course.- Optimization and simulation of pre-warehouse multi-objective stock levels based on the Weibull distribution function.- Research and Analysis of Fine Management of Engineering Cost Based on BIM.- Survey of Software-Defined Network Security Issues.- A Fall Detection Algorithm Based on a Support Vector Machine.- A Multi-AGV Optimal Scheduling Algorithm Based on Particle Swarm Optimization.- An efficient quantum private comparison protocol based on conjugate coding.- Deliberated Vulnerable Environment Generating based on Container and Structured Vulnerability Intelligence.- Deep neural network watermarking based on texture analysis.- A Robust Reversible Image Watermarking Scheme based on Singular Value Decomposition and Quantization Technique.- Boolean Function Decomposition Based on Grover Algorithm and Its Simulation Using Quantum Language Quipper.- Research on Emotional Recognition of EEG Signals in Experimental Paradigm.- Research on Rasterized Population Evaluation Method Based on Multi-class Machine Learning Method.- Salary Satisfaction of University Teachers with Different Professional Positions.- Defining Cost Function for Robust Embedding.- Efficient Identity-Based Signature authentication scheme for smart home system.- A Resource Allocation Mechanism for Edge-blockchain.- Remote sensing image zero-watermarking algorithm based on accurate polar harmonic Fourier moment.- Multi-layer quantum secret sharing based on GHZ states.- Robot scheduling system based on semantic recognition.- Pedestrian Counting without Tracking for the Security Application of IoT Framework.
• (source: Nielsen Book Data)

The 3-volume set CCIS 1252 until CCIS 1254 constitutes the refereed proceedings of the 6th International Conference on Artificial Intelligence and Security, ICAIS 2020, which was held in Hohhot, China, in July 2020. The conference was formerly called "International Conference on Cloud Computing and Security" with the acronym ICCCS. The total of 178 full papers and 8 short papers presented in this 3-volume proceedings was carefully reviewed and selected from 1064 submissions. The papers were organized in topical sections as follows: Part I: artificial intelligence; Part II: artificial intelligence; Internet of things; information security; Part III: information security; big data and cloud computing; information processing.
(source: Nielsen Book Data)

• Invited Talks.- Complementing Behavioural Modeling with Cognitive Modeling for Better Recommendations.- Nominees for Best Paper Award.- The Construction of Action Rules to Raise Artwork Prices.- Metric-Guided Multi-Task Learning.- Sentiment Analysis with Contextual Embeddings and Self-Attention.- Nominees for Best Student Paper Award.- Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings.- Decision-Making with Probabilistic Reasoning in Engineering Design.- Hyperbolic embeddings for hierarchical multi-label classification.- Natural Language Processing.- Joint Multiclass Debiasing of Word Embeddings.- Recursive Neural Text Classification using Discourse Tree Structure for Argumentation Mining and Sentiment Analysis Tasks.- Named Entity Recommendations to Enhance Multilingual Retrieval in Europeana.eu.- A Deep Learning Approach to Fake News Detection.- Satirical News Detection with Semantic Feature Extraction and Game-theoretic Rough Sets.- Deep Learning and Embeddings.- Comparing state-of-the-art neural network ensemble methods in soccer predictions.- Static Music Emotion Recognition Using Recurrent Neural Networks.- Saliency detection in hyperspectral images using autoencoder-based data reconstruction.- Mesoscale Anisotropically-Connected Learning.- Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering.- Neural spike sorting using unsupervised adversarial learning.- Digital Signal Processing.- Poriferal Vision: Classifying benthic sponge spicules to assess historical impacts of marine climate change.- Experimental Evaluation of GAN-Based One-Class Anomaly Detection on Office Monitoring.- Ranking Speech Features for their Usage in Singing Classification.- Leveraging Machine Learning in IoT to predict the trustworthiness of Mobile Crowd Sensing data.- A Hierarchical-Based Web-Platform for Crowdsourcing Distinguishable Image Patches.- Performing Arithmetic Using a Neural Network Trained on Digit Permutation Pairs.- Modelling and Reasoning.- CatIO - Framework for Model Based Diagnosis of Cyber-Physical Systems.- Data Publishing: Availibility of data under Security Policies - Preliminary work.- Matrix Factorization based Heuristics Learning for Solving Constraint Satisfaction Problems.- Explaining object motion using answer set programming.- The GraphBRAIN System for Knowledge Graph Management and Advanced Fruition.- Mining Exceptional Mediation Models.- Machine Learning Applications.- Multivariate predictive clustering trees for classification.- Comparison of Machine Learning Methods to Detect Anomalies in the Activity of Dairy Cows.- Clustering Algorithm Consistency in Fixed Dimensional Spaces.- Estimating the Importance of Relational Features by using Gradient Boosting.- Multi-objective Discrete Moth-Flame Optimization for Complex Network Clustering.- Predicting associations between proteins and multiple diseases.- Short Papers.- Exploiting Answer Set Programming for Building Explainable Recommendations.- Tailoring Random Forest for Requirements Classification.- On the Design of a Natural Logic System for Knowledge Bases.- Evaluation of post-hoc XAI approaches through synthetic tabular data.- SimLoss: Class Similarities in Cross Entropy.- Efficient and Precise Classification of CT Scannings of Renal Tumors using Convolutional Neural Networks.- Deep Autoencoder Ensembles for Anomaly Detection on Blockchain.- A Parallelized Variant of Junker's QUICKXPLAIN Algorithm.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 25th International Symposium on Foundations of Intelligent Systems, ISMIS 2020, held in Graz, Austria, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 35 full and 8 short papers presented in this volume were carefully reviewed and selected from 79 submissions. Included is also one invited talk. The papers deal with topics such as natural language processing; deep learning and embeddings; digital signal processing; modelling and reasoning; and machine learning applications.
(source: Nielsen Book Data)

• FOUNDATIONS COMPUTATIONAL INTELLIGENCE EVOLUTIONARY COMPUTATION CONCEPTS AND PARADIGMS EVOLUTIONARY COMPUTATION IMPLEMENTATIONS NEURAL NETWORK CONCEPTS AND PARADIGMS NEURAL NETWORK IMPLEMENTATIONS FUZZY SYSTEMS CONCEPTS AND PARADIGMS FUZZY SYSTEMS IMPLEMENTATIONS COMPUTATIONAL INTELLIGENCE IMPLEMENTATIONS PERFORMANCE METRICS ANALYSIS AND EXPLANATION CASE STUDY SUMMARIES.
• (source: Nielsen Book Data)

Computational Intelligence: Concepts to Implementations provides the most complete and practical coverage of computational intelligence tools and techniques to date. This book integrates various natural and engineering disciplines to establish Computational Intelligence. This is the first comprehensive textbook on the subject, supported with lots of practical examples. It asserts that computational intelligence rests on a foundation of evolutionary computation. This refreshing view has set the book apart from other books on computational intelligence. This book lays emphasis on practical applications and computational tools, which are very useful and important for further development of the computational intelligence field. Focusing on evolutionary computation, neural networks, and fuzzy logic, the authors have constructed an approach to thinking about and working with computational intelligence that has, in their extensive experience, proved highly effective. The book moves clearly and efficiently from concepts and paradigms to algorithms and implementation techniques by focusing, in the early chapters, on the specific con. It explores a number of key themes, including self-organization, complex adaptive systems, and emergent computation. It details the metrics and analytical tools needed to assess the performance of computational intelligence tools. The book concludes with a series of case studies that illustrate a wide range of successful applications. This book will appeal to professional and academic researchers in computational intelligence applications, tool development, and systems.
(source: Nielsen Book Data)

• Introduction.- Part I: The Components.- Computers.- People.- Thought.- Part II: The Synergies.- Computers and People.- People and Thought.- Computers and Thought.- Computers, People, and Thought.- Part III: The Wider Picture.- Ethical, Societal, Philosophical, and Spiritual Issues.- Jobs and Education.- Superintelligence.- Lethal Autonomous Weapons Systems.- The Right to Privacy as a Cornerstone of Human Individuality and Freedom?.- The Future of Society: Choices, Decisions, and Consequences.- App. A: What Is the Most Efficient Number Base?.- App. B: Ternary State Machine Design.- References.
• (source: Nielsen Book Data)

In this book the author discusses synergies between computers and thought, related to the field of Artificial Intelligence; between people and thought, leading to questions of consciousness and our existence as humans; and between computers and people, leading to the recent remarkable advances in the field of humanoid robots. He then looks toward the implications of intelligent 'conscious' humanoid robots with superior intellects, able to operate in our human environments. After presenting the basic engineering components and supporting logic of computer systems, and giving an overview of the contributions of pioneering scientists in the domains of computing, logic, and robotics, in the core of the book the author examines the meaning of thought and intelligence in the context of specific tasks and successful AI approaches. In the final part of the book he introduces related societal and ethical implications. The book will be a useful accompanying text in courses on artificial intelligence, robotics, intelligent systems, games, and evolutionary computing. It will also be valuable for general readers and historians of technology.
(source: Nielsen Book Data)

• Chapter 1: An Overview of Artificial Intelligence
• Chapter 2: An Overview of Machine Learning
• Chapter 3: Introduction to Deep Learning
• Chapter 4: Machine Learning Versus Deep Learning
• Chapter 5: Machine Learning with Python
• Chapter 6: Introduction to Jupyter Notebooks
• Chapter 7: Python Programming on the Jupyter Notebook
• Chapter 8: The Tensorflow Machine Learning Library
• Chapter 9: Programming with Tensorflow 1.0
• Chapter 10: Introducing TensorFlow 2.0
• Chapter 11: Machine Learning Programming with TensorFlow 2.0.

Create, execute, modify, and share machine learning applications with Python in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebooks instead of a text editor or a regular IDE. Youll start by learning fundamental concepts in Python necessary for working with machine learning application development. Then use Jupyter Notebooks to improve the way you program with Python. After grounding your skills in working with Python in Jupyter Notebooks, youll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Notebooks allow you to apply concepts from earlier in the book. Those who are new to machine learning can start in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier. You will: Program machine learning models in Python Tackle basic machine learning obstacles Develop in the Jupyter Notebooks environment.

• Chapter 1
• Welcome to TensorFlow.js Headings
• What is TensorFlow.js?
• TensorFlow.js API
• Tensors Operations Variables
• How to install it
• Use cases Chapter 2 Building your First Model Headings
• Building a logistic regression classification model
• Building a linear regression model
• Doing unsupervised learning with k-means
• Dimensionality reduction and visualization with t-SNE and d3.js
• Our first neural network Chapter 3 Create a drawing app to predict handwritten digits using Convolutional Neural Networks and MNIST Headings
• Convolutional Neural Networks
• The MNIST Dataset
• Design the model architecture
• Train the model
• Evaluate the model
• Build the drawing app
• Integrate the model within the app
• Chapter 4 "Move your body!" A game featuring PoseNet, a pose estimator model Headings
• What is PoseNet?
• Loading the model
• Interpreting the result
• Building a game around it Chapter 5 Detect yourself in real-time using an object detection model trained in Google Cloud's AutoML Headings
• TensorFlow Object Detection API
• Google Cloud's AutoML
• Training the model
• Exporting the model and importing it in TensorFlow.js
• Building the webcam app Chapter 6 Transfer Learning with Image Classifier and Voice Recognition Headings
• What's Transfer Learning?
• MobileNet and ImageNet (MobileNet is the base model and ImageNet is the training set)
• Transferring the knowledge
• Re-training the model
• Testing the model with a video Chapter 7 Censor food you do not like with pix2pix, Generative Adversarial Networks, and ml5.js Headings
• Introduction to Generative Adversarial Networks
• What is image translation?
• Training your custom image translator with pix2pix
• Deploying the model with ml5.js Chapter 8 Detect toxic words from a Chrome Extension using a Universal Sentence Encoder Headings
• Toxicity classifier
• Training the model
• Testing the model
• Integrating the model in a Chrome Extension Chapter 9 Time Series Analysis and Text Generation with Recurrent Neural Networks Headings
• Recurrent Neural Networks
• Example 1: Building an RNN for time series analysis
• Example 2: Building an RNN to generate text Chapter 10 Best practices, integrations with other platforms, remarks and final words Headings
• Best practices
• Integration with other platforms
• Materials for further practice
• Conclusion.
• (source: Nielsen Book Data)

Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow. js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard , ml5js , tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow. js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis. Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. What You'll Learn Build deep learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN) Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis Who This Book Is For Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.
(source: Nielsen Book Data)

• Decision Support Model for Assessing Projects by a Group of Investors with Regards of Multi-factors
• 7-Dimensional Optimization Task: PBO-Nature-Inspired Optimizer Versus 10-Years-Old Differential Evolution Based Optimizer 3rd Generation EPSDE
• A Hand Contour Classification Using Ensemble of Natural Features: A Large Comparative Study
• Energy Consumption Reduction in Real Time Multiprocessor Embedded Systems with Uncertain Data
• Developing an Efficient Method for Automatic Threshold Detection Based on Hybrid Feature Selection Approach
• Comparison of Hybrid ACO-k-Means Algorithm and Graph Cut for MRI Images Segmentation
• Parallel Deep Neural Network for Motor Imagery EEG Recognition with Spatiotemporal Features.

This book gathers the refereed proceedings of the Artificial Intelligence and Bioinspired Computational Methods Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Artificial intelligence and bioinspired computational methods now represent crucial areas of computer science research. The topics presented here reflect the current discussion on cutting-edge hybrid and bioinspired algorithms and their applications. .
(source: Nielsen Book Data)

• 1: Introduction
• 2: Fundamentals of Edge Computing
• 2.1 Paradigms of Edge Computing
• Cloudlet and Micro Data Centers
• Fog Computing
• Mobile (Multi-access) Edge Computing (MEC)
• Definition of Edge Computing Terminologies
• Collaborative End-Edge-Cloud Computing
• 2.2 Hardware for Edge Computing
• AI Hardware for Edge Computing
• Integrated Commodities Potentially for Edge Nodes
• Edge Computing Frameworks
• 2.3 Virtualizing the Edge
• Virtualization Techniques
• Network Virtualization
• Network Slicing
• 3. Fundamentals of Deep Learning
• 3.1 Neural Networks in Deep Learning
• Fully Connected Neural Network (FCNN)
• Auto-Encoder (AE)
• Convolutional Neural Network (CNN)
• Generative Adversarial Network (GAN)
• Recurrent Neural Network (RNN)
• Transfer Learning (TL)
• 3.2 Deep Reinforcement Learning (DRL)
• Value-based DRL
• Policy-gradient-based DRL
• 3.3 Distributed DL Training
• 3.4 Potential DL Libraries for Edge
• 4. Deep Learning Applications on Edge
• 4.1 Real-time Video Analytic
• 4.2 Autonomous Internet of Vehicles (IoVs)
• 4.3 Intelligent Manufacturing
• 4.4 Smart Home and City
• 5. Deep Learning Inference in Edge
• 5.1 Optimization of DL Models in Edge
• General Methods for Model Optimization
• Model Optimization for Edge Devices
• 5.2 Segmentation of DL Models
• 5.3 Early Exit of Inference (EEoI)
• 5.4 Sharing of DL Computation
• 6. Edge Computing for Deep Learning
• 6.1 Edge Hardware for DL
• Mobile CPUs and GPUs
• FPGA-based Solutions
• 6.2 Communication and Computation Modes for Edge DL
• Integral Offloading
• Partial Offloading
• Vertical Collaboration
• Horizontal Collaboration
• 6.3 Tailoring Edge Frameworks for DL
• 6.4 Performance Evaluation for Edge DL
• 7. Deep Learning Training at Edge
• 7.1 Distributed Training at Edge
• 7.2 Vanilla Federated Learning at Edge
• 7.3 Communication-efficient FL
• 7.4 Resource-optimized FL
• 7.5 Security-enhanced FL
• 8. Deep Learning for Optimizing Edge
• 8.1 DL for Adaptive Edge Caching
• 8.2 DL for Optimizing Edge Task Offloading
• 8.3 DL for Edge Management and Maintenance
• Edge Communication
• Edge Security
• Joint Edge Optimization
• 9. Lessons Learned and Open Challenges
• 9.1 More Promising Applications
• 9.2 General DL Model for Inference
• 9.3 Complete Edge Architecture for DL
• 9.4 Practical Training Principles at Edge
• 9.5 Deployment and Improvement of Intelligent Edge
• .
• (source: Nielsen Book Data)

As an important enabler for changing people's lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
(source: Nielsen Book Data)

• A Prescriptive Analytical Logic Model Incorporated with Analytic Hierarchy Process for Software Application Error Analysis
• Simplified Framework of Natural Language Processing for Structure Management of Current-Age Data
• Design and Software Implementation of Heuristic and Suboptimal Strategies for the Mancala/Kalah Game
• Sector-Selective Hybrid Scheme Facilitating Hardware Supportability Over Image Compression
• Performance Evaluation of Joint Rate-Distortion Model of Video Codec
• To Defeat DDoS Attacks in Cloud Computing Environment Using Software Defined Networking (SDN)
• Oriented Petri Nets as Means of Describing a Human-Computer Interaction
• FPGA Based Transient Fault Generate and Fault Tolerant for Asynchronous and Synchronous Circuits
• Designing an Energy Efficient Routing for Subsystems Sensors in Internet of Things Eco-System Using Distributed Approach
• The Compare of Solo Programming and Pair Programming Strategies in a Scrum Team: A Multi-agent Simulation
• Fair Random Access with Track-Based SNR Scheduling for Full-Duplex Wireless Powered Communication Networks
• A DTN Gateway-Based Architecture for Web Access in Space Internet
• Multi-objective Scheduling Optimization for ETL Tasks in Cluster Environment
• An LSTM-Based Encoder-Decoder Model for State-of-Charge Estimation of Lithium-Ion Batteries
• Analysis of Social Engineering Attacks Using Exploit Kits
• Bipolar Disorder: A Pathway Towards Research Progress in Identification and Classification
• Managing Electronic Waste with Recycling: A Review of Developing and Developed Regions
• Trajectory Modeling in a Pursuit Problem with Curvature Restrictions
• An Approach to Using Templates for Modeling Exceptions in Terms of Petri Nets
• Performance Bottleneck Analysis and Optimization Framework in Concurrent Environments
• Matlab Code Generation and Consumption
• The Problem of Preventing the Development of Critical Combinations of Events in Large-Scale Systems
• Using Software Package "Multimeat-Expert" for Modeling and Optimization of Composition Chopped Meat Product with Vegetable Additive
• Application of Machine Learning for Document Classification and Processing in Adaptive Information Systems
• SNR-Based TDMA Scheduling with Continuous Energy Transfer for Wireless Powered Communication Networks
• Comparative Analysis of Software Development Life Cycle Models (SDLC). Plus 27 more papers.

This book gathers the refereed proceedings of the Intelligent Algorithms in Software Engineering Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Software engineering research and its applications to intelligent algorithms have now assumed an essential role in computer science research. In this book, modern research methods, together with applications of machine and statistical learning in software engineering research, are presented. .
(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)

• Aggregation Operators and Decision Making.- Data science and Data Mining.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 17th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2020, held in Sant Cugat, Spain, in September 2020.* The 24 papers presented in this volume were carefully reviewed and selected from 46 submissions. They discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: aggregation operators and decision making, and data science and data mining. * The conference was canceled due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Knowledge Bases.- Conceptual Structures.- Reasoning Models.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 25th International Conference on Conceptual Structures, ICCS 2020, held in Bolzano, Italy, in September 2020.* The 10 full papers, 5 short papers and 1 poster paper presented were carefully reviewed and selected from 24 submissions. The proceedings also include two keynotes and two tutorials. The papers focus on the representation of and reasoning with conceptual structures in a variety of contexts. The topics of this year's conference range from formal concept analysis to decision making, from machine learning to natural language processing. *The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Cover
• FM
• Copyright
• Table of Contents
• Preface
• Chapter 1: Introduction to Reinforcement Learning
• Introduction
• Learning Paradigms
• Introduction to Learning Paradigms
• Supervised versus Unsupervised versus RL
• Classifying Common Problems into Learning Scenarios
• Predicting Whether an Image Contains a Dog or a Cat
• Detecting and Classifying All Dogs and Cats in an Image
• Playing Chess
• Fundamentals of Reinforcement Learning
• Elements of RL
• Agent
• Actions
• Environment
• Policy
• An Example of an Autonomous Driving Environment

• Exercise 1.01: Implementing a Toy Environment Using Python
• The Agent-Environment Interface
• What's the Agent? What's in the Environment?
• Environment Types
• Finite versus Continuous
• Deterministic versus Stochastic
• Fully Observable versus Partially Observable
• POMDP versus MDP
• Single Agents versus Multiple Agents
• An Action and Its Types
• Policy
• Stochastic Policies
• Policy Parameterizations
• Exercise 1.02: Implementing a Linear Policy
• Goals and Rewards
• Why Discount?
• Reinforcement Learning Frameworks
• OpenAI Gym
• Getting Started with Gym
• CartPole

• Gym Spaces
• Exercise 1.03: Creating a Space for Image Observations
• Rendering an Environment
• Rendering CartPole
• A Reinforcement Learning Loop with Gym
• Exercise 1.04: Implementing the Reinforcement Learning Loop with Gym
• Activity 1.01: Measuring the Performance of a Random Agent
• OpenAI Baselines
• Getting Started with Baselines
• DQN on CartPole
• Applications of Reinforcement Learning
• Games
• Go
• Dota 2
• StarCraft
• Robot Control
• Autonomous Driving
• Summary
• Chapter 2: Markov Decision Processes and Bellman Equations
• Introduction
• Markov Processes

• The Markov Property
• Markov Chains
• Markov Reward Processes
• Value Functions and Bellman Equations for MRPs
• Solving Linear Systems of an Equation Using SciPy
• Exercise 2.01: Finding the Value Function in an MRP
• Markov Decision Processes
• The State-Value Function and the Action-Value Function
• Bellman Optimality Equation
• Solving the Bellman Optimality Equation
• Solving MDPs
• Algorithm Categorization
• Value-Based Algorithms
• Policy Search Algorithms
• Linear Programming
• Exercise 2.02: Determining the Best Policy for an MDP Using Linear Programming
• Gridworld

• Activity 2.01: Solving Gridworld
• Summary
• Chapter 3: Deep Learning in Practice with TensorFlow 2
• Introduction
• An Introduction to TensorFlow and Keras
• TensorFlow
• Keras
• Exercise 3.01: Building a Sequential Model with the Keras High-Level API
• How to Implement a Neural Network Using TensorFlow
• Model Creation
• Model Training
• Loss Function Definition
• Optimizer Choice
• Learning Rate Scheduling
• Feature Normalization
• Model Validation
• Performance Metrics
• Model Improvement
• Overfitting
• Regularization
• Early Stopping
• Dropout
• Data Augmentation

With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning's core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease.

• AGI and the Knight-Darwin Law: why idealized AGI reproduction requires collaboration.- Error-Correction for AI Safety.- Artificial Creativity Augmentation.- The hierarchical memory based on compartmental spiking neuron model.- The Dynamics of Growing Symbols: A Ludics Approach to Language Design by Autonomous Agents.- Approach for development of engineering tools based on knowledge graphs and context separation.- Towards Dynamic Process Composition in the DSO Cognitive Architecture.- SAGE: Task-Environment Platform for Evaluating a Broad Range of AI Learners.- Post-Turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence.- Self-explaining AI as an alternative to interpretable AI.- AGI needs the Humanities.- A report of a recent book: "AI and Human Thought and Emotion".- Cognitive Machinery and Behaviours.- Combinatorial Decision Dags: A Natural Computational Model for General Intelligence.- What Kind of Programming Language Best Suits Integrative AGI?.- Guiding Symbolic Natural Language Grammar Induction via Transformer-Based Sequence Probabilities.- Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences.- Delta Schema Network in Model-based Reinforcement Learning.- Information Digital Twin{Enabling Agents to Anticipate Changes in their Tasks.- 'OpenNARS for Applications': Architecture and Control.- Towards AGI Agent Safety by Iteratively Improving the Utility Function.- Learning to Model Another Agent's Beliefs: A Preliminary Approach.- An Attentional Control Mechanism for Reasoning and Learning.- Hyperdimensional Representations in Semiotic Approach to AGI.- The Conditions of Artificial General Intelligence: Logic, Autonomy, Resilience, Integrity, Morality, Emotion, Embodiment, and Embeddedness.- Position paper: The use of engineering approach in creation of artificial general intelligence.- How do you test the strength of AI?.- Omega: An Architecture for AI Unification.- Analyzing Elementary School Olympiad Math Tasks as a Benchmark for AGI.- The meaning of things as a concept in a strong AI architecture.- Toward a General Believable Model of Human-Analogous Intelligent Socially Emotional Behavior.- Autonomous Cumulative Transfer Learning.- New Brain Simulator II Open-Source Software.- Experience-specific AGI Paradigms.- Psychological portrait of a virtual agent in the Teleport game paradigm.- Logical probabilistic biologically inspired cognitive architecture.- An Architecture for Real-time Reasoning and Learning.- A Model for Artificial General Intelligence.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 13th International Conference on Artificial General Intelligence, AGI 2020, held in St. Petersburg, Russia, in September 2020.The 30 full papers and 8 short papers presented in this book were carefully reviewed and selected from 60 submissions. The papers cover topics such as AGI architectures, artificial creativity and AI safety, transfer learning, AI unification and benchmarks for AGI.
(source: Nielsen Book Data)

• Towards an Assistive Social Robot Interacting with Human Patient to Establish a Mutual and Effective Support.- Selecting for Positive Responses to Knock outs in Boolean Networks.- Avalanches of Perturbations in Modular Gene Regulatory Networks.- The Effects of a Simplified Model of Chromatin Dynamics on Attractors Robustness in Random Boolean Networks with Self-loops: an Experimental Study.- A Memetic Approach for the Orienteering Problem.- The Detection of Dynamical Organization in Cancer Evolution Models.- The Simulation of Noise Impact on the Dynamics of a Discrete Chaotic Map.- Exploiting Distributed Discrete-Event Simulation Techniques for Parallel Execution of Cellular Automata.- A Relevance Index-based Method for Improved Detection of Malicious Users in Social Networks.- An Analysis of Cooperative Coevolutionary Differential Evolution as Neural Networks Optimizer.- Design and Evaluation of a Heuristic Optimization Tool Based on Evolutionary Grammars Using PSoCs.- How Word Choice Affects Cognitive Impairment Detection by Handwriting Analysis: a Preliminary Study.- Modeling the Coordination of a Multiple Robots Using Nature Inspired Approaches.- Nestedness Temperature in the Agent-Artifact Space: Emergence of Hierarchical Order in the 2000-2014 Photonics Techno-Economic Complex System.- Towards Programmable Chemistries.- Studying and Simulating the Three-dimensional Arrangement of Droplets.- Investigating Three-dimensional Arrangements of Droplets.
• (source: Nielsen Book Data)

This book constitutes the revised selected papers of the 14th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2019, held in Rende, Italy, in September 2019. The 13 full papers and 4 short paper presented were thoroughly reviewed and selected from 31 submissions. They are focused on the topics of information systems, design and analysis of algorithms, artificial intelligence, machine learning, cognitive science, modeling and simulation, collaborative and social computing, parallel computing, distributed computing. The chapter "Nestedness Temperature in the Agent-Artifact Space: Emergence of Hierarchical Order in the 2000-2014 Photonics Techno-Economic Complex System" is available open access under a CC BY 4.0 license.
(source: Nielsen Book Data)

• Part 1: Fundamentals
• Chapter 1: Introduction to Deep Learning
• Chapter 2: Introduction to Reinforcement Learning
• Chapter 3: Taxonomy of Reinforcement Learning Algorithms
• Chapter 4: Deep Q-Networks
• Chapter 5: Policy Gradient
• Chapter 6: Combine Deep Q-Networks with Actor-Critic
• Part II: Research
• Chapter 7: Challenges of Reinforcement Learning
• Chapter 8: Imitation Learning
• Chapter 9: Integrating Learning and Planning
• Chapter 10: Hierarchical Reinforcement Learning
• Chapter 11: Multi-Agent Reinforcement Learning
• Chapter 12: Parallel Computing
• Part III: Applications
• Chapter 13: Learning to Run
• Chapter 14: Robust Image Enhancement
• Chapter 15: AlphaZero
• Chapter 16: Robot Learning in Simulation
• Chapter 17: Arena Platform for Multi-Agent Reinforcement Learning
• Chapter 18: Tricks of Implementation
• Part IV: Summary
• Chapter 19: Algorithm Table
• Chapter 20: Algorithm Cheatsheet.

Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine, and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids and finance. Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations. The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. It also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.
(source: Nielsen Book Data)

• Preface.- Part I: Introduction.-
• Chapter 1: Introduction to Domain Adaptation.-
• Chapter 2: Shallow Domain Adaptation.- Part II: Domain Alignment in the Feature Space.-
• Chapter 3: d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding.-
• Chapter 4: Deep Hashing Network for Unsupervised Domain Adaptation.-
• Chapter 5: Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation.- Part III: Domain Alignment in the Image Space.-
• Chapter 6: Unsupervised Domain Adaptation with Duplex Generative Adversarial Network.-
• Chapter 7: Domain Adaptation via Image to Image Translation.-
• Chapter 8: Domain Adaptation via Image Style Transfer.- Part IV: Future Directions in Domain Adaptation.-
• Chapter 9: Towards Scalable Image Classifier Learning with Noisy Labels via Domain Adaptation.-
• Chapter 10: Adversarial Learning Approach for Open Set Domain Adaptation.-
• Chapter 11: Universal Domain Adaptation.-
• Chapter 12: Multi-source Domain Adaptation by Deep CockTail Networks.-
• Chapter 13: Zero-Shot Task Transfer.
• (source: Nielsen Book Data)

This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation. Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation. This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.
(source: Nielsen Book Data)

• Introduction.- Definition, motivation and impact of digitalization in energy transition.- Definition of a general scheme (layers) of a digitalized system in energy transition.- Challenges of digitalization in energy transition.- Artificial Intelligence for energy transition.- General principals and classification of Artificial Intelligence techniques for energy transition.- Artificial Intelligence for Smart Energy Management.- Smart energy management (intrusive and non-intrusive load monitoring).- Artificial Intelligence for Cyber Security and Privacy.- Artificial Intelligence for Mobility and Electrical Vehicles.- Mobility and electrical vehicles.- Artificial Intelligence for Micro Grid Operations and Dynamic Pricing Revenue Management.- Micro Grid operations and Dynamic Pricing Revenue Management.- Artificial Intelligence for Renewable Energy Penetration and Demand Side Management.- Renewable Energy Penetration and Demand Side Management.- Emerging Trends, Open problems, and Future Challenges.- Conclusion.
• (source: Nielsen Book Data)

This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.).
(source: Nielsen Book Data)

• Ontologies and Meta Modeling.- Cyber Physical Systems and Block-Chains.- Recommender Systems.- Machine Learning based Applications.- Combinatorial Optimization.- Simulations and Deep Learning.- Workshop Session.
• (source: Nielsen Book Data)

This volume constitutes refereed proceedings of the Third International Conference on Smart Applications and Data Analysis, SADASC 2020, held in Marrakesh, Morocco. Due to the COVID-19 pandemic the conference has been postponed to June 2020. The 24 full papers and 3 short papers presented were thoroughly reviewed and selected from 44 submissions. The papers are organized according to the following topics: ontologies and meta modeling; cyber physical systems and block-chains; recommender systems; machine learning based applications; combinatorial optimization; simulations and deep learning. .
(source: Nielsen Book Data)

• Data Centric Programming.- Next Generation Computing.
• (source: Nielsen Book Data)

This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics. .
(source: Nielsen Book Data)

• Introduction to support vector learning
• roadmap. Part 1 Theory: three remarks on the support vector method of function estimation, Vladimir Vapnik
• generalization performance of support vector machines and other pattern classifiers, Peter Bartlett and John Shawe-Taylor
• Bayesian voting schemes and large margin classifiers, Nello Cristianini and John Shawe-Taylor
• support vector machines, reproducing kernel Hilbert spaces, and randomized GACV, Grace Wahba
• geometry and invariance in kernel based methods, Christopher J.C. Burges
• on the annealed VC entropy for margin classifiers - a statistical mechanics study, Manfred Opper
• entropy numbers, operators and support vector kernels, Robert C. Williamson et al. Part 2 Implementations: solving the quadratic programming problem arising in support vector classification, Linda Kaufman
• making large-scale support vector machine learning practical, Thorsten Joachims
• fast training of support vector machines using sequential minimal optimization, John C. Platt. Part 3 Applications: support vector machines for dynamic reconstruction of a chaotic system, Davide Mattera and Simon Haykin
• using support vector machines for time series prediction, Klaus-Robert Muller et al
• pairwise classification and support vector machines, Ulrich Kressel. Part 4 Extensions of the algorithm: reducing the run-time complexity in support vector machines, Edgar E. Osuna and Federico Girosi
• support vector regression with ANOVA decomposition kernels, Mark O. Stitson et al
• support vector density estimation, Jason Weston et al
• combining support vector and mathematical programming methods for classification, Bernhard Scholkopf et al.
• (source: Nielsen Book Data)

The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.ContributorsPeter Bartlett, Kristin P. Bennett, Christopher J.C. Burges, Nello Cristianini, Alex Gammerman, Federico Girosi, Simon Haykin, Thorsten Joachims, Linda Kaufman, Jens Kohlmorgen, Ulrich Kressel, Davide Mattera, Klaus-Robert Muller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Ratsch, Bernhard Schoelkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson.
(source: Nielsen Book Data)
The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.
(source: Nielsen Book Data)

• Knowledge Discovery and Data Mining.- Pattern Recognition and Signal Processing.- Intelligent Image Processing and Artificial Vision.- Ad-hoc and Wireless.- Sensor Networks.- Business Intelligence and Big Data Analytics.
• (source: Nielsen Book Data)

This book explores and discusses various aspects of intelligent systems technologies and their applications. Presenting the refereed post-conference proceedings of the 5th International Symposium on Intelligent Systems Technologies and Applications (ISTA 2019), held at the Indian Institute of Information Technology and Management-Kerala (IIITM-K), Trivandrum, India, on December 18-21, 2019, it covers a variety of topics, such as knowledge discovery, data mining, pattern recognition, signal processing, intelligent image processing, artificial vision, ad hoc and wireless sensor networks, business intelligence and big data analytics.
(source: Nielsen Book Data)

• State of the Art: A Review on Vehicular Communications, Impact of 5G, Fractal Antennas for Future Communication
• 1 Energy Enhancement of TORA and DYMO by Optimization of Hello Messaging Using BFO for MANETs
• Horseshoe-Shaped Multiband Antenna for Wireless Application
• A Review Paper on Performance Analysis of IEEE 802.11e
• Voice-Controlled IoT Devices Framework for Smart Home
• Comprehensive Analysis of Social-Based Opportunistic Routing Protocol: A Study
• An Efficient Delay-Based Load Balancing using AOMDV in MANET
• Metaheuristic-Based Intelligent Solutions Searching Algorithms of Ant Colony Optimization and Backpropagation in Neural Networks
• Evaluating Cohesion Score with Email Clustering. Plus 56 other papers.

• Part I: Communication and Network Technologies
• Part II: Advanced Computing Technologies and Latest Electrical and Electronics Trends
• Part III: Data Analytics and Intelligent Learning
• Part IV: Security and Privacy Issues.

This book features selected research papers presented at the First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019), organized by Northwest Group of Institutions, Punjab, India, Southern Federal University, Russia, and IAC Educational Trust, India along with KEC, Ghaziabad and ITS, College Ghaziabad as an academic partner and held on 12-13 October 2019. It includes innovative work from researchers, leading innovators and professionals in the area of communication and network technologies, advanced computing technologies, data analytics and intelligent learning, the latest electrical and electronics trends, and security and privacy issues.
(source: Nielsen Book Data)

• Preface
• 1. The Machine That Could Learn Anything
• 2. Consider Thy Neighbor
• 3. Kepler on Mars
• 4. The Information Chicane
• 5. Fence-and-Fill Learning
• 6. Turing and the Submarines
• 7. The Relational Gulf
• 8. The Supercharged Learner
• 9. David Hume and the Crash of '87
• 10. Phases of Compression
• 11. Protorepresentational Learning
• 12. The Creativity Continuum
• References
• Index.

Chris Thornton makes the compelling claim that learning is not a passive discovery operation but an active process involving creativity on the part of the learner. This study of learning in autonomous agents offers a bracing intellectual adventure. Chris Thornton makes the compelling claim that learning is not a passive discovery operation but an active process involving creativity on the part of the learner. Although theorists of machine learning tell us that all learning methods contribute some form of bias and thus involve a degree of creativity, Thornton carries the idea much further. He describes an incremental process, recursive relational learning, in which the results of one learning step serve as the basis for the next. Very high-level recodings are then substantially the creative artifacts of the learner's own processing. Lower-level recodings are more "objective" in that their properties are more severely constrained by the source data. Thornton sees consciousness as a process at the outer fringe of relational learning, just prior to the onset of creativity. According to this view, we cannot assume consciousness to be an exclusively human phenomenon, but rather the expected feature of any cognitive mechanism able to engage in extended flights of relational learning. Thornton presents key background material in an entertaining manner, using extensive mental imagery and a minimum of mathematics. Anecdotes and dialogue add to the text's informality.
(source: Nielsen Book Data)

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior-thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI, " and "behavior-based AI." This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.
(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)

• Preface; Acknowledgements; 1. Introduction; 2. Statistical physics and phase transitions; 3. The satisfiability problem; 4. Constraint satisfaction problems; 5. Machine learning; 6. Searching the hypothesis space; 7. Statistical physics and machine learning; 8. Learning, SAT, and CSP; 9. Phase transition in FOL covering test; 10. Phase transitions and relational learning; 11. Phase transitions in grammatical inference; 12. Relationships with complex systems; 13. Phase transitions in natural systems; 14. Discussions and open issues; Appendix A. Phase transitions detected in two real cases; Appendix B. An intriguing idea; References; Index.

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Machine learning and statistics overview
• 3. Performance measures I
• 4. Performance measures II
• 5. Error estimation
• 6. Statistical significance testing
• 7. Data sets and experimental framework
• 8. Recent developments
• 9. Conclusion
• Appendix A: statistical tables
• Appendix B: additional information on the data
• Appendix C: two case studies.
• (source: Nielsen Book Data)

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA, facilitating better practical insight as well as implementation. Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.
(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)

• Hybrid Strategies Towards Safe "Self-Aware" Super-intelligent Systems.- Request Confirmation Networks in MicroPsi 2.- Task Analysis for Teaching Cumulative Learners.- Associative Memory: A Spiking Neural Network Robotic Implementation.- A Comprehensive Ethical Framework for AI Entities: Foundations.- Partial Operator Induction with Beta Distributions.- Solving Tree Problems with Category Theory.- Goal-directed Procedure Learning.- Can Machines Design? An Artificial General Intelligence Approach.- Resource-constrained Social Evidence Based Cognitive Model for Empathy-driven Artificial Intelligence.- Unsupervised Language Learning in OpenCog.- Functionalist Emotion Model in NARS.- Towards a Sociological Conception of Artificial Intelligence.- Efficient Concept Formation in Large State Spaces.- DSO Cognitive Architecture: Implementation and Validation of the Global Workspace Enhancement.- The Foundations of Deep Learning with a Path Towards General Intelligence.- Zeta Distribution and Transfer Learning Problem.- Vision System for AGI: Problems and Directions.- Semantic Image Retrieval by Uniting Deep Neural Networks and Cognitive Architectures.- The Temporal Singularity: Time-accelerated Simulated Civilizations and Their Implications.- A Computational Theory for Life-Long Learning of Semantics.- Cumulative Learning with Causal-Relational Models.- Transforming Kantian Aesthetic Principles into Qualitative Hermeneutics for Contemplative AGI Agents.- Towards General Evaluation of Intelligent Systems: Using Semantic Analysis to Improve Environments in the AIQ Test.- Perception from an AGI Perspective.- A Phenomenologically Justifiable Simulation of Mental Modeling.- A Time-critical Simulation of Language Comprehension.- How Failure Facilitates Success.- Adaptive Compressed Search.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 11th International Conference on Artificial General Intelligence, AGI 2018, held in Prague, Czech Republic, in August 2018. The 19 regular papers and 10 poster papers presented in this book were carefully reviewed and selected from 52 submissions. The conference encourage interdisciplinary research based on different understandings of intelligence, and exploring different approaches. As the AI field becomes increasingly commercialized and well accepted, maintaining and emphasizing a coherent focus on the AGI goals at the heart of the field remains more critical than ever.
(source: Nielsen Book Data)

• Evolutionary Computation and Learning.- Neural Networks.- Pattern Recognition.- Image Processing.- Information Security.- Virtual Reality and Human-Computer Interaction.- Business Intelligence and Multimedia Technology.- Biomedical Informatics Theory and Methods.- Swarm Intelligence and Optimization.- Natural Computing.- Quantum Computing.- Intelligent Computing in Computer Vision.- Fuzzy Theory and Algorithms.- Machine Learning.- Systems Biology.- Intelligent Systems and Applications for Bioengineering.- Evolutionary Optimization: Foundations and Its Applications to Intelligent Data Analytics.- Swarm Evolutionary Algorithms for Scheduling and Combinatorial Optimization.- Swarm Intelligence and Applications in Combinatorial Qoptimization.- Advances in Metaheuristic Optimization Algorithm.- Advances in Image Processing and Pattern Techniques.- Bioinformatics.
• (source: Nielsen Book Data)

This book constitutes - in conjunction with the two-volume set LNCS 10954 and LNCS 10955 - the refereed proceedings of the 14th International Conference on Intelligent Computing, ICIC 2018, held in Wuhan, China, in August 2018. The 275 full papers and 72 short papers of the three proceedings volumes were carefully reviewed and selected from 632 submissions. The papers are organized in topical sections such as Evolutionary Computation and Learning; Neural Networks; Pattern Recognition; Image Processing; Information Security; Virtual Reality and Human-Computer Interaction; Business Intelligence and Multimedia Technology; Biomedical Informatics Theory and Methods; Swarm Intelligence and Optimization; Natural Computing; Quantum Computing; Intelligent Computing in Computer Vision; Fuzzy Theory and Algorithms; Machine Learning; Systems Biology; Intelligent Systems and Applications for Bioengineering; Evolutionary Optimization: Foundations and Its Applications to Intelligent Data Analytics; Swarm Evolutionary Algorithms for Scheduling and Combinatorial Optimization; Swarm Intelligence and Applications in Combinatorial Qoptimization; Advances in Metaheuristic Optimization Algorithm; Advances in Image Processing and Pattern Techniques; Bioinformatics.
(source: Nielsen Book Data)

• Neural Networks.- Pattern Recognition.- Image Processing.- Intelligent Computing in Robotics.- Intelligent Control and Automation.- Intelligent Data Analysis and Prediction.- Fuzzy Theory and Algorithms.- Supervised Learning.- Unsupervised Learning.- Kernel Methods and Supporting Vector Machines.- Knowledge Discovery and Data Mining.- Natural Language Processing and Computational Linguistics.- Gene Expression Array Analysis.- Systems Biology.- Computational Genomics.- Computational Proteomics.- Gene Regulation Modeling and Analysis.- Protein-Protein Interaction Prediction.- Next-Gen Sequencing and Metagenomics.- Structure Prediction and Folding.- Evolutionary Optimization for Scheduling.- High-Throughput Biomedical Data Integration and Mining.- Machine Learning Algorithms and Applications.- Heuristic Optimization Algorithms for Real-World Applications.- Evolutionary Multi-Objective Optimization and Its Applications.- Swarm Evolutionary Algorithms for Scheduling and Combinatorial.- Optimization.- Swarm Intelligence and Applications in Combinatorial Optimization.- Advances in Metaheuristic Optimization Algorithm.- Advances in Image Processing and Pattern Recognition Techniques.- AI in Biomedicine.- Bioinformatics.- Biometrics Recognition.- Information Security.- Virtual Reality and Human-Computer Interaction.- Healthcare Informatics Theory and Methods.- Intelligent Computing in Computer Vision.- Intelligent Agent and Web Applications.- Reinforcement Learning.- Machine Learning.- Modeling, Simulation, and Optimization of Biological Systems.- Biomedical Data Modeling and Mining.- Cheminformatics.- Intelligent Computing in Computational Biology.- Protein Structure and Function Prediction.- Biomarker Discovery.- Hybrid Computational Intelligence: Theory and Application in Bioinformatics, Computational Biology and Systems Biology.- IoT and Smart Data.- Intelligent Systems and Applications for Bioengineering.- Evolutionary Optimization: Foundations and Its Applications to Intelligent Data Analytics.- Protein and Gene Bioinformatics: Analysis, Algorithms and Applications.
• (source: Nielsen Book Data)

This two-volume set LNCS 10954 and LNCS 10955 constitutes - in conjunction with the volume LNAI 10956 - the refereed proceedings of the 14th International Conference on Intelligent Computing, ICIC 2018, held in Wuhan, China, in August 2018. The 275 full papers and 72 short papers of the three proceedings volumes were carefully reviewed and selected from 632 submissions. The papers are organized in topical sections such as Neural Networks.- Pattern Recognition.- Image Processing.- Intelligent Computing in Robotics.- Intelligent Control and Automation.- Intelligent Data Analysis and Prediction.- Fuzzy Theory and Algorithms.- Supervised Learning.- Unsupervised Learning.- Kernel Methods and Supporting Vector Machines.- Knowledge Discovery and Data Mining.- Natural Language Processing and Computational Linguistics.- Gene Expression Array Analysis.- Systems Biology.- Computational Genomics.- Computational Proteomics.- Gene Regulation Modeling and Analysis.- Protein-Protein Interaction Prediction.- Next-Gen Sequencing and Metagenomics.- Structure Prediction and Folding.- Evolutionary Optimization for Scheduling.- High-Throughput Biomedical Data Integration and Mining.- Machine Learning Algorithms and Applications.- Heuristic Optimization Algorithms for Real-World Applications.- Evolutionary Multi-Objective Optimization and Its Applications.- Swarm Evolutionary Algorithms for Scheduling and Combinatorial.- Optimization.- Swarm Intelligence and Applications in Combinatorial Optimization.- Advances in Metaheuristic Optimization Algorithm.- Advances in Image Processing and Pattern Recognition Techniques.- AI in Biomedicine.- Bioinformatics.- Biometrics Recognition.- Information Security.- Virtual Reality and Human-Computer Interaction.- Healthcare Informatics Theory and Methods.- Intelligent Computing in Computer Vision.- Intelligent Agent and Web Applications.- Reinforcement Learning.- Machine Learning.- Modeling, Simulation, and Optimization of Biological Systems.- Biomedical Data Modeling and Mining.- Cheminformatics.- Intelligent Computing in Computational Biology.- Protein Structure and Function Prediction.- Biomarker Discovery.- Hybrid Computational Intelligence: Theory and Application in Bioinformatics, Computational Biology and Systems Biology.- IoT and Smart Data.- Intelligent Systems and Applications for Bioengineering.- Evolutionary Optimization: Foundations and Its Applications to Intelligent Data Analytics.- Protein and Gene Bioinformatics: Analysis, Algorithms and Applications.
(source: Nielsen Book Data)

• Neural Networks.- Pattern Recognition.- Image Processing.- Intelligent Computing in Robotics.- Intelligent Control and Automation.- Intelligent Data Analysis and Prediction.- Fuzzy Theory and Algorithms.- Supervised Learning.- Unsupervised Learning.- Kernel Methods and Supporting Vector Machines.- Knowledge Discovery and Data Mining.- Natural Language Processing and Computational Linguistics.- Gene Expression Array Analysis.- Systems Biology.- Computational Genomics.- Computational Proteomics.- Gene Regulation Modeling and Analysis.- Protein-Protein Interaction Prediction.- Next-Gen Sequencing and Metagenomics.- Structure Prediction and Folding.- Evolutionary Optimization for Scheduling.- High-Throughput Biomedical Data Integration and Mining.- Machine Learning Algorithms and Applications.- Heuristic Optimization Algorithms for Real-World Applications.- Evolutionary Multi-Objective Optimization and Its Applications.- Swarm Evolutionary Algorithms for Scheduling and Combinatorial.- Optimization.- Swarm Intelligence and Applications in Combinatorial Optimization.- Advances in Metaheuristic Optimization Algorithm.- Advances in Image Processing and Pattern Recognition Techniques.- AI in Biomedicine.- Bioinformatics.- Biometrics Recognition.- Information Security.- Virtual Reality and Human-Computer Interaction.- Healthcare Informatics Theory and Methods.- Intelligent Computing in Computer Vision.- Intelligent Agent and Web Applications.- Reinforcement Learning.- Machine Learning.- Modeling, Simulation, and Optimization of Biological Systems.- Biomedical Data Modeling and Mining.- Cheminformatics.- Intelligent Computing in Computational Biology.- Protein Structure and Function Prediction.- Biomarker Discovery.- Hybrid Computational Intelligence: Theory and Application in Bioinformatics, Computational Biology and Systems Biology.- IoT and Smart Data.- Intelligent Systems and Applications for Bioengineering.- Evolutionary Optimization: Foundations and Its Applications to Intelligent Data Analytics.- Protein and Gene Bioinformatics: Analysis, Algorithms and Applications.
• (source: Nielsen Book Data)

This two-volume set LNCS 10954 and LNCS 10955 constitutes - in conjunction with the volume LNAI 10956 - the refereed proceedings of the 14th International Conference on Intelligent Computing, ICIC 2018, held in Wuhan, China, in August 2018. The 275 full papers and 72 short papers of the three proceedings volumes were carefully reviewed and selected from 632 submissions. The papers are organized in topical sections such as Neural Networks.- Pattern Recognition.- Image Processing.- Intelligent Computing in Robotics.- Intelligent Control and Automation.- Intelligent Data Analysis and Prediction.- Fuzzy Theory and Algorithms.- Supervised Learning.- Unsupervised Learning.- Kernel Methods and Supporting Vector Machines.- Knowledge Discovery and Data Mining.- Natural Language Processing and Computational Linguistics.- Gene Expression Array Analysis.- Systems Biology.- Computational Genomics.- Computational Proteomics.- Gene Regulation Modeling and Analysis.- Protein-Protein Interaction Prediction.- Next-Gen Sequencing and Metagenomics.- Structure Prediction and Folding.- Evolutionary Optimization for Scheduling.- High-Throughput Biomedical Data Integration and Mining.- Machine Learning Algorithms and Applications.- Heuristic Optimization Algorithms for Real-World Applications.- Evolutionary Multi-Objective Optimization and Its Applications.- Swarm Evolutionary Algorithms for Scheduling and Combinatorial.- Optimization.- Swarm Intelligence and Applications in Combinatorial Optimization.- Advances in Metaheuristic Optimization Algorithm.- Advances in Image Processing and Pattern Recognition Techniques.- AI in Biomedicine.- Bioinformatics.- Biometrics Recognition.- Information Security.- Virtual Reality and Human-Computer Interaction.- Healthcare Informatics Theory and Methods.- Intelligent Computing in Computer Vision.- Intelligent Agent and Web Applications.- Reinforcement Learning.- Machine Learning.- Modeling, Simulation, and Optimization of Biological Systems.- Biomedical Data Modeling and Mining.- Cheminformatics.- Intelligent Computing in Computational Biology.- Protein Structure and Function Prediction.- Biomarker Discovery.- Hybrid Computational Intelligence: Theory and Application in Bioinformatics, Computational Biology and Systems Biology.- IoT and Smart Data.- Intelligent Systems and Applications for Bioengineering.- Evolutionary Optimization: Foundations and Its Applications to Intelligent Data Analytics.- Protein and Gene Bioinformatics: Analysis, Algorithms and Applications.
(source: Nielsen Book Data)

• Intro
• Preface
• Organization
• Contents
• Constraint Solving and Optimization
• A Method for the Online Construction of the Set of States of a Markov Decision Process Using Answer Set Programming
• 1 Introduction
• 2 Background
• 2.1 MDP and Reinforcement Learning
• 2.2 Answer Set Programming
• 3 Online ASP for MDP: oASP(MDP)
• 4 Tests and Results
• 4.1 First Test: Changes in the Wall-Free-Space Ratio
• 4.2 Second Test: Changes in the Transition Probabilities
• 5 Related Work
• 6 Conclusion
• References

• Hardware/Software Co-design for Template Matching Using Cuckoo Search Optimization
• 1 Introduction
• 2 Theoretical Background
• 3 Hardware Architecture
• 4 Results
• 5 Conclusion
• References
• Optimziation Methods for Beacon Based Foraging Algorithms
• 1 Introduction
• 2 Related Work
• 3 The Rostering Technique
• 4 Rostering Experimental Analysis
• 5 Network Optimizaiton
• 6 NetOpt Experimental Analysis
• 7 Future Work
• 8 Conclusions
• References
• On Using ``Stochastic Learning on the Line'' to Design Novel Distance Estimation Methods
• 1 Introduction

• 2 Distance Estimation: Core Concepts
• 2.1 Distance Estimation Functions (DEFs)
• 3 The Adaptive Tertiary Search and Its Use in DE
• 3.1 Updating Search Intervals
• 3.2 The Corresponding LA
• 3.3 The Corresponding Environment
• 4 Testing and Results: 2-Dimensional Environments
• 5 Conclusions
• References
• Data Mining and Knowledge Discovery
• Person Re-identification Using Masked Keypoints
• 1 Introduction
• 2 Person Re-identification
• 3 A Keypoint-Based Re-ID Method
• 3.1 Person Detection
• 3.2 Person Re-identification
• 4 Experiments
• 4.1 Data Collection

• 4.2 Parameters Setting
• 4.3 Comparing Our Method
• 5 Conclusions
• References
• Knowledge Discovery Process for Detection of Spatial Outliers
• 1 Introduction
• 2 Knowledge Discovery Process for Detection of Spatial Outliers
• 2.1 Data Preparation
• 2.2 Neighborhood Definition
• 2.3 Outlier Detection and Filtering
• 2.4 Neighborhood Description
• 2.5 Group Outlier Detection
• 2.6 Information Analysis
• 3 Proof of Concept
• 4 Conclusion
• References
• Text Modeling Using Multinomial Scaled Dirichlet Distributions
• 1 Introduction

• 2 Multinomial and Dirichlet Compound Multinomial
• 3 Multinomial Scaled Dirichlet (MSD) Distribution
• 4 MSD Mixture Model Learning
• 5 Experimental Results
• 6 Conclusion
• References
• A Comparison of Knee Strategies for Hierarchical Spatial Clustering
• 1 Introduction
• 2 Background
• 2.1 Hierarchical Clustering
• 2.2 Distance Measures
• 2.3 Knee Determination
• 3 Spatial Data and Preparation
• 4 Results
• 5 Conclusion
• References
• Credit Card Default Prediction as a Classification Problem
• Abstract
• 1 Introduction
• 2 Related Works

This book constitutes the thoroughly refereed proceedings of the 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, held in Montreal, QC, Canada, in June 2018. The 53 full papers and 33 short papers presented were carefully reviewed and selected from 146 submissions. They are organized in the following topical sections: constraint solving and optimization; data mining and knowledge discovery; evolutionary computation; expert systems and robotics; knowledge representation, machine learning; meta-heuristics; multi-agent systems; natural language processing; neural networks; planning, scheduling and spatial reasoning; rough sets, Internet of Things (IoT), ubiquitous computing and big data; data science, privacy, and security; inelligent systems approaches in information extraction; and artificial intelligence, law and justice.
(source: Nielsen Book Data)

• Neural Networks
• Neural Computing and Optimization
• Case Based Reasoning and Autonomy-Oriented Computing
• Knowledge Discovery and Data Mining
• Evolutionary Computing and Genetic Algorithms
• Fuzzy Systems and Soft Computing
• A4 Particle Swarm Optimization and Niche Technology
• Swarm Intelligence and Optimization
• Kernel Methods and Support Vector Machines
• Biological and Quantum Computing
• Intelligent Agent and Web Applications
• Intelligent Sensor Networks
• Intelligent Fault Diagnosis
• Intelligent Control and Automation
• Intelligent Data Fusion and Security
• Intelligent Prediction and Time Series Analysis
• Natural Language Processing and Expert Systems
• Intelligent Image/Document Retrievals
• Intelligent Computing in Bioinformatics
• Intelligent Computing in Signal Processing
• Intelligent Computing in Pattern Recognition
• Intelligent Computing in Commiuncation
• Special Session on Computational Intelligence Approaches and Methods for Security Engineering
• Special Session on Intelligent Human-Computer Interactions for Multi-modal and Autonomous Environment
• Special Session on Intelligent Robot Systems Based on Vision Technology
• Other Topics.

This volume, in conjunction with the two volumes LNCS 4681 and LNAI 4682, constitutes the refereed proceedings of the Third International Conference on Intelligent Computing held in Qingdao, China, in August 2007. The conference sought to establish contemporary intelligent computing techniques as an integral method that underscores trends in advanced computational intelligence and links theoretical research with applications.
(source: Nielsen Book Data)

• Session
• 1. Agents.- Modeling Role-Based Agent Team.- Distributed Collaborative Filtering for Robust Recommendations Against Shilling Attacks.- Competition and Coordination in Stochastic Games.- Multiagent-Based Dynamic Deployment Planning in RTLS-Enabled Automotive Shipment Yard.- R-FRTDP: A Real-Time DP Algorithm with Tight Bounds for a Stochastic Resource Allocation Problem.- A Reorganization Strategy to Build Fault-Tolerant Multi-Agent Systems.- Session
• 2. Bioinformatics.- A Multi-site Subcellular Localizer for Fungal Proteins.- Selecting Genotyping Oligo Probes Via Logical Analysis of Data.- Session
• 3. Classification.- Learning the Semantic Meaning of a Concept from the Web.- On Combining Dissimilarity-Based Classifiers to Solve the Small Sample Size Problem for Appearance-Based Face Recognition.- A Novel Approach for Automatic Palmprint Recognition.- ICS: An Interactive Classification System.- Fast Most Similar Neighbor Classifier for Mixed Data.- Performance Measures in Classification of Human Communications.- Cost-Sensitive Decision Trees with Pre-pruning.- Probability Based Metrics for Locally Weighted Naive Bayes.- Recurrent Boosting for Classification of Natural and Synthetic Time-Series Data.- Pattern Classification in No-Limit Poker: A Head-Start Evolutionary Approach.- Session
• 4. Constraint Satisfaction.- Managing Conditional and Composite CSPs.- Multiagent Constraint Satisfaction with Multiply Sectioned Constraint Networks.- Session
• 5. Data Mining.- A Clustering Algorithm Based on Adaptive Subcluster Merging.- Efficient Algorithms for Video Association Mining.- Distributed Data Mining in a Ubiquitous Healthcare Framework.- Constructing a User Preference Ontology for Anti-spam Mail Systems.- Question Answering Summarization of Multiple Biomedical Documents.- A Profit-Based Business Model for Evaluating Rule Interestingness.- Session
• 6. Knowledge Representation and Reasoning.- Reasoning About Operations on Sets.- Analytic Results on the Hodgkin-Huxley Neural Network: Spikes Annihilation.- Improving Importance Sampling by Adaptive Split-Rejection Control in Bayesian Networks.- Adding Local Constraints to Bayesian Networks.- On the Use of Possibilistic Bases for Local Computations in Product-Based Possibilistic Networks.- Reasoning with Conditional Preferences Across Attributes.- Path Propagation for Inference in Bayesian Networks.- Problem-Solving Knowledge Mining from Users' Actions in an Intelligent Tutoring System.- Incremental Neighborhood Graphs Construction for Multidimensional Databases Indexing.- Session
• 7. Learning.- Learning Network Topology from Simple Sensor Data.- Reinforcement Learning in Nonstationary Environment Navigation Tasks.- On the Stability and Bias-Variance Analysis of Kernel Matrix Learning.- Session
• 8. Natural Language.- Query-Based Summarization of Customer Reviews.- Multi-state Directed Acyclic Graphs.- Fuzzy Clustering for Topic Analysis and Summarization of Document Collections.- Creating a Fuzzy Believer to Model Human Newspaper Readers.- Rethinking the Semantics of Complex Nominals.- A Hybrid Approach to Improving Automatic Speech Recognition Via NLP.- Session
• 9. Planning.- Planning in Multiagent Expedition with Collaborative Design Networks.- Hierarchical Shortest Pathfinding Applied to Route-Planning for Wheelchair Users.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, held in Montreal, Canada, in May 2007. The 46 revised full papers cover agents, bioinformatics, classification, constraint satisfaction, data mining, knowledge representation and reasoning, learning, natural language, and planning.
(source: Nielsen Book Data)

• Local Ordinal Classification.- Using Genetic Algorithms and Decision Trees for a posteriori Analysis and Evaluation of Tutoring Practices based on Student Failure Models.- Exploiting Decision Trees in Product-based Fuzzy Neural Modeling to Generate Rules with Dynamically Reduced Dimensionality.- Retraining the Neural Network for Data Visualization.- Rule-Based Adaptive Navigation for an Intelligent Educational Mobile Robot.- BRWM: A relevance feedback mechanism for web page clustering.- Bagged Averaging of Regression Models.- Argument-based User Support Systems using Defeasible Logic Programming.- Knowledge Modelling Using The UML Profile.- Optimized Multi-Domain Secure Interoperation using Soft Constraints.- Learning context models for the recognition of scenarios.- MDA-Based Architecture of a Description Logics Reasoner.- Incremental guideline formalization with tool support.- Accessing data in the semantic web: An intelligent data integration and navigation approaches.- An Expert System Delivery Environment for the World Wide Web.- A Formally Specified Ontology Management API as a Registry for Ubiquitous Computing Systems.- Intelligent Configurable Electronic Shop Platform based on Ontologies and 3D Visualization.- Adapting User Interfaces to the User Interaction Requirements in Real Time.- On supporting young students in visual logic modeling.- Attentional Model for Perceiving Social Context in Intelligent Environments.- Penguin Quart - Slovak Digit Speech Recognition Game Based on HMM.- Impact of Face Registration Errors on Recognition.- Unsupervised Segmentation of Meeting Configurations and Activities using Speech Activity Detection.- A Model of Real-Time Indoor Surveillance System using Behavior Detection.- A Filter Module Used in Pedestrian Detection System.- User Localization for Intelligent Crisis Management.- An Intrusion Detection System for Network-Initiated Attacks Using a Hybrid Neural Network.- Which Adequate Trust Model for Trust Networks?.- XML Systems for Intelligent Management of Pervasive Computing Resources.- A constraint based approach for aiding heat treatment operation design and distortion evaluation.- Applying AI to Cooperating Agricultural Robots.- Capacity Evaluation of an Indoor Smart Antenna System at 60 GHz.- Steady State Contingency analysis of electrical networks using machine learning techniques.- Robust Multimodal Audio-Visual Processing for Advanced Context Awareness in Smart Spaces.- Toward supporting group dynamics.- Multimodal Integration of Sensor Network.- Multimodal Identity Tracking in a Smartroom.- Multimodal Focus Attention and Stress Detection and feedback in an Augmented Driver Simulator.- A Fuzzy Expert System for the Early Warning of Accidents Due to Driver Hypo-Vigilance.- Mixed Reality Cane Simulation.- 3D content-based search using sketches.- Manual Annotation and Automatic Image Processing of Multimodal Emotional Behaviors in TV Interviews.- MPEG-4 Facial Expression Synthesis based on Appraisal Theory.- Towards On- and Off-line Search, Browse and Replay of Home Activities.- Engineering an interoperable adaptive hypermedia testing tool supporting user adaptable strategies.- Teaching a Computer Science Course using Adaptable Instructional Images.- e-Class Personalized: Design and Evaluation of an Adaptive Learning Content Management System.- The Use of Psychophysiological Measures for Designing Adaptive Learning Systems.- Developing Personalized E-Books: A Multi-Layered Approach.- Designing a Solver for Arithmetic Constraints to Support Education in Mathematics.- A Tutoring System Discovering Gaps in the Current Body of Students' Knowledge.- Sequencing Parametric Exercises for an Operating System Course.- A gene expression analysis system for medical diagnosis.- Recording, Monitoring and Interrelating Changes of Invivo Bio-cells from Video (Biosignatures).- An Archetype for MRI guided Tele-interventions.- Differential Evolution Algorithms for Finding Predictive Gene Subsets in Microarray Data.- Feature Selection for Microarray Data Analysis Using Mutual Information and Rough Set Theory.- A Support Vector Machine Approach to Breast Cancer Diagnosis and Prognosis.- Source Code Author Identification Based on N-gram Author Profiles.- AJA - Tool for Programming Adaptable Agents.- Investigating the Predictability of Empirical Software Failure Data with Artificial Neural Networks and Hybrid Models.- Selecting the Appropriate Machine Learning Techniques for the Prediction of Software Development Costs.- On the Idea of Using Nature-Inspired Metaphors to Improve Software Testing.- Fast Video Object Tracking using Affine Invariant Normalization.- Knowledge Acquisition from Multimedia Content using an Evolution Framework.- Exploratory Search: Image Retrieval without Deep Semantics.- Word Senses: The Stepping Stones in Semantic-Based Natural Language Processing.- Space-Time Tubes and Motion Representation.- Semantic Concept Detection from News Videos with Self-Organizing Maps.- Analysis of Semantic Information Available in an Image Collection Augmented with Auxiliary Data.- Supporting Semi-Automatic Semantic Annotation of Multimedia Resources.- A Simulation Tool for Modelling Pedestrian Dynamics during Evacuation of Large Areas.- Radar Imaging by Range Density Functions.- A Method for Incremental Data Fusion in Distributed Sensor Networks.- Information Society: the two faces of Janus.- Ant Seeker: An algorithm for enhanced web search.- Increasing Intelligent Wireless Sensor Networks Survivability by Applying Energy-Efficient Schemes.- A Review of Video Watermarking and a Benchmarking Framework.- Chaotic Encryption Driven Watermarking of Human Video Objects Based on Hu Moments.- Semi-Fragile Watermarking Authentication with Local and Global Watermarks.- Decentralising the Digital Rights Management Value Chain by means of Distributed License Catalogues.- AXMEDIS architectural solution for interoperable content and DRM on multichannel distribution.- Computer Aided Diagnosis of CT Focal Liver Lesions based on Texture Features, Feature Selection and Ensembles of Classifiers.- Texture Analysis for Classification of Endometrial Tissue in Gray Scale Transvaginal Ultrasonography.- Wavelet-based Feature Analysis for Classification of Breast Masses from Normal Dense Tissue.- Microcalcification Features Extracted from Principal Component Analysis in the Wavelet Domain.- Classification of Atherosclerotic Carotid Plaques Using Gray Level Morphological Analysis on Ultrasound images.
• (source: Nielsen Book Data)

Artificial Intelligence applications build on a rich and proven theoretical background to provide solutions to a wide range of real life problems. The ever expanding abundance of information and computing power enables researchers and users to tackle higly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information based on preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The purpose of the 3rd IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI) is to bring together researchers, engineers, and practitioners interested in the technical advances and business and industrial applications of intelligent systems. AIAI 2006 is focused on providing insights on how AI can be implemented in real world applications.
(source: Nielsen Book Data)

• Knowledge and Information Management.- Can Common Sense uncover cultural differences in computer applications?.- Artificial Intelligence and Knowledge Management.- Agents 1.- The IONWI Algorithm: Learning when and when not to interrupt.- Formal Analysis of the Communication of Probabilistic Knowledge.- Detecting and Repairing Anomalous Evolutions in Noisy Environments: Logic Programming Formalization and Complexity Results.- Adding Semantic Web Services Matching and Discovery Support to the MoviLog Platform.- Learning Browsing Patterns for Context-Aware Recommendation.- Applying Collaborative Filtering to Reputation Domain: a model for more precise reputation estimates in case of changing behavior by rated participants.- Integration of AI with other Technologies.- Combine Vector Quantization and Support Vector Machine for Imbalanced Datasets.- Ontology Support for Translating Negotiation Primitives.- Statistical Method of Context Evaluation for Biological Sequence Similarity.- Biological inspired algorithm for Storage Area Networks (ACOSAN).- Neural Nets.- Radial Basis Functions Versus Geostatistics in Spatial Interpolations.- Neural Networks applied to wireless communications.- Anomaly Detection using prior knowledge: application to TCP/IP traffic.- A study on the ability of Support Vector Regression and Neural Networks to Forecast Basic Time Series Patterns.- Neural Plasma.- Knowledge Acquisition and Data Mining.- Comparison of SVM and Some Older Classification Algorithms in Text Classification Tasks.- An automatic graph layout procedure to visualize correlated data.- Knowledge Perspectives in Data Grids.- On the Class Distribution Labelling Step Sensitivity of CO-TRAINING.- Two new feature selection algorithms with Rough Sets Theory.- Evolutionary Computation.- Global Convexity in the Bi-Criteria Traveling Salesman Problem.- Evolutionary Algorithm for State Encoding.- Hypercube FrameWork for ACO applied to timetabling.- Multitree-Multiobjective Multicast Routing for Traffic Engineering.- Speech and Natural Language.- Road Segment Identification in Natural Language Text.- Learning Discourse-new References in Portuguese Texts.- Analysing Definition Questions by Two Machine Learning Approaches.- Fuzzy Rule-Based Hand Gesture Recognition.- Comparison of distance measures for historical spelling variants.- Industrial Applications of AI.- Patterns in Temporal Series of Meteorological Variables Using SOM & TDIDT.- Applying Genetic Algorithms to Convoy Scheduling.- A GRASP algorithm to solve the problem of dependent tasks scheduling in different machines.- A support vector machine as an estimator of mountain papaya ripeness using resonant frequency or frequency centroid.- FieldPlan: Tactical Field Force Planning in BT.- An Agent Solution to Flexible Planning and Scheduling of Passenger Trips.- Machine Vision.- Facial expression recognition using shape and texture information.- Limited Receptive Area neural classifier for texture recognition of metal surfaces.- A Tracking Framework for Accurate Face Localization.- A Combination of Spatiotemporal ICA and Euclidean Features for Face Recognition.- Agents 2.- Three Technologies for Automated Trading.- Modeling Travel Assistant Agents: a graded BDI approach.- e-Tools: An agent coordination layer to support the mobility of persons with disabilities..- Expert Systems.- Conceptualization Maturity Metrics for Expert Systems.- Toward developing a tele-diagnosis system on fish disease.- A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model.- Effective Prover for Minimal Inconsistency Logic.- Identification of Important News for Exchange Rate Modeling.- Planning and Scheduling.- Autonomous Search and Rescue Rotorcraft Mission Stochastic Planning with Generic DBNs.- Solving multi-objective scheduling problems-An integrated systems approach.
• (source: Nielsen Book Data)

This volume presents proceedings from the 19th IFIP World Computer Congress in Santiago, Chile. The proceedings of the World Computer Congress are a product of the gathering of 2,000 delegates from more than 70 countries to discuss a myriad of topics in the ICT domain. Of particular note, this marks the first time that a World Computer Congress has been held in a Latin American country. Topics in this series include: the 4th International Conference on Theoretical Computer Science; Education for the 21st Century; Impact of ICT and Digital Resources; Mobile and Wireless Communication Networks; Ad-Hoc Networking; Network Control and Engineering for QoS, Security, and Mobility; The Past and Future of Information Systems: 1976-2006 and Beyond; History of Computing and Education; Biologically Inspired Cooperative Computing; Artificial Intelligence in Theory and Practice; Applications in Artificial Intelligence; and, Advanced Software Engineering: Expanding the Frontiers of Software.
(source: Nielsen Book Data)
The papers in this volume comprise the refereed proceedings of the conference 'Artificial Intelligence in Theory and Practice' (IFIP AI 2006), which formed part of the 19th World Computer Congress of IFIP, the International Federation for Information Processing (WCC- 2006), in Santiago, Chile in August 2006. The conference is organised by the IFIP Technical Committee on Artificial Intelligence (Technical Committee 12) and its Working Group 12.5 (Artificial Intelligence Applications). All papers were reviewed by at least two members of our Programme Committee. The best papers were selected for the conference and are included in this volume. The international nature of IFIP is amply reflected in the large number of countries represented here. The conference featured invited talks by Rose Dieng, John Atkinson, John Debenham and myself. IFIP AI 2006 also included the Second IFIP Symposium on Professional Practice in Artificial Intelligence, organised by Professor John Debenham, which ran alongside the refereed papers. I should like to thank the conference chair. Professor Debenham for all his efforts in organising the Symposium and the members of our programme committee for reviewing an unexpectedly large number of papers to a very tight deadline. This is the latest in a series of conferences organised by IFIP Technical Committee 12 dedicated to the techniques of Artificial Intelligence and their real-world applications. The wide range and importance of these applications is clearly indicated by the papers in this volume. Further information about TCI 2 can be found on our website http://www.ifiptcl2.org.
(source: Nielsen Book Data)

• 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.
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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)

• Plenary Talk.- From Search Engines to Question-Answering Systems: The Problems of World Knowledge, Relevance, Deduction, and Precisiation.- Invited Session: Fuzzy Multiperson and Multicriteria Decisions Modelling.- A Fuzzy Approach to Optimal R&D Project Portfolio Selection.- Choquet Integration and Correlation Matrices in Fuzzy Inference Systems.- Linguistic Summarization of Some Static and Dynamic Features of Consensus Reaching.- Consistency for Nonadditive Measures: Analytical and Algebraic Methods.- Neural Nets.- Neuro-Fuzzy Kolmogorov's Network with a Modified Perceptron Learning Rule for Classification Problems.- A Self-Tuning Controller for Teleoperation System using Evolutionary Learning Algorithms in Neural Networks.- A Neural-Based Method for Choosing Embedding Dimension in Chaotic Time Series Analysis.- On Classification of Some Hopfield-Type Learning Rules via Stability Measures.- Applications I.- A New Genetic Based Algorithm for Channel Assignment Problems.- Max-Product Fuzzy Relational Equations as Inference Engine for Prediction of Textile Yarn Properties.- Automatic Defects Classification and Feature Extraction Optimization.- Short-Term Load Forecasting in Power System Using Least Squares Support Vector Machine.- Plenary Talk.- Fifteen Years of Fuzzy Logic in Dortmund.- Invited Session: Intuitionistic Fuzzy Sets and Generalized Nets I.- Intuitionistic Fuzzy Graphs.- On Some Intuitionistic Properties of Intuitionistic Fuzzy Implications and Negations.- On Intuitionistic Fuzzy Negations.- Invited Session: Soft Computing Techniques for Reputation and Trust I.- A Simulation Model for Trust and Reputation System Evaluation in a P2P Network.- A Fuzzy Trust Model Proposal to Ensure the Identity of a User in Time.- Quantification of the Effectiveness of the Markov Model for Trustworthiness Prediction.- Applications II.- Fuzzy-Genetic Methodology for Web-based Computed-Aided Diagnosis in Medical Applications.- Weight Optimization for Loan Risk Estimation with Genetic Algorithm.- A Fuzzy Feature Extractor Neural Network and its Application in License Plate Recognition.- Invited Session: Intuitionistic Fuzzy Sets and Generalized Nets II.- Nearest Interval Approximation of an Intuitionistic Fuzzy Number.- On Intuitionistic Fuzzy Expert Systems With Temporal Parameters.- Generalized Fuzzy Cardinalities of IF Sets.- Invited Session: Soft Computing Techniques for Reputation and Trust II.- Towards Usage Policies for Fuzzy Inference Methodologies for Trust and QoS Assessment.- Simulating a Trust-Based Peer-to-Peer Metadata Publication Center.- The Complex Facets of Reputation and Trust.- Theory I.- Fuzzy Covering Relation and Ordering: An Abstract Approach.- Measures of Differentiability.- Lipschitz Continuity of Triangular Norms.- Plenary Talk.- Formal Models of Knowledge Operators: Rough-Set-Style and Fuzzy-Set-Style Approaches.- Invited Session: Looking at Language with Fuzzy Logic.- Using a Fuzzy Model for Combining Search Results from Different Information Sources to Build a Metasearch Engine.- Some Fuzzy Counterparts of the Language uses of And and Or.- Fuzzy Sets Versus Language.- Theory II.- Some Properties of Fuzzy Languages.- General Form of Lattice Valued Intuitionistic Fuzzy Sets.- A Note on Generated Pseudo-Operations with Two Parameters as a base for the Generalized Pseudo-Laplace Type Transform.- Theory III.- Fuzzy All-Pairs Shortest Paths Problem.- Optimal Toll Charges in a Fuzzy Flow Problem.- Modified Interval Global Weights in AHP.- Plenary Talk.- Fuzzy Approaches to Trust Management.- Invited Session: Complex-Valued Neural Networks.- Proposal of Holographic 3D-Movie Generation Using Coherent Neural-Network Interpolation.- Blur Identification Using Neural Network for Image Restoration.- Solving the Parity n Problem and Other Nonlinearly Separable Problems Using a Single Universal Binary Neuron.- Some Novel Real/Complex-Valued Neural Network Models.- Theory IV.- Extending the Fuzzy Rule Interpolation "FIVE" by Fuzzy Observation.- Fuzzy Rule Interpolation Based on Polar Cuts.- Approximate Reasoning Using Fodor's Implication.- Plenary Talk.- Brain-, Gene-, and Quantum-Inspired Computational Intelligence: Challenges and Opportunities.- Invited Session: Intelligent Data Mining.- Incremental Learning for E-mail Classification.- Reduction of Search Space for Instance-Based Classifier Combination.- Invited Session: Preferences and Decisions.- Linguistic Matrix Aggregation Operators: Extensions of the Borda Rule.- Evolutionary Algorithms.- An Evolutionary Algorithm for the Biobjective QAP.- On a Hill-Climbing Algorithm with Adaptive Step Size: Towards a Control Parameter-Less Black-Box Optimisation Algorithm.- Self-Adaptive Baldwinian Search in Hybrid Genetic Algorithms.- Intragenerational Mutation Shape Adaptation.- Theory V.- The Choquet-Integral as an Aggregation Operator in Case-Based Learning.- Fuzzy Sets and Multicriteria Decision Making.- Fuzzy Reinforcement Learning for Routing in Wireless Sensor Networks.- Outlier Resistant Recursive Fuzzy Clustering Algorithms.- Invited Session: Fuzzy Sets - 40 years after.- Fuzzy Set Theory - 40 Years of Foundational Discussions.- Fuzzy Control - Expectations, Current State, and Perspectives.- Fuzzy Sets in Categories of Sets with Similarity Relations.- Fuzzy Sets as a Special Mathematical Model of Vagueness Phenomenon.- Fuzzy IF-THEN Rules from Logical Point of View.- Applications III.- Synthesizing Adaptive Navigational Robot Behaviours Using a Hybrid Fuzzy A* Approach.- Fuzzy Impulse Noise Reduction Methods for Color Images.- Use of Variable Fuzzy Sets Methods for Desertification Evaluation.- A Fuzzy Ultrasonic System for Estimating Degradation of Insulating Oil.- A Genetic Algorithm-Based Fuzzy Inference System in Prediction of Wave Parameters.- Poster Contributions.- Estimation of Degree of Polymerisation and Residual Age of Transformers Based on Furfural Levels in Insulating Oil Through Generalized Regression Neural Networks.- Fuzzy Shortest Paths in Fuzzy Graphs.- Improving Vegas Algorithm Using PID and Fuzzy PID Controllers.- A Fuzzy-Based Automation Level Analysis in Irrigation Equipment.- Motorized Skateboard Stabilization Using Fuzzy Controller.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 9th Dortmund Fuzzy Days, Dortmund, Germany, 2006. This conference has established itself as an international forum for the discussion of new results in the field of Computational Intelligence. The papers presented here, all thoroughly reviewed, are devoted to foundational and practical issues in fuzzy systems, neural networks, evolutionary algorithms, and machine learning and thus cover the whole range of computational intelligence.
(source: Nielsen Book Data)

• E-Services and Intelligent Techniques.- E-Service Intelligence: An Introduction.- Rough Sets and Conflict Analysis.- Rough Ontology Mapping in E-Business Integration.- Concept-based Semantic Web Search and Q&A.- E-Service Composition Tools from a Lifecycle Perspective.- Web Information Presentation, Search, and Mining.- Web Information Representation, Extraction, and Reasoning based on Existing Programming Technology.- Techniques and Technologies Behind Maps of Internet and Intranet Document Collections.- Exchange Rate Modelling for E-Negotiators Using Text Mining Techniques.- A Similarity-aware Web Content Caching Scheme and Agentbased Web Document Pre-fetching.- Representation and Discovery of Intelligent E-Services.- Learning the Nonlinear Dynamics of Cyberlearning.- Personalization, Privacy, and Trust in E-Services.- Personalizing E-Commerce with Data Mining.- Personal eBanking Solutions based on Semantic Web Services.- Secure E-Transactions Protocol using Intelligent Mobile Agents with Fair Privacy.- Trust and Reputation in E- ervices: Concepts, Models and Applications.- An Incremental Technique for Analyzing User Behaviors in an E-Business Environment.- Customer Experience Management in E-Services.- E-Service Evaluation, Optimization, and Knowledge Discovery.- E-Service Cost Benefit Evaluation and Analysis.- Evaluation of Experience-based Support for Organizational Employees.- A Web based Intelligent Sensory Evaluation System in the Textile Integrated Supply Chain.- E-intelligence in Portfolio Investment Optimization.- Orchestrating the Knowledge Discovery Process.- On the Knowledge Repository Design and Management in E-Learning.- Adding Value to E-Services: a Business-Oriented Model.- Intelligent E-Service Support System Developments.- Developing a Knowledge-based Intelligent Services System in Sports Websites.- Developing a Model Agent-based E-Commerce System.- Creating Visual Browsers for Large-Scale Online Auctions.- Design and Implementation of Multi-Agents for Learner-oriented Course Scheduling on the Internet.- AGrIP - Agent Grid Intelligence Platform.- Web-based Service Information Systems based on Fuzzy Linguistic Techniques and Semantic Web Technologies.- Application of Chaos-based Pseudo-Random-Bit Generators in Internet-based Online Payments.- Computer Hardware Devices in Efficient E-Servicing: Case Study of Disk Scheduling by Soft Computing.
• (source: Nielsen Book Data)

Business organizations and governments are nowadays developing and providing internet based electronic services (e-services) featuring various intelligent functions. This book offers a thorough introduction and systematic overview of the new field e-service intelligence. It covers the state-of-the-art of e-service intelligence including both theorems and applications, and a broad range of topics are discussed.
(source: Nielsen Book Data)

• A Bayesian Approach to Causal Discovery.- A Tutorial on Learning Causal Influence.- Learning Based Programming.- N-1 Experiments Suffice to Determine the Causal Relations Among N Variables.- Support Vector Inductive Logic Programming.- Neural Probabilistic Language Models.- Computational Grammatical Inference.- On Kernel Target Alignment.- The Structure of Version Space.
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Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.
(source: Nielsen Book Data)

This 1179-page book assembles the complete contributions to the International Conference on Intelligent Computing, ICIC 2006: one volume of Lecture Notes in Computer Science (LNCS); one of Lecture Notes in Artificial Intelligence (LNAI); one of Lecture Notes in Bioinformatics (LNBI); and two volumes of Lecture Notes in Control and Information Sciences (LNCIS). Include are 149 revised full papers, and a Special Session on Computing for Searching Strategies to Control Dynamic Processes.
(source: Nielsen Book Data)

• Knowledge Representation.- Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS.- Use of Learning Classifier System for Inferring Natural Language Grammar.- Backpropagation in Accuracy-Based Neural Learning Classifier Systems.- Binary Rule Encoding Schemes: A Study Using the Compact Classifier System.- Mechanisms.- Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System.- Post-processing Clustering to Decrease Variability in XCS Induced Rulesets.- LCSE: Learning Classifier System Ensemble for Incremental Medical Instances.- Effect of Pure Error-Based Fitness in XCS.- A Fuzzy System to Control Exploration Rate in XCS.- Counter Example for Q-Bucket-Brigade Under Prediction Problem.- An Experimental Comparison Between ATNoSFERES and ACS.- The Class Imbalance Problem in UCS Classifier System: A Preliminary Study.- Three Methods for Covering Missing Input Data in XCS.- New Directions.- A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients.- Adaptive Value Function Approximations in Classifier Systems.- Three Architectures for Continuous Action.- A Formal Relationship Between Ant Colony Optimizers and Classifier Systems.- Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis.- Application-Oriented Research and Tools.- Data Mining in Learning Classifier Systems: Comparing XCS with GAssist.- Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule.- Using XCS to Describe Continuous-Valued Problem Spaces.- The EpiXCS Workbench: A Tool for Experimentation and Visualization.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed joint post-proceedings of three consecutive International Workshops on Learning Classifier Systems that took place in Chicago, IL in July 2003, in Seattle, WA in June 2004, and in Washington, DC in June 2005. Topics in the 22 revised full papers range from theoretical analysis of mechanisms to practical consideration for successful application of such techniques to everyday datamining tasks.
(source: Nielsen Book Data)

• Invited Presentations.- Property Testing: A Learning Theory Perspective.- Spectral Algorithms for Learning and Clustering.- Unsupervised, Semisupervised and Active Learning I.- Minimax Bounds for Active Learning.- Stability of k-Means Clustering.- Margin Based Active Learning.- Unsupervised, Semisupervised and Active Learning II.- Learning Large-Alphabet and Analog Circuits with Value Injection Queries.- Teaching Dimension and the Complexity of Active Learning.- Multi-view Regression Via Canonical Correlation Analysis.- Statistical Learning Theory.- Aggregation by Exponential Weighting and Sharp Oracle Inequalities.- Occam's Hammer.- Resampling-Based Confidence Regions and Multiple Tests for a Correlated Random Vector.- Suboptimality of Penalized Empirical Risk Minimization in Classification.- Transductive Rademacher Complexity and Its Applications.- Inductive Inference.- U-Shaped, Iterative, and Iterative-with-Counter Learning.- Mind Change Optimal Learning of Bayes Net Structure.- Learning Correction Grammars.- Mitotic Classes.- Online and Reinforcement Learning I.- Regret to the Best vs. Regret to the Average.- Strategies for Prediction Under Imperfect Monitoring.- Bounded Parameter Markov Decision Processes with Average Reward Criterion.- Online and Reinforcement Learning II.- On-Line Estimation with the Multivariate Gaussian Distribution.- Generalised Entropy and Asymptotic Complexities of Languages.- Q-Learning with Linear Function Approximation.- Regularized Learning, Kernel Methods, SVM.- How Good Is a Kernel When Used as a Similarity Measure?.- Gaps in Support Vector Optimization.- Learning Languages with Rational Kernels.- Generalized SMO-Style Decomposition Algorithms.- Learning Algorithms and Limitations on Learning.- Learning Nested Halfspaces and Uphill Decision Trees.- An Efficient Re-scaled Perceptron Algorithm for Conic Systems.- A Lower Bound for Agnostically Learning Disjunctions.- Sketching Information Divergences.- Competing with Stationary Prediction Strategies.- Online and Reinforcement Learning III.- Improved Rates for the Stochastic Continuum-Armed Bandit Problem.- Learning Permutations with Exponential Weights.- Online and Reinforcement Learning IV.- Multitask Learning with Expert Advice.- Online Learning with Prior Knowledge.- Dimensionality Reduction.- Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections.- Sparse Density Estimation with ?1 Penalties.- ?1 Regularization in Infinite Dimensional Feature Spaces.- Prediction by Categorical Features: Generalization Properties and Application to Feature Ranking.- Other Approaches.- Observational Learning in Random Networks.- The Loss Rank Principle for Model Selection.- Robust Reductions from Ranking to Classification.- Open Problems.- Rademacher Margin Complexity.- Open Problems in Efficient Semi-supervised PAC Learning.- Resource-Bounded Information Gathering for Correlation Clustering.- Are There Local Maxima in the Infinite-Sample Likelihood of Gaussian Mixture Estimation?.- When Is There a Free Matrix Lunch?.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 20th Annual Conference on Learning Theory, COLT 2007, held in San Diego, CA, USA in June 2007. It covers unsupervised, semisupervised and active learning, statistical learning theory, inductive inference, regularized learning, kernel methods, SVM, online and reinforcement learning, learning algorithms and limitations on learning, dimensionality reduction, as well as open problems.
(source: Nielsen Book Data)

• Multi-Objective Clustering, Feature Extraction and Feature Selection.- Feature Selection Using Rough Sets.- Multi-Objective Clustering and Cluster Validation.- Feature Selection for Ensembles Using the Multi-Objective Optimization Approach.- Feature Extraction Using Multi-Objective Genetic Programming.- Multi-Objective Learning for Accuracy Improvement.- Regression Error Characteristic Optimisation of Non-Linear Models.- Regularization for Parameter Identification Using Multi-Objective Optimization.- Multi-Objective Algorithms for Neural Networks Learning.- Generating Support Vector Machines Using Multi-Objective Optimization and Goal Programming.- Multi-Objective Optimization of Support Vector Machines.- Multi-Objective Evolutionary Algorithm for Radial Basis Function Neural Network Design.- Minimizing Structural Risk on Decision Tree Classification.- Multi-objective Learning Classifier Systems.- Multi-Objective Learning for Interpretability Improvement.- Simultaneous Generation of Accurate and Interpretable Neural Network Classifiers.- GA-Based Pareto Optimization for Rule Extraction from Neural Networks.- Agent Based Multi-Objective Approach to Generating Interpretable Fuzzy Systems.- Multi-objective Evolutionary Algorithm for Temporal Linguistic Rule Extraction.- Multiple Objective Learning for Constructing Interpretable Takagi-Sugeno Fuzzy Model.- Multi-Objective Ensemble Generation.- Pareto-Optimal Approaches to Neuro-Ensemble Learning.- Trade-Off Between Diversity and Accuracy in Ensemble Generation.- Cooperative Coevolution of Neural Networks and Ensembles of Neural Networks.- Multi-Objective Structure Selection for RBF Networks and Its Application to Nonlinear System Identification.- Fuzzy Ensemble Design through Multi-Objective Fuzzy Rule Selection.- Applications of Multi-Objective Machine Learning.- Multi-Objective Optimisation for Receiver Operating Characteristic Analysis.- Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination.- Fuzzy Tuning for the Docking Maneuver Controller of an Automated Guided Vehicle.- A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments.- Multi-Objective Neural Network Optimization for Visual Object Detection.
• (source: Nielsen Book Data)

Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.
(source: Nielsen Book Data)

• Kernel-Based Fusion.- Combining Pattern Recognition Modalities at the Sensor Level Via Kernel Fusion.- The Neutral Point Method for Kernel-Based Combination of Disjoint Training Data in Multi-modal Pattern Recognition.- Kernel Combination Versus Classifier Combination.- Deriving the Kernel from Training Data.- Applications.- On the Application of SVM-Ensembles Based on Adapted Random Subspace Sampling for Automatic Classification of NMR Data.- A New HMM-Based Ensemble Generation Method for Numeral Recognition.- Classifiers Fusion in Recognition of Wheat Varieties.- Multiple Classifier Methods for Offline Handwritten Text Line Recognition.- Applying Data Fusion Methods to Passage Retrieval in QAS.- A Co-training Approach for Time Series Prediction with Missing Data.- An Improved Random Subspace Method and Its Application to EEG Signal Classification.- Ensemble Learning Methods for Classifying EEG Signals.- Confidence Based Gating of Colour Features for Face Authentication.- View-Based Eigenspaces with Mixture of Experts for View-Independent Face Recognition.- Fusion of Support Vector Classifiers for Parallel Gabor Methods Applied to Face Verification.- Serial Fusion of Fingerprint and Face Matchers.- Boosting.- Boosting Lite - Handling Larger Datasets and Slower Base Classifiers.- Information Theoretic Combination of Classifiers with Application to AdaBoost.- Interactive Boosting for Image Classification.- Cluster and Graph Ensembles.- Group-Induced Vector Spaces.- Selecting Diversifying Heuristics for Cluster Ensembles.- Unsupervised Texture Segmentation Using Multiple Segmenters Strategy.- Classifier Ensembles for Vector Space Embedding of Graphs.- Cascading for Nominal Data.- Feature Subspace Ensembles.- A Combination of Sample Subsets and Feature Subsets in One-Against-Other Classifiers.- Random Feature Subset Selection for Ensemble Based Classification of Data with Missing Features.- Feature Subspace Ensembles: A Parallel Classifier Combination Scheme Using Feature Selection.- Stopping Criteria for Ensemble-Based Feature Selection.- Multiple Classifier System Theory.- On Rejecting Unreliably Classified Patterns.- Bayesian Analysis of Linear Combiners.- Applying Pairwise Fusion Matrix on Fusion Functions for Classifier Combination.- Modelling Multiple-Classifier Relationships Using Bayesian Belief Networks.- Classifier Combining Rules Under Independence Assumptions.- Embedding Reject Option in ECOC Through LDPC Codes.- Intramodal and Multimodal Fusion of Biometric Experts.- On Combination of Face Authentication Experts by a Mixture of Quality Dependent Fusion Classifiers.- Index Driven Combination of Multiple Biometric Experts for AUC Maximisation.- Q???stack: Uni- and Multimodal Classifier Stacking with Quality Measures.- Reliability-Based Voting Schemes Using Modality-Independent Features in Multi-classifier Biometric Authentication.- Optimal Classifier Combination Rules for Verification and Identification Systems.- Majority Voting.- Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets.- On the Diversity-Performance Relationship for Majority Voting in Classifier Ensembles.- Hierarchical Behavior Knowledge Space.- Ensemble Learning.- A New Dynamic Ensemble Selection Method for Numeral Recognition.- Ensemble Learning in Linearly Combined Classifiers Via Negative Correlation.- Naive Bayes Ensembles with a Random Oracle.- An Experimental Study on Rotation Forest Ensembles.- Cooperative Coevolutionary Ensemble Learning.- Robust Inference in Bayesian Networks with Application to Gene Expression Temporal Data.- An Ensemble Approach for Incremental Learning in Nonstationary Environments.- Invited Papers.- Multiple Classifier Systems in Remote Sensing: From Basics to Recent Developments.- Biometric Person Authentication Is a Multiple Classifier Problem.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 7th International Workshop on Multiple Classifier Systems, MCS 2007, held in Prague, Czech Republic in May 2007. It covers kernel-based fusion, applications, boosting, cluster and graph ensembles, feature subspace ensembles, multiple classifier system theory, intramodal and multimodal fusion of biometric experts, majority voting, and ensemble learning.
(source: Nielsen Book Data)

• Keynotes
• Text processing
• [Special] Fuzzy system applications
• Vision
• [Special] Real world interaction
• Genetic algorithm
• Robot
• Poster [session]
• Fuzzy logic
• Manufacturing
• Data mining
• Neural network
• Constraint satisfaction
• Machine learning
• [Special] Chance discovery and social network
• Education
• Speech
• [Special] E-commerce
• Heuristic search
• Application system
• Agent-based system
• Other applications.

This book constitutes the refereed proceedings of the 20th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2007, held in Kyoto, Japan. Coverage includes text processing, fuzzy system applications, real-world interaction, data mining, machine learning chance discovery and social networks, e-commerce, heuristic search application systems, and other applications.
(source: Nielsen Book Data)

• Learning and Neural Nets.- Detection of Breast Lesions in Medical Digital Imaging Using Neural Networks.- Identification of Velocity Variations in a Seismic Cube Using Neural Networks.- Improving the k-NN method: Rough Set in edit training set.- Agents.- Membranes as Multi-agent Systems: an Application to Dialogue Modelling.- Agent Planning, Models, Virtual Haptie Computing, and Visual Ontology.- Improving Interoperability Among Learning Objects Using FIPA Agent Communication Framework.- An Agent-Oriented Programming Language for Computing in Context.- Search.- A Little Respect (for the Role of Common Components in Heuristic Search).- Recursive and Iterative Algorithms for N-ary Search Problems.- Ontologies and Intelligent Web.- Process of Ontology Construction for the Development of an Intelligent System for the Organization and Retrieval of Knowledge in Biodiversity - SISBIO.- Service interdependencies: insight into use cases for service composition.- Combining Contexts and Ontologies: A Case Study.- The RR Project - A Framework for Relationship Network Viewing and Management.- Web Service-based Business Process Automation Using Matching Algorithms.- A Graphic Tool for Ontology Viewing Based on Graph Theory.- Knowledge Engineering.- Using Competence Modeling to create Knowledge Engineering Team.- Intelligent Systems Engineering with Reconfigurable Computing.- Ontological Evaluation in the Knowledge Based System.- A Model for Concepts Extraction and Context Identification in Knowledge Based Systems.- Proposal of Fuzzy Object Oriented Model in Extended JAVA.- Knowledge Discovery.- Adopting Knowledge Discovery in Databases for Customer Relationship Management in Egyptian Public Banks.- Pattern Discovery and Model Construction: an Evolutionary Learning and Data Mining Approach.- Towards a Framework for Knowledge Discovery.- Language Processing.- Prototype Of Speech Translation System For Audio Effective Communication.- A Knowledge Representation Semantic Network for a Natural Language Syntactic Analyzer Based on the UML.- Applications.- Fast simulation of animal locomotion: lamprey swimming.- Towards a case-based reasoning approach to analyze road accidents.- Solving the Short Run Economic Dispatch Problem Using Concurrent Constraint Programming.- Micromechanics as a Testbed for Artificial Intelligence Methods Evaluation.- Burst Synchronization in Two Pulse-Coupled Resonate-and-Fire Neuron Circuits.- Ant Colonies using Arc Consistency Techniques for the Set Partitioning Problem.- Automatic Query Recommendation using Click-Through Data.- A Shared-Memory Multiprocessor Scheduling Algorithm.- Web Attack Detection Using ID3.- A Statistical Sampling Strategy for Iris Recognition.- An Application of ARX Stochastic Models to Iris Recognition.
• (source: Nielsen Book Data)

The Second Symposium on Professional Practice in AI 2006 is a conference within the IFIP World Computer Congress 2006, Santiago, Chile. The Symposium is organised by the IFIP Technical Committee on Artificial Intelligence (Technical Committee 12) and its Working Group 12.5 (Artificial Intelligence Applications). The First Symposium in this series was one of the conferences in the IFIP World Computer Congi-ess 2004, Toulouse France. The conference featured invited talks by Rose Dieng, John Atkinson, John Debenham and Max Bramer. The Symposium was a component of the IFIP AI 2006 conference, organised by Professor Max Bramer. I should like to thank the Symposium General Chair, Professor Bramer for his considerable assistance in making the Symposium happen within a very tight deadline. These proceedings are the result of a considerable amount of hard work. Beginning with the preparation of the submitted papers, the papers were each reviewed by at least two members of the international Program Committee. The authors of accepted papers then revised their manuscripts to produce their final copy. The hard work of the authors, the referees and the Program Committee is gratefully aclaiowledged. The IFIP AI 2006 conference and the Symposium are the latest in a series of conferences organised by IFIP Technical Committee 12 dedicated to the techniques of Aitificial Intelligence and their real-world applications. Further infoirmation about TC12 can be found on our website http; //www.ifiptcI2.org.
(source: Nielsen Book Data)

• Preface Acknowledgment PART I. Theoretical Foundation
• Chapter 1. The Goal of Artificial Intelligence 1.1 To define intelligence 1.2 Various schools in AI research 1.3 AI as a whole
• Chapter 2. A New Approach Toward AI 2.1 To define AI 2.2 Intelligent reasoning systems 2.3 Major design issues of NARS PART II. Non-Axiomatic Reasoning System
• Chapter 3. The Core Logic 3.1 NAL-0: binary inheritance 3.2 The language of NAL-1 3.3 The inference rules of NAL-1
• Chapter 4. First-Order Inference 4.1 Compound terms 4.2 NAL-2: sets and variants of inheritance 4.3 NAL-3: intersections and differences 4.4 NAL-4: products, images, and ordinary relations
• Chapter 5. Higher-Order Inference 5.1 NAL-5: statements as terms 5.2 NAL-6: statements with variables 5.3 NAL-7: temporal statements 5.4 NAL-8: procedural statements
• Chapter 6. Inference Control 6.1 Task management 6.2 Memory structure 6.3 Inference processes 6.4 Budget assessment . PART III. Comparison and Discussion
• Chapter 7. Semantics 7.1 Experience vs. model 7.2 Extension and intension 7.3 Meaning of term 7.4 Truth of statement
• Chapter 8. Uncertainty 8.1 The non-numerical approaches 8.2 The fuzzy approach 8.3 The Bayesian approach 8.4 Other probabilistic approaches 8.5 Unified representation of uncertainty
• Chapter 9. Inference Rules 9.1 Deduction 9.2 Induction 9.3 Abduction 9.4 Implication
• Chapter 10. NAL as a Logic 10.1 NAL as a term logic 10.2 NAL vs. predicate logic 10.3 Logic and AI
• Chapter 11. Categorization and Learning 11.1 Concept and categorization 11.2 Learning in NARS
• Chapter 12. Control and Computation 12.1 NARS and theoretical computer science 12.2 Various assumptions about resources 12.3 Dynamic natures of NARS PART IV. Conclusions
• Chapter 13. Current Results 13.1 Theoretical foundation 13.2 Formal model 13.3 Computer implementation
• Chapter 14. NARS in the Future 14.1 Next steps of the project 14.2 What NARS is not 14.3 General implications Bibliography Index.
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This book is the most comprehensive description of the decades-long Non-Axiomatic Reasoning System (NARS) project, including its philosophical foundation, methodological consideration, conceptual design details, implications in the related fields, and its similarities and differences to many related works in cognitive science. While most current works in Artificial Intelligence (AI) focus on individual aspects of intelligence and cognition, NARS is designed and developed to attack the AI problem as a whole.
(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.
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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.
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• Methodologies Based on Particle Swarm Intelligence.- Swarm Intelligence: Foundations, Perspectives and Applications.- Waves of Swarm Particles (WoSP).- Grammatical Swarm: A Variable-Length Particle Swarm Algorithm.- SWARMs of Self-Organizing Polymorphic Agents.- Experiences Using Particle Swarm Intelligence.- Swarm Intelligence - Searchers, Cleaners and Hunters.- Ant Colony Optimisation for Fast Modular Exponentiation using the Sliding Window Method.- Particle Swarm for Fuzzy Models Identification.- A Matlab Implementation of Swarm Intelligence based Methodology for Identification of Optimized Fuzzy Models.
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Systems designers have learned that many agents co-operating within the system can solve very complex problems with a minimal design effort. In general, multi-agent systems that use swarm intelligence are said to be swarm intelligent systems. Today, these are mostly used as search engines and optimization tools. This volume reviews innovative methodologies of swarm intelligence, outlines the foundations of engineering swarm intelligent systems and applications, and relates experiences using the particle swarm optimisation.
(source: Nielsen Book Data)