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• PART 1, Data mining, machine learning, knowledge discovery, Tensor Networks for Dimensionality Reduction, Big Data and Deep Learning.- Local Data Characteristics in Learning Classifiers from Imbalanced Data.- Similarity dimensions of semantic ontologies.- Some interesting phenomenon occurring during self-learning process with its psychological interpretation.- PART 2, Neural networks and connectionist systems, On the interpretation and characterization of echo state networks dynamics: A complex systems perspective.- Optimization of Ensemble Neural Networks with Type-1 and Interval Type-2 Fuzzy Integration for Forecasting the Taiwan Stock Exchange.- Deep Neural Networks - A Brief History.- PART 3, Intelligent technologies in systems modeling, Techniques for Construction and Integration of Rule Bases.- New Aspects of Interpretability of Fuzzy Systems for Nonlinear Modeling.- On the intuitionistic fuzzy sets of n-th type.-
• PART 4. Intelligent technologies in decision making, optimization and control, MCTS/UCT in solving real-life problems.- Interactive cone contraction for evolutionary mutliple objective optimization.- A review of fuzzy and mathematic methods for dynamic parameter adaptation in the firefly algorithm.-
• PART 5. Applications of intelligent technologies Computational Intelligence Methods in Personalized Pharmacotherapy.- Embodying Intelligence in Autonomous and Robotic Systems with the Use of Cognitive Psychology and Motivation Theories.- Evolutionary Approach for Automatic Design of PID Controllers.- Fuzzy-genetic approach to identity verification using a handwritten signature.- A method of design and optimization for SiC-based grid-connected AC-DC converters.
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This book is a tribute to Professor Jacek Zurada, who is best known for his contributions to computational intelligence and knowledge-based neurocomputing. It is dedicated to Professor Jacek Zurada, Full Professor at the Computational Intelligence Laboratory, Department of Electrical and Computer Engineering, J.B. Speed School of Engineering, University of Louisville, Kentucky, USA, as a token of appreciation for his scientific and scholarly achievements, and for his longstanding service to many communities, notably the computational intelligence community, in particular neural networks, machine learning, data analyses and data mining, but also the fuzzy logic and evolutionary computation communities, to name but a few. At the same time, the book recognizes and honors Professor Zurada's dedication and service to many scientific, scholarly and professional societies, especially the IEEE (Institute of Electrical and Electronics Engineers), the world's largest professional technical professional organization dedicated to advancing science and technology in a broad spectrum of areas and fields. The volume is divided into five major parts, the first of which addresses theoretic, algorithmic and implementation problems related to the intelligent use of data in the sense of how to derive practically useful information and knowledge from data. In turn, Part 2 is devoted to various aspects of neural networks and connectionist systems. Part 3 deals with essential tools and techniques for intelligent technologies in systems modeling and Part 4 focuses on intelligent technologies in decision-making, optimization and control, while Part 5 explores the applications of intelligent technologies.
(source: Nielsen Book Data)

• Business intelligence and customer relationship management
• Data mining in medicine and system biology
• Aspects of data mining
• Data mining in finance
• Text and document mining
• Data mining in environment.

This book constitutes the refereed proceedings of the 15th Industrial Conference on Advances in Data Mining, ICDM 2015, held in Hamburg, Germany, in July 2015. The 16 revised full papers presented were carefully reviewed and selected from numerous submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine and agriculture, and in process control, industry and society.

• Data Mining
• Multimedia Data in Marketing. Medicine
• E-Commerce
• Web-Mining
• Multimedia Data.

This book constitutes the refereed proceedings of the 16th Industrial Conference on Advances in Data Mining, ICDM 2016, held in New York, NY, USA, in July 2016. The 33 revised full papers presented were carefully reviewed and selected from 100 submissions. The topics range from theoretical aspects of data mining to applications of data mining, such as in multimedia data, in marketing, in medicine, and in process control, industry, and society.

• Invited Papers.- Information extraction and Integration.- Data Mining and Knowledge Discovery.- Indexing, Query Processing and Optimization.- Data Quality and Data Cleansing.- Distributed Data Platforms, Including Cloud Data Systems, Key-Value Stores, and Big Data Systems.- Streaming Data Analysis.- Web, XML and Semi-Structured Databases.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 22nd European Conference on Advances in Databases and Information Systems, ADBIS 2018, held in Budapest, Hungary, in September 2018.The 17 regular papers presented together with two invited papers were carefully selected and reviewed from numerous submissions. The papers are organized in topical sections such as information extraction and integration; data mining and knowledge discovery; indexing, query processing and optimization; data quality and data cleansing; distributed data platforms, including cloud data systems, key-value stores, and big data systems; and streaming data analysis; web, XML and semi-structured databases.
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• Keynote Extended Abstracts.- Blockchains and Databases: Opportunities and Challenges for the Permissioned and the Permissionless.- Processing Temporal and Time Series Data: Present State and Future Challenges.- Integrating (Very) Heterogeneous Data Sources: a Structured and an Unstructured Perspective.- Data Access and Database Performance.- Upper Bound on the Size of FP-tree.- An Efficient Index for Reachability Queries in Public Transport Networks.- Context-Free Path Querying by Kronecker Product.- Pattern Sampling in Distributed Databases.-Can We Probabilistically Generate Uniformly Distributed Relation Instances Efficiently?.- Machine Learning.- Towards Proximity Graph Auto-Configuration: an Approach Based on Meta-Learning.- Fake News Detection Based on Subjective Opinions.- Improving on Coalitional Prediction Explanation.- Data Processing.- JSON Functionally.- Semantic Web.- Template-Based Multi-Solution for Data-to-Text Generation (on RDF).- Distributed Tree-Pattern Matching in Big Data Analytics Systems.- Data Analytics.- Iterations and Propensity Score Matching in MonetDB.- The Tell-Tale Cube.
• (source: Nielsen Book Data)

This book constitutes the proceedings of the 24th European Conference on Advances in Databases and Information Systems, ADBIS 2020, held in Lyon, France, in August 2020.* The 13 full papers presented were carefully reviewed and selected from 82 submissions. The papers cover a wide range of topics from different areas of research in database and information systems technologies and their advanced applications from theoretical foundations to optimizing index structures. They are organized in the following topical sections: keynote extended abstracts; data access and database performance; machine learning; semantic web; data processing; and data analytics. * The conference was held virtually due to the COVID-19 pandemic.
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• Fuzzy Numbers.- Ordering of Fuzzy Numbers.- Fuzzy Random Variable and the Dempster-Shafer Theory of Evidence.- Multi-Attribute Decision Making Process and its Application.- Risk Assessment in the Presence of Uncertainty.- Application of Fuzzy Theory in Steel Production Planning and Scheduling.- Application of Fuzzy Decision Trees in Analog Forecasting.- Selected Issues of Visualisation of Fuzziness in Cardiac Imaging Data.
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• 2.4 Yager Ranking Index Approach2.5 Degree of Possibility Approach; 2.6 Weighted Averaging Approach Based on α--cuts; 2.7 Two-Dimensional Radius of Gyration Approach; 2.8 Fuzzy Maximising-Minimising Points Approach; 2.9 Area Based Approach; 2.10 Left and Right Dominance Approach; 2.11 An α-weighted Valuations Approach; References; 3 Fuzzy Random Variable and the Dempster-Shafer Theory of Evidence; 3.1 Fuzzy Random Variable; 3.2 Dempster--Shafer (D--S) Theory of Evidence; 3.3 The Hybrid Data Propagation Method; References; 4 Multi-attribute Decision Making Process and Its Application

• Intro; Preface; Contents; 1 Fuzzy Numbers; 1.1 Preliminary Theory; 1.1.1 LR--type Fuzzy Numbers; 1.1.2 T-Operators; 1.2 Arithmetic of Fuzzy Numbers; 1.2.1 Zadeh's Extension Principle; 1.2.2 α--cuts Based Operations; 1.2.3 Shape-Preserving Operations; 1.2.4 Examples; 1.3 Modelling Dependencies Between Fuzzy Numbers; 1.3.1 Modelling Stochastic Dependencies; 1.3.2 Interval Regression; 1.4 Possibility and Credibility Theory; 1.4.1 Non-linear Programming Approach; References; 2 Ordering of Fuzzy Numbers; 2.1 Probabilistic Approach; 2.2 Defuzzification Approach; 2.3 Centroid-Point Approach

This book shows how common operation management methods and algorithms can be extended to deal with vague or imprecise information in decision-making problems. It describes how to combine decision trees, clustering, multi-attribute decision-making algorithms and Monte Carlo Simulation with the mathematical description of imprecise or vague information, and how to visualize such information. Moreover, it discusses a broad spectrum of real-life management problems including forecasting the apparent consumption of steel products, planning and scheduling of production processes, project portfolio selection and economic-risk estimation. It is a concise, yet comprehensive, reference source for researchers in decision-making and decision-makers in business organizations alike.
(source: Nielsen Book Data)

• Contributors
• Acknowledgments
• 1. An Introduction to the Third Volume / Lee Spector, William B. Langdon, Una-May O'Reilly, Peter J. Angelino
• I. Applications
• 2. An Automatic Software Re-Engineering Tool Based on Genetic Programming / Conor Ryan and Laur Ivan
• 3. CAD Surface Reconstruction from Digitized 3D Point Data with a Genetic Programming/Evolution Strategy Hybrid / Robert E. Keller, Wolfgang Banzhaf, Jorn Mehnen and Klaus Weinert
• 4. A Genetic Programming Approach for Robust Language Interpretation / Carolyn Penstein Rose
• 5. Time Series Modeling Using Genetic Programming: An Application to Rainfall-Runoff Models / Peter A. Whigham and Peter F. Crapper
• 6. Automatic Synthesis, Placement, and Routing of Electrical Circuits by Means of Genetic Programming / John R. Koza and Forest H. Bennett III
• 7. Quantum Computing Applications of Genetic Programming / Lee Spector, Howard Barnum, Herbert J. Bernstein and Nikhil Swamy
• II. Theory
• II. Theory
• 8. The Evolution of Size and Shape / William B. Langdon, Terry Soule, Riccardo Poli and James A. Foster
• 9. Fitness Distributions: Tools for Designing Efficient Evolutionary Computations / Christian Igel and Kumar Chellapilla
• 10. Analysis of Single-Node (Building) Blocks in Genetic Programming / Jason M. Daida, Robert R. Bertram, John A. Polito 2 and Stephen A. Stanhope
• 11. Rooted-Tree Schemata in Genetic Programming / Justinian P. Rosca and Dana H. Ballard
• III. Extensions
• III. Extensions
• 12. Efficient Evolution of Machine Code for CISC Architectures Using Instruction Blocks and Homologous Crossover / Peter Nordin, Wolfgang Banzhaf and Frank D. Francone
• 13. Sub-machine-code Genetic Programming / Riccardo Poli and William B. Langdon
• 14. The Internal Reinforcement of Evolving Algorithms / Astro Teller
• 15. Inductive Genetic Programming with Immune Network Dynamics / Nikolay I. Nikolaev, Hitoshi Iba and Vanio Slavov
• 16. A Self-Tuning Mechanism for Depth-Dependent Crossover / Takuyo Ito, Hitoshi Iba and Satoshi Sato
• 17. Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series / Geum Yong Lee
• 18. Co-evolutionary Fitness Switching: Learning Complex Collective Behaviors Using Genetic Programming / Byoung-Tak Zhang and Dong-Yeon Cho
• 19. Evolving Multiple Agents by Genetic Programming / Hitoshi Iba
• Index.

Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field. Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field.
(source: Nielsen Book Data)
Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time -- and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of "Advances in Genetic Programming" highlights many of the recent technical advances in this increasingly popular field.
(source: Nielsen Book Data)

• A perspective on the work in this book / Kenneth E. Kinnear, Jr.
• Introduction to genetic programming / John R. Koza
• The evolution of evolvability in genetic programming / Lee Altenberg
• Genetic programming and emergent intelligence / Peter J. Angeline
• Scalable learning in genetic programming using automatic function definition / John R. Koza
• Alternatives in automatic function definition : a comparison of performance / Kenneth E. Kinnear, Jr.
• The donut problem : scalability, generalization and breeding policies in genetic programming / Walter Alden Tackett, Aviram Carmi
• Effects of locality in individual and population evolution / Patrik D'haeseleer, Jason Bluming
• The evolution of mental models / Astro Teller
• Evolution of obstacle avoidance behavior : using noise to promote robust solutions / Craig W. Reynolds
• Pygmies and civil servants / Conor Ryan
• Genetic programming using a minimum decsription length principle / Hitoshi Iba, Hugo de Garis, Taisuke Sato
• Genetic programming in C++: implementation issues / Mike J. Keith, Martin C. Martin. A compiling genetic programming system that directly manipulates the machine code / Peter Nordin
• Automatic generation of programs for crawling and walking / Graham Spencer
• Genetic programming for the acquisition of double auction market strategies / Martin Andrews, Richard Prager
• Two scientific applications of genetic programming : stack filters and non-linear equation fitting to chaotic data / Howard Oakley
• The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions / Simon G. Handley
• Competitively evolving decision trees against fixed training cases for natural language processing / Eric V. Siegel
• Cracking and co-evolving randomizers / Jan Jannink
• Optimizing confidence of text classification by evolution of symbolic expressions / Brij Masand
• Evolvable 3D modeling for model-based object recognition systems / Thang Nguyen, Thomas Huang
• Automatically defined features : the simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them / David Andre
• Genetic micro programming of neural networks / Frédéric Gruau.

Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in manu of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public-domain code is available, and on how to become part of the active genetic programming community via electronic mail.
(source: Nielsen Book Data)
There is increasing interest in genetic programming by both researchers and professional software developers. These twenty-two invited contributions show how a wide variety of problems across disciplines can be solved using this new paradigm. Advances in Genetic Programming reports significant results in improving the power of genetic programming, presenting techniques that can be employed immediately in the solution of complex problems in many areas, including machine learning and the simulation of autonomous behavior. Popular languages such as C and C++ are used in many of the applications and experiments, illustrating how genetic programming is not restricted to symbolic computing languages such as LISP. Researchers interested in getting started in genetic programming will find information on how to begin, on what public domain code is available, and on how to become part of the active genetic programming community via electronic mail. A major focus of the book is on improving the power of genetic programming. Experimental results are presented in a variety of areas, including adding memory to genetic programming, using locality and "demes" to maintain evolutionary diversity, avoiding the traps of local optima by using coevolution, using noise to increase generality, and limiting the size of evolved solutions to improve generality. Significant theoretical results in the understanding of the processes underlying genetic programming are presented, as are several results in the area of automatic function definition. Performance increases are demonstrated by directly evolving machine code, and implementation and design issues for genetic programming in C++ are discussed.
(source: Nielsen Book Data)

• A Multi-level Hierarchical Approach for Configuring Business Processes
• Profiling simulation performance: The example of the German toll system
• Analysis of Content of Posts and Comments in Evolving Social Groups
• Generation of Hierarchical Business Process Models from Attribute Relationship Diagrams
• Nonlinear Time Structures and Nonlinear Time Ontology for Description of Economic Phenomena
• Business Intelligence and Analytics in Organizations
• Low-frequency Signal Reconstruction and Abrupt Change Detection in Non-Stationary Time Series by Enhanced Moving Trend Based Filters
• The Assessment of the EPQ Parameter for Detecting H-Index Manipulation and the Analysis of Scientific Publications
• Fuzzy Multi-attribute Evaluation of Investments
• Theory of digital data processing in the ICT
• The opportunities and challenges connected with implementation of the Big Data concept.

This contributed volume is a result of discussions held at ABICT?13(4th International Workshop on Advances in Business ICT) in Krakow, September 8-11, 2013. The book focuses on Advances in Business ICT approached from a multidisciplinary perspective and demonstrates different ideas and tools for developing and supporting organizational creativity, as well as advances in decision support systems. This book is an interesting resource for researchers, analysts and IT professionals including software designers. The book comprises eleven chapters presenting research results on business analytics in organization, business processes modeling, problems with processing big data, nonlinear time structures and nonlinear time ontology application, simulation profiling, signal processing (including change detection problems), text processing and risk analysis.

• Image processing technology
• Image analysis and understanding
• Computer vision and pattern recognition
• Big data mining, computer graphics and VR.-Image technology application.

This book constitutes the refereed proceedings of the 11th Chinese Conference on Image and Graphics Technologies and Applications, IGTA 2016, held in Beijing, China in July 2016. The 27 papers presented were carefully reviewed and selected from 69 submissions. They provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR; as well as image technology applications.

• System security
• Searchable encryption
• Cryptanalysis
• Permutation and symmetric encryption
• Privacy preserving
• Hardware security
• Post-quantum cryptography
• Paring computation.

This book constitutes the refereed proceedings of the 11th International Workshop on Security, IWSEC 2016, held in Tokyo, Japan, in September 2016. The 15 regular papers and 4 short papers presented in this volume were carefully reviewed and selected from 53 submissions. They were organized in topical sections named: system security; searchable encryption; cryptanalysis; permutation and symmetric encryption; privacy preserving; hardware security; post-quantum cryptography; and paring computation.

• Symmetric-Key Cryptography 1.- Malicious Activity Detection 1.- Multiparty Computation.- Malicious Activity Detection 2.- Post Quantum Cryptography.- Symmetric-Key Cryptography 2.- Privacy.- Pairing-Based Cryptography.- Machine Learning.
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This book constitutes the refereed proceedings of the 15th International Workshop on Security, IWSEC 2020, held in Fukui, Japan, in September 2020.* The 15 regular papers and 1 short paper presented in this volume were carefully reviewed and selected from 47 submissions. They were organized in topical sections named: symmetric-key cryptography, malicious activity detection, multiparty computation, post quantum cryptography, privacy, pairing-based cryptography, and machine learning. * The conference was held virtually due to the COVID-19 pandemic.
(source: Nielsen Book Data)

• Topic Modelling.- Health Applications.- Deep Learning.- Evulations and user behavoir.- Representation.- Recommendation.- Retrieval.- Learning and Classification.- Microblogs. .
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This book constitutes the refereed proceedings of the 40th European Conference on IR Research, ECIR 2018, held in Grenoble, France, in March 2018. The 39 full papers and 39 short papers presented together with 6 demos, 5 workshops and 3 tutorials, were carefully reviewed and selected from 303 submissions. Accepted papers cover the state of the art in information retrieval including topics such as: topic modeling, deep learning, evaluation, user behavior, document representation, recommendation systems, retrieval methods, learning and classication, and micro-blogs.
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• Childrens Age and Gender Recognition from Raw Speech Waveform using DNN
• Inter-Vehicular Communication in Urban Traffic Scenario
• Distributed Estimation of IIR Systems Parameter in Sensor Network using Multihop Diffusion LMS Algorithm
• Implementation of Raspberry Pi for fault detection in optic fibre line
• Distributed Optical Fiber Sensing System Performance Improvement using Signal Processing Techniques.

This book features high-quality research papers presented at the 2nd International Conference on Intelligent Computing and Advances in Communication (ICAC 2019), held at Siksha 'O Anusandhan Deemed to be University, Bhubaneswar, Odisha, India, in November 2019. Covering a wide variety of topics, including management of clean and smart energy systems and environmental challenges, it is a valuable resource for researchers and practicing engineers working in various fields of renewable energy generation, and clean and smart energy management.

• Data analytics and optimization for assessing a ride sharing system.- Constraint-Based Querying for Bayesian Network Exploration.- Efficient Model Selection for Regularized Classification by Exploiting Unlabeled Data.- Segregation Discovery in a Social Network of Companies.- A first+-order-logic based model for grounded language learning.- A parallel distributed processing algorithm for image feature extraction.- Modeling concept drift: A probabilistic graphical model based approach.- Diversity-driven Widening of Hierarchical Agglomerative Clustering.- Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering.- Time Series Classification with Representation Ensembles.- Simultaneous Clustering and Model Selection for Multinomial Distribution: A Comparative Study.- On Binary Reduction of Large-scale Multiclass Classification Problems.- Probabilistic Active Learning in Data Streams.- Implicitly Constrained Semi-Supervised Least Squares Classification.- Diagonal Co-clustering Algorithm for Document-Word Partitioning.- I-Louvain: an attributed graph clustering method.- Class-based outlier detection: staying zombies or awaiting for resurrection?.- Using Metalearning for Prediction of Taxi Trip Duration Using Different Granularity Levels.- Using entropy as a measure of acceptance for multi-label classification.- Investigation of Node Deletion Techniques for Clustering Applications of Growing Self Organizing Maps.- Exploratory topic modeling with distributional semantics.- Assigning Geo-Relevance of Sentiments Mined from Location-Based Social Media Posts.- Continuous and Discrete Deep Classifiers for Data Integration.- A Bayesian Approach for Identifying Multivariate Differences Between Groups.- Automatically Discovering Offensive Patterns in Soccer Match Data.- Fast Algorithm Selection using Learning Curves.- Optimally Weighted Cluster Kriging for Big Data Regression.- Slower can be faster: The iRetis incremental model tree learner.- VoQs: A Web Application for Visualization of Questionnaire Surveys.
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This book constitutes the refereed conference proceedings of the 14th International Conference on Intelligent Data Analysis, which was held in October 2015 in Saint Etienne. France. The 29 revised full papers were carefully reviewed and selected from 65 submissions. The traditional focus of the IDA symposium series is on end-to-end intelligent support for data analysis. The symposium aims to provide a forum for inspiring research contributions that might be considered preliminary in other leading conferences and journals, but that have a potentially dramatic impact. To facilitate this, IDA 2015 will feature two tracks: a regular "Proceedings" track, as well as a "Horizon" track for early-stage research of potentially ground-breaking nature.
(source: Nielsen Book Data)

• DSCo-NG: A Practical Language Modeling Approach for Time Series Classification
• Ranking Accuracy for Logistic-GEE models
• The Morality Machine: Tracking Moral Values in Tweets
• A Hybrid Approach for Probabilistic Relational Models Structure Learning
• On the Impact of Data Set Size in Transfer Learning Using Deep Neural Networks
• Obtaining Shape Descriptors from a Concave Hull-Based Clustering Algorithm
• Visual Perception of Discriminative Landmarks in Classified Time Series
• Spotting the Diffusion of New Psychoactive Substances over the Internet
• Feature Selection Issues in Long-Term Travel Time Prediction
• A Mean-Field Variational Bayesian Approach to Detecting Overlapping Communities with Inner Roles Using Poisson Link Generation
• Online Semi-supervised Learning for Multi-target Regression in Data streams Using AMRules
• A Toolkit for Analysis of Deep Learning Experiments
• The Optimistic Method for Model Estimation
• Does Feature Selection Improve Classification? A Large Scale Experiment in OpenML
• Learning from the News: Predicting Entity Popularity on Twitter
• Multi-scale Kernel PCA and Its Application to Curvelet-based Feature Extraction for Mammographic Mass Characterization
• Weakly-supervised Symptom Recognition for Rare Diseases in Biomedical Text
• Estimating Sequence Similarity from Read Sets for Clustering Sequencing Data
• Widened Learning of Bayesian Network Classifiers
• Vote Buying Detection via Independent Component Analysis
• Unsupervised Relation Extraction in Specialized Corpora Using Sequence Mining
• A Framework for Interpolating Scattered Data Using Space-filling Curves
• Privacy-Awareness of Distributed Data Clustering Algorithms Revisited
• Bi-stochastic Matrix Approximation Framework for Data Co-clustering
• Sequential Cost-Sensitive Feature Acquisition
• Explainable and Efficient Link Prediction in Real-World Network Data
• DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs
• Similarity Based Hierarchical Clustering with an Application to Text Collections
• Determining Data Relevance Using Semantic Types and Graphical Interpretation Cues
• A First Step Toward Quantifying the Climate's Information Production over the Last 68,000 Years
• HAUCA Curves for the Evaluation of Biomarker Pilot Studies with Small Sample Sizes and Large Numbers of Features
• Stability Evaluation of Event Detection Techniques for Twitter
• IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures
• An Optimized k-NN Approach for Classification on Imbalanced Datasets with Missing Data
• Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance
• Prediction of Failures in the Air Pressure System of Scania Trucks Using a Random Forest and Feature Engineering.

This book constitutes the refereed conference proceedings of the 15th International Conference on Intelligent Data Analysis, which was held in October 2016 in Stockholm, Sweden. The 36 revised full papers presented were carefully reviewed and selected from 75 submissions. The traditional focus of the IDA symposium series is on end-to-end intelligent support for data analysis. The symposium aims to provide a forum for inspiring research contributions that might be considered preliminary in other leading conferences and journals, but that have a potentially dramatic impact.

• Improving Chairlift Security with Deep Learning.- Discovering motifs with variants in music databases.- Biclustering Multivariate Time Series.- Visualization of Topic-Sentiment Dynamics in Crowdfunding Projects.- Regression tree for bandits models in A/B testing.- Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases.- Skin Cancer Detection in Dermoscopy Images Using Sub-Region Features.- Bucket Selection: A Model-Independent Diverse Selection Strategy for Widening.- Interactive Pattern Sampling for Characterizing Unlabeled Data.- Searching for Spatio-Temporal-Keyword Patterns in Semantic Trajectories.- Natural Language Analysis of Online Health Forums.- Freudian Slips: Analysing the Internal Representations of a Neural Network from its Mistakes.- KAPMiner: Mining ordered association rules with constraints.- A Dynamic Adaptive Questionnaire for Improved Disease Diagnostics.- ABIDE: Querying Time-Evolving Sequences of Temporal Intervals.- The Actors of History: Narrative Network Analysis Reveals the Institutions of Power in British Society Between 1800-1950.- Learning DTW-Preserving Shapelets.- Droplet Ensemble Learning on Drifting Data Streams.- Predictive clustering trees for hierarchical multi-target regression.- Identifying novel features from specimen data for the prediction of valuable field trips.- Adapting supervised classifiation algorithms to arbitrary weak label scenarios.- The Combination of Decision in Crowds When The Number of Reliable Annotator is Rare.- Estimating Sequence Similarity from Contig Sets.- Computational Topology Techniques for Characterizing Time-Series Data.- Improving Cold-Start Recommendations with Social-Media Trends and Reputations.- Hierarchical Novelty Detection.- Towards automatic evaluation of asphalt irregularity using smartphone's Sensors.- A structural benchmark for logical argumentation frameworks.
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This book constitutes the conference proceedings of the 16th International Symposium on Intelligent Data Analysis, which was held in October 2017 in London, UK. The 28 full papers presented in this book were carefully reviewed and selected from 66 submissions. The traditional focus of the IDA symposium series is on end-to-end intelligent support for data analysis. IDA solicits papers on all aspects of intelligent data analysis, including papers on intelligent support for modelling and analyzing data from complex, dynamical systems.
(source: Nielsen Book Data)

• Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder
• Dual Sequential Variational Autoencoders for Fraud Detection
• A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks
• Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams
• GraphMDL: Graph Pattern Selection Based on Minimum Description Length
• Towards Content Sensitivity Analysis
• Gibbs Sampling Subjectively Interesting Tiles
• Even Faster Exact k-Means Clustering
• Ising-Based Consensus Clustering on Special Purpose Hardware
• Transfer Learning by Learning Projections from Target to Source
• Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs
• Towards Evaluation of CNN Performance in Semantically Meaningful Latent Spaces
• Vouw: Geometric Pattern Mining Using the MDL Principle
• A Consensus Approach to Improve NMF Document Clustering
• Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams
• Widening for MDL-Based Retail Signature Discovery
• Addressing the Resolution Limit and the Field of View Limit in Community Mining
• Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics
• Adversarial Attacks Hidden in Plain Sight
• Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code
• Overlapping Hierarchical Clustering (OHC)
• Digital Footprints of International Migration on Twitter
• Percolation-Based Detection of Anomalous Subgraphs in Complex Networks
• A Late-Fusion Approach to Community Detection in Attributed Networks
• Reconciling Predictions in the Regression Setting: an Application to Bus Travel Time Prediction
• A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
• Actionable Subgroup Discovery and Urban Farm Optimization
• AVATAR
• Machine Learning Pipeline Evaluation Using Surroga te Model
• Detection of Derivative Discontinuities in Observational Data
• Improving Prediction with Causal Probabilistic Variables
• DO-U-Net for Segmentation and Counting
• Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media
• Event Recognition Based on Classification of Generated Image Captions
• Human-to-AI Coach: Improving Human Inputs to AI Systems
• Aleatoric and Epistemic Uncertainty with Random Forests
• Master your Metrics with Calibration
• Supervised Phrase-Boundary Embeddings
• Predicting Remaining Useful Life with Similarity-Based Priors
• Orometric Methods in Bounded Metric Data
• Interpretable Neuron Structuring with Graph Spectral Regularization
• Comparing the Preservation of Network Properties by Graph Embeddings
• Making Learners (More) Monotone
• Combining Machine Learning and Simulation to a Hybrid Modelling Approach
• LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification
• Angle-Based Crowding Degree Estimation for Many-Objective Optimization.

This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDAs mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.

• Six Sigma Methodology and Implementation in Indian Context: A Review Based Study.- Evaluation of work posture using ergonomics in Indian small scale industry.- Manufacturing Intelligence in the Context of Indian SME's.- Multi-objective optimization of process parameter during dry Turning of Grade5 Titanium alloy with carbide inserts: Hybrid Fuzzy-TOPSIS approach.- Analysis of Factors for Green Supply Chain in Indian Timber Market: An ISM Approach.- The Value Engineering Way: A Case of Industrial Fans.- Obstacles of Flexible Manufacturing System: An AHP Approach for Quantifying the Rank Given by the ISM Model.- To Find the Effectiveness of Barriers in Reverse Logistics by using ISM.- Optimization of MQL Machining Parameters Using Combined Taguchi and TOPSIS Method.- Life Cycle Assessment Framework for Sustainable Development in Manufacturing Environment.- Artificial Neural Network Models for the Prediction of Metal Removal Rate in Rotary Ultrasonic Machining.- Finding the Percentage Effectiveness of Agile Manufacturing Barriers: An AHP Approach.- Modeling the Knowledge Sharing Barriers in Indian Small and Medium Industries using ISM.- Simulation and Optimization of Flexible Flow Shop Scheduling Problem in an Indian Manufacturing Industry.- Computational Investigation on the Thermal Characteristics of Heat Pipe Using Nanofluids.- Analysis of Barriers to Lean-Green Manufacturing System (Lgms): A Multi-Criteria Decision Making Approach.- Machine Learning Application for Pulsating Flow Through.- Ranking of Factors for Integrated Lean, Green and Agile Manufacturing for Indian Manufacturing Sme's.
• (source: Nielsen Book Data)

This book consists of select proceedings of the International Conference on Functional Material, Manufacturing and Performances (ICFMMP) 2019, and presents latest research on using the combined intelligence of people, processes, and machines to impact the overall economics of manufacturing. The book focuses on optimizing manufacturing resources, improving business value and safety, and reducing waste - both on the floor and in back-office operations. It highlights the applications of the latest manufacturing execution system (MES), intelligent devices, machine-to-machine communication, and data analysis for the production lines and facilities. This book will be useful to manufacturers of finished goods and of sub-assemblies in the automotive, agriculture, and construction equipment sector. It will also provide solutions to make production strategies exceptional and can be a useful reference for beginners, researchers, and professionals interested in intelligent manufacturing technologies.
(source: Nielsen Book Data)

This book aims to provide the latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to intelligent social networks and collaborative systems, intelligent networking systems, mobile collaborative systems, secure intelligent cloud systems, etc., as well as to reveal synergies among various paradigms in such a multi-disciplinary field intelligent collaborative systems. With the fast development of the Internet, we are experiencing a shift from the traditional sharing of information and applications as the main purpose of the Web to an emergent paradigm, which locates people at the very centre of networks and exploits the value of people's connections, relations and collaboration. Social networks are also playing a major role in the dynamics and structure of intelligent Web-based networking and collaborative systems. Virtual campuses, virtual communities and organizations strongly leverage intelligent networking and collaborative systems by a great variety of formal and informal electronic relations, such as business-to-business, peer-to-peer and many types of online collaborative learning interactions, including the emerging e-learning systems. This has resulted in entangled systems that need to be managed efficiently and in an autonomous way. In addition, latest and powerful technologies based on grid and wireless infrastructure as well as cloud computing are currently enhancing collaborative and networking applications as a great deal but also facing new issues and challenges. The principal purpose of the research and development community is to stimulate research that will lead to the creation of responsive environments for networking and, at longer-term, the development of adaptive, secure, mobile and intuitive intelligent systems for collaborative work and learning.
(source: Nielsen Book Data)