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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.
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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)