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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.
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• (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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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)