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This book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.* The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part I, including: Healthcare, Education, Web Application and On-device application. * The conference was originally planned for December 2021, but was postponed to 2022. .
(source: Nielsen Book Data)

This book constitutes the proceedings of the 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, held in Sydney, Australia in February 2022.* The 26 full papers presented together with 35 short papers were carefully reviewed and selected from 116 submissions. The papers were organized in topical sections in Part II named: Pattern mining; Graph mining; Text mining; Multimedia and time series data mining; and Classification, clustering and recommendation. * The conference was originally planned for December 2021, but was postponed to 2022.
(source: Nielsen Book Data)

• Research Track.- Parallel Nonlinear Dimensionality Reduction Using GPU Acceleration.- Taking the Confusion out of Multinomial Confusion Matrices and Imbalanced Classes.- Sharpshooting Most Beneficial Part of AUC for Detecting Malicious Logs.- A Drift Aware Hierarchical Test based Approach for Combating Spammers in Online Social Networks.- Hospital Readmission Prediction Using Semantic Relations Between Medical Codes.- HFM++: An Enhanced Holographic Factorization Machine for Recommendation.- Deep Learning for Bias Detection: From Inception to Deployment.- Exploring Fusion Strategies in Deep Learning Models for Multi-modal Classification.- Application Track.- Chameleon: A Python Workflow Toolkit for Feature Selection.- PostMatch: A Framework for Efficient Address Matching.- Detection of Classical Cipher Types with Feature-Learning Approach.- SOMPS-Net: Attention based Social Graph Framework for Early Detection of Fake Health News.- How to Read the News: A Study of How Sentiment Effects Financial Markets.- Investigation of Topic Modelling Methods for Understanding the Reports of the Mining Projects in Queensland.- A Semi-Automatic Data Extraction System for Heterogeneous Data Sources: A Case Study from Cotton Industry.- Nonnegative Matrix Factorization to Understand Spatio-Temporal Traffic Pattern Variations during COVID
• -19: A Case Study.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 19th Australasian Conference on Data Mining, AusDM 2021, held in Brisbane, Queensland, Australia, in December 2021.* The 16 revised full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in sections on research track and application track. *Due to the COVID-19 pandemic the conference was held online. .
(source: Nielsen Book Data)

• Recommender Systems
• Classification
• Clustering
• Mining Social Networks
• Representation Learning and Embedding
• Mining Behavioral Data
• Deep Learning
• Feature Extraction and Selection
• Human, Domain, Organizational and Social Factors in Data Mining.

The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 135 full papers presented were carefully reviewed and selected from 628 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: recommender systems; classification; clustering; mining social networks; representation learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.

• Mining Sequential Data
• Mining Imbalanced Data
• Association
• Privacy and Security
• Supervised Learning
• Novel Algorithms
• Mining Multi-Media/Multi-Dimensional Data
• Application
• Mining Graph and Network Data
• Anomaly Detection and Analytics
• Mining Spatial, Temporal, Unstructured and Semi-Structured Data
• Sentiment Analysis
• Statistical/Graphical Model
• Multi-Source/Distributed/Parallel/Cloud Computing.

The two-volume set LNAI 12084 and 12085 constitutes the thoroughly refereed proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, which was due to be held in Singapore, in May 2020. The conference was held virtually due to the COVID-19 pandemic. The 135 full papers presented were carefully reviewed and selected from 628 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, visualization, decision-making systems, and the emerging applications. They are organized in the following topical sections: recommender systems; classification; clustering; mining social networks; representation learning and embedding; mining behavioral data; deep learning; feature extraction and selection; human, domain, organizational and social factors in data mining; mining sequential data; mining imbalanced data; association; privacy and security; supervised learning; novel algorithms; mining multi-media/multi-dimensional data; application; mining graph and network data; anomaly detection and analytics; mining spatial, temporal, unstructured and semi-structured data; sentiment analysis; statistical/graphical model; multi-source/distributed/parallel/cloud computing.

• Knowledge Discovery and Information Retrieval.- Knowledge Engineering and Ontology Development.- Knowledge Management and Information Sharing.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2018, held in Seville, Spain, in September 2018.The 12 full papers presented were carefully reviewed and selected from 167 submissions. The papers are organized in topical sections on knowledge discovery and information retrieval; knowledge engineering and ontology development; and knowledge management and information sharing.
(source: Nielsen Book Data)

• Intro; Preface; Organization; Contents; The 14th Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2019); A Supporting Tool for IT System Security Specification Evaluation Based on ISO/IEC 15408 and ISO/IEC 18045; 1 Introduction; 2 Security Target and Its Evaluation Process; 2.1 Security Target with ISO/IEC 15408; 2.2 Evaluation Process Based on ISO/IEC 18045; 2.3 Issues of the Evaluation Process; 3 Supporting Methods for Evaluation on Security Targets; 3.1 Analysis of Evaluation Tasks on Security Targets; 3.2 Supporting Methods

• 4 Security Target Evaluator: A Supporting Tool for Evaluation on Security Targets4.1 Requirements for Security Target Evaluator; 4.2 Components for Supporting Methods in Security Target Evaluator; 4.3 Management of Relevant Documents and Data; 5 Concluding Remarks; References; An Investigation on Multi View Based User Behavior Towards Spam Detection in Social Networks; Abstract; 1 Introduction; 2 Related Work; 3 Hypothesis; 4 Approach and Experimental Setup; 4.1 Dataset Description; 4.2 Representation of User's Content Interest for the Investigation

• 4.3 Representation of User's Popularity for the Investigation4.4 Experimental Methods for User Behavior Analysis; 5 Discussion; 6 Conclusion; References; A Cluster Ensemble Strategy for Asian Handicap Betting; 1 Introduction; 2 Related Works; 3 Methodology; 3.1 Expected Goal Difference Trend; 3.2 Model Design; 3.3 Component Cluster; 3.4 Cluster Ensemble; 4 Case Studies; 4.1 Data; 4.2 Evaluation Metrics; 4.3 Model Parameters Selection; 4.4 Out of Sample Prediction; 5 Conclusions; References; Designing an Integrated Intelligence Center: New Taipei City Police Department as an Example; Abstract

• 1 Introduction2 Literature Review; 3 Method; 3.1 Discovery; 3.2 Understanding; 3.3 Implementation; 3.3.1 Query Service; 3.3.2 Crime Information Analysis; 3.3.3 Applied System Q 3.3.4 Image Analysis Service; 3.3.5 Real-Time Video and Image Transmission; 3.3.6 Internet Information Collection; 4 Discussion and Future Work; 5 Conclusion; References; Early Churn User Classification in Social Networking Service Using Attention-Based Long Short-Term Memory; 1 Introduction; 2 Data; 2.1 RoomClip; 2.2 Definition of Early Churn Users in RoomClip; 2.3 Distribution of Event Interval of New Users

• 3 Proposed Model3.1 Input; 3.2 Sequence Encoding Layer; 3.3 Attention Layer; 3.4 Early Churn Classification Layer; 3.5 Learning Algorithm; 4 Experiment; 4.1 Evaluation Criteria; 4.2 Preprocessing; 4.3 Baseline Models; 4.4 Result; 5 Feature Importance Analysis; 5.1 Importance of Each Temporal Part in Event Sequence; 5.2 Importance of Event Type; 6 Related Research; 7 Discussion and Conclusion; References; PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future (WeL 2019); Weakly Supervised Learning by a Confusion Matrix of Contexts; Abstract; 1 Introduction

This book constitutes the thoroughly refereed post-workshop proceedings of the workshops that were held in conjunction with the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2019, in Macau, China, in April 2019. The 31 revised papers presented were carefully reviewed and selected from a total of 52 submissions. They stem from the following workshops: · PAISI 2019: 14th Pacific Asia Workshop on Intelligence and Security Informatics · WeL 2019: PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future · LDRC 2019: PAKDD 2019 Workshop on Learning Data Representation for Clustering · BDM 2019: 8th Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining · DLKT 2019: 1st Pacific Asia Workshop on Deep Learning for Knowledge Transfer. -- Provided by publisher.

• Data Mining Foundations.- Big Data.- Text and Multimedia Mining.- Miscellaneous Topics.
• (source: Nielsen Book Data)

This book constitutes the refereed proceedings of the 14th International Conference on Advanced Data Mining and Applications, ADMA 2018, held in Nanjing, China in November 2018. The 23 full and 22 short papers presented in this volume were carefully reviewed and selected from 104 submissions. The papers were organized in topical sections named: Data Mining Foundations; Big Data; Text and Multimedia Mining; Miscellaneous Topics.
(source: Nielsen Book Data)

• Evolutionary computation.- Knowledge exploration in IoT.- Artificial intelligence.- Machine learning.- Image processing.- Pattern recognition.- Speech processing.- Information retrieval.- Natural language processing.- Social network analysis.- Security.- Fuzzy rough sets.
• (source: Nielsen Book Data)

This book constitutes the refereed conference proceedings of the 6th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2018, held in Cluj-Napoca, Romania, in December 2018. The 33 full papers presented were carefully reviewed and selected from 93 submissions. The papers were grouped into various subtopics including evolutionary computation, knowledge exploration in IoT, artificial intelligence, machine learning, image processing, pattern recognition, speech processing, information retrieval, natural language processing, social network analysis, security, and fuzzy rough sets.
(source: Nielsen Book Data)

• Big Data Analytics for Social Computing (BDASC).- Australasian Workshop on Machine Learning for Cyber-security (ML4Cyber).- Biologically-inspired Techniques for Knowledge Discovery and Data Mining (BDM).- Pacific Asia Workshop on Intelligence and Security Informatics (PAISI).- Data Mining for Energy Modeling and Optimization (DaMEMO).
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed post-workshop proceedings at PAKDD Workshops 2018, held in conjunction with the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2018, in Melbourne, Australia, in June 2018. The 32 revised papers presented were carefully reviewed and selected from 46 submissions. The workshops affiliated with PAKDD 2018 include: Workshop on Big Data Analytics for Social Computing, BDASC, Australasian Workshop on Machine Learning for Cyber-security, ML4Cyber, Workshop on Biologically-inspired Techniques for Knowledge Discovery and Data Mining, BDM, Pacific Asia Workshop on Intelligence and Security Informatics, PAISI, and Workshop on Data Mining for Energy Modeling and Optimization, DaMEMO.
(source: Nielsen Book Data)

• PART I. THEORETICAL FOUNDATIONS
• Commonsense causal modeling in the data mining context / Lawrence J. Mazlack
• Definability of association rules in predicate calculus / Jan Rauch
• A measurement-theoretic foundation of rule interestingness evaluation / Yiyu Yao [and others]
• Statistical independence as linear dependence in a contingency table / Shusaku Tsumoto
• Foundations of classification / JingTao Yao [and others]
• Data mining as generalization: a formal model
• PART II. NOVEL APPROACHES
• SVM-OD: SVM method to detect outliers / Jiaqi Wang [and others]
• Extracting rules from incomplete decision systems: system ERID / Agnieszka Dardzinska, Zbigniew W. Ras
• Mining for patterns based on contingency tables by KL-Miner -- first experience / Jan Rauch [and others]
• Knowledge discovery in fuzzy databases using attribute-oriented induction / Rafal A. Angryk, Frederick E. Petry
• Rough set strategies to data with missing attribute values / Jerzy W. Grzymala-Busse
• Privacy-preserving collaborative data mining / Justin Zhan [and others]
• Impact of purity measures on knowledge extraction in decision trees / Mitja Lenič [and others]
• Multidimensional on-line mining / Ching-Yao Wang [and others]
• Quotient space based cluster analysis / Ling Zhang, Bo Zhang
• PART III. NOVEL APPLICATIONS
• Research issues in web structural delta mining / Qiankun Zhao [and others]
• Workflow reduction for reachable-path rediscovery in workflow ming / Kwang-Hoon Kim, Clarence A. Ellis
• Principal component-based anomaly detection scheme / Mei-Ling Shyu [and others]
• Making better sense of the demographic data value in the data mining procedure / katherin M. Shelfer, Xiaohua Hu
• An effective approach for ming time-series gene expression profile / Vincent S.M. Tseng, Yen-Lo Chen.

Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for real-world problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.
(source: Nielsen Book Data)

• Invited Talk.- Moving Targets.- Bioinformatics Contributions to Data Mining.- Theoretical Aspects of Data Mining.- Bootstrap Feature Selection for Ensemble Classifiers.- Evaluating the Quality of Clustering Algorithms Using Cluster Path Lengths.- Finding Irregularly Shaped Clusters Based on Entropy.- Fuzzy Conceptual Clustering.- Mining Concept Similarities for Heterogeneous Ontologies.- Re-mining Positive and Negative Association Mining Results.- Multi-Agent Based Clustering: Towards Generic Multi-Agent Data Mining.- Describing Data with the Support Vector Shell in Distributed Environments.- Robust Clustering Using Discriminant Analysis.- New Approach in Data Stream Association Rule Mining Based on Graph Structure.- Multimedia Data Mining.- Fast Training of Neural Networks for Image Compression.- Processing Handwritten Words by Intelligent Use of OCR Results.- Saliency-Based Candidate Inspection Region Extraction in Tape Automated Bonding.- Image Classification Using Histograms and Time Series Analysis: A Study of Age-Related Macular Degeneration Screening in Retinal Image Data.- Entropic Quadtrees and Mining Mars Craters.- Hybrid DIAAF/RS: Statistical Textual Feature Selection for Language-Independent Text Classification.- Multimedia Summarization in Law Courts: A Clustering-Based Environment for Browsing and Consulting Judicial Folders.- Comparison of Redundancy and Relevance Measures for Feature Selection in Tissue Classification of CT Images.- Data Mining in Marketing.- Quantile Regression Model for Impact Toughness Estimation.- Mining for Paths in Flow Graphs.- Combining Unsupervised and Supervised Data Mining Techniques for Conducting Customer Portfolio Analysis.- Managing Product Life Cycle with MultiAgent Data Mining System.- Modeling Pricing Strategies Using Game Theory and Support Vector Machines.- Data Mining in Industrial Processes.- Determination of the Fault Quality Variables of a Multivariate Process Using Independent Component Analysis and Support Vector Machine.- Dynamic Pattern Extraction of Parameters in Laser Welding Process.- Trajectory Clustering for Vibration Detection in Aircraft Engines.- Episode Rule-Based Prognosis Applied to Complex Vacuum Pumping Systems Using Vibratory Data.- Predicting Disk Failures with HMM- and HSMM-Based Approaches.- Aircraft Engine Health Monitoring Using Self-Organizing Maps.- Data Mining in Medicine.- Finding Temporal Patterns in Noisy Longitudinal Data: A Study in Diabetic Retinopathy.- Selection of High Risk Patients with Ranked Models Based on the CPL Criterion Functions.- Medical Datasets Analysis: A Constructive Induction Approach.- Data Mining in Agriculture.- Regression Models for Spatial Data: An Example from Precision Agriculture.- Trend Mining in Social Networks: A Study Using a Large Cattle Movement Database.- WebMining.- Spam Email Filtering Using Network-Level Properties.- Domain-Specific Identification of Topics and Trends in the Blogosphere.- Combining Business Process and Data Discovery Techniques for Analyzing and Improving Integrated Care Pathways.- Interest-Determining Web Browser.- Web-Site Boundary Detection.- Data Mining in Finance.- An Application of Element Oriented Analysis Based Credit Scoring.- A Semi-supervised Approach for Reject Inference in Credit Scoring Using SVMs.- Aspects of Data Mining.- Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool.- The Orange Customer Analysis Platform.- Semi-supervised Learning for False Alarm Reduction.- Learning from Humanoid Cartoon Designs.- Mining Relationship Associations from Knowledge about Failures Using Ontology and Inference.- Data Mining for Network Performance Monitoring.- Event Prediction in Network Monitoring Systems: Performing Sequential Pattern Mining in Osmius Monitoring Tool.- Selection of Effective Network Parameters in Attacks for Intrusion Detection.
• (source: Nielsen Book Data)

These are the proceedings of the tenth event of the Industrial Conference on Data Mining ICDM held in Berlin (www.data-mining-forum.de). For this edition the Program Committee received 175 submissions. After the pe- review process, we accepted 49 high-quality papers for oral presentation that are included in this book. The topics range from theoretical aspects of data mining to app- cations of data mining such as on multimedia data, in marketing, finance and telec- munication, in medicine and agriculture, and in process control, industry and society. Extended versions of selected papers will appear in the international journal Trans- tions on Machine Learning and Data Mining (www.ibai-publishing.org/journal/mldm). Ten papers were selected for poster presentations and are published in the ICDM Poster Proceeding Volume by ibai-publishing (www.ibai-publishing.org). In conjunction with ICDM four workshops were held on special hot applicati- oriented topics in data mining: Data Mining in Marketing DMM, Data Mining in LifeScience DMLS, the Workshop on Case-Based Reasoning for Multimedia Data CBR-MD, and the Workshop on Data Mining in Agriculture DMA. The Workshop on Data Mining in Agriculture ran for the first time this year. All workshop papers will be published in the workshop proceedings by ibai-publishing (www.ibai-publishing.org). Selected papers of CBR-MD will be published in a special issue of the international journal Transactions on Case-Based Reasoning (www.ibai-publishing.org/journal/cbr).
(source: Nielsen Book Data)

• Business Data Science: Fintech, Management, and Analytics.- Health and Biological Data Science.- Novel Data Science Theory and Applications.
• (source: Nielsen Book Data)

This book constitutes selected papers of the Third International Conference on Data Science, Medicine and Bioinformatics, IDMB 2019, held in Nanning, China, in June 2019. The 19 full papers and 1 short paper were carefully reviewed and selected from 93 submissions. The papers are organized according to the following topical sections: business data science: fintech, management, and analytics.- health and biological data science.- novel data science theory and applications.
(source: Nielsen Book Data)

• Rethinking the Input for Process Mining: Insights from the XES Survey and Workshop
• Probability Estimation of Uncertain Process Trace Realizations
• Visualizing Trace Variants From Partially Ordered Event Data
• Analyzing Multi-level BOM-structured Event Data
• Linac: A Smart Environment Simulator of Human Activities
• Root Cause Analysis in Process Mining with Probabilistic Temporal Logic
• xPM: A Framework for Process Mining with Exogenous Data
• A Bridging Model for Process Mining and IoT
• Exploiting Instance Graphs and Graph Neural Networks for next activity prediction
• Can deep neural networks learn process modelstructure? An assessment framework and analysis
• Remaining Time Prediction for Processes with Inter-Case Dynamics
• Event Log Sampling for Predictive Monitoring
• Active Anomaly Detection for Key Item Selection in Process Auditing
• Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach
• Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring
• Online Prediction of Aggregated Retailer Consumer Behaviour
• PErrCas: Process Error Cascade Mining in Trace Streams
• Continuous performance evaluation for business process outcome monitoring
• An Event Data Extraction Approach from SAP ERP for Process Mining
• Towards a Natural Language Conversational Interface for Process Mining
• On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization
• Verifying guideline compliance in clinical treatment using multi-perspective conformance checking: a case study
• Patient Discharge Classification based on the Hospital Treatment Process
• Combining the Clinical and Operational Perspectives in Heterogeneous Treatment E ect Inference in Healthcare Processes
• Interactive Process Mining Applied in a Cardiology Outpatient Department
• Discovering care pathways for multi-morbid patients using event graphs
• Process Mining in Trusted Execution Environments: Towards Hardware Guarantees for Trust-aware Inter-organizational Process Analysis
• Quantifying the Re-identification Risk in Published Process Models
• Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities.

This open access book constitutes revised selected papers from the International Workshops held at the Third International Conference on Process Mining, ICPM 2021, which took place in Eindhoven, The Netherlands, during October 31–November 4, 2021. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 28 papers included in this volume were carefully reviewed and selected from 65 submissions. They stem from the following workshops: 2nd International Workshop on Event Data and Behavioral Analytics (EDBA) 2nd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 4th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 2nd International Workshop on Trust, Privacy, and Security in Process Analytics (TPSA) One survey paper on the results of the XES 2.0 Workshop is included.

• 1st International Workshop on Event Data and Behavioral Analytics (EDBA).- Visually Representing History Dependencies in Event Logs.- Analysis of Business Process Batching using Causal Event Models.- Process Procespecting to Improve Renewable Energy Interconnection Queues: A Case Study.- Automated Discovery of Process Models with True Concurrency and Inclusive Choices.- A Novel Approach to Discover Switch Behaviours in Process Mining.- Process Model Discovery from Sensor Event Data.- Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study.- 1st International Workshop on Leveraging Machine Learning in Process Mining (ML4PM).- Predicting Remaining Cycle Time from Ongoing Cases: A Survival Analysis-based Approach.- Time Matters:Time-Aware LSTMs for Predictive Business Process Monitoring.- A preliminary study on the application of Reinforcement Learning for Predictive Process Monitoring.- An Alignment Cost-Based Classi cation of Log Traces Using Machine-Learning.- Improving the Extraction of Process Annotations from Text with Inter-Sentence Analysis.- Case2vec: Advances in Representation Learning for Business Processes.- Supervised Conformance Checking using Recurrent Neural Network Classifiers.- 1st International Workshop on Streaming Analytics for Process Mining (SA4PM'20).- Online Anomaly Detection Using Statistical Leverage for Streaming Business Process Events.- Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation.- OTOSO: Online Trace Ordering for Structural Overviews.- Performance Skyline: Inferring Process Performance Models from Interval Events.- 5th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2020).- Alignment Approximation for Process Trees.- Stochastic Process Discovery By Weight Estimation.- Graph-based Process Mining.- Third International Workshop on Process-Oriented Data Science for Healthcare (PODS4H).- A Process Mining approach to statistical analysis: application to a real-world advanced melanoma dataset.- Process Mining of Disease Trajectories in MIMIC-III: A Case Study.- The Need for Interactive Data-Driven Process Simulation in Healthcare: A Case Study.- Process mining on the extended event log to analyse the system usage during healthcare processes (Case study: the GP Tab usage during chemotherapy treatments).- Process Mining on FHIR - An Open Standards-Based Process Analytics Suite for Healthcare.- Deriving a sophisticated clinical pathway based on patient conditions from electronic health record data.- Exploration on How Global Warming Affects Emergency Services.- 1st Workshop on Trust and Privacy in Process Analytics (TPPA).- Towards Quantifying Privacy in Process Mining.
• (source: Nielsen Book Data)

This book constitutes revised selected papers from the International Workshops held at the Second International Conference on Process Mining, ICPM 2020, which took place during October 4-9, 2020. The conference was planned to take place in Padua, Italy, but had to be held online due to the COVID-19 pandemic.The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 29 papers included in this volume were carefully reviewed and selected from 59 submissions. They stem from the following workshops: 1st International Workshop on Event Data and Behavioral Analytics (EDBA) 1st International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) 1st International Workshop on Streaming Analytics for Process Mining (SA4PM'20) 5th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) 3rd International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) 1st International Workshop on Trust and Privacy in Process Analytics (TPPA) .
(source: Nielsen Book Data)

• 1. NLP Based Resume Screening & Job Recruitment Portal.-
• 2. Access Control and Authorization Techniques w.r.t. Client Applications.-
• 3. Anomaly Detection In Biomedical Data and Image Using Various Shallow and Deep Learning Algorithms.-
• 4. Quantum Generative Adversarial Networks.-
• 5. Sentimental Analysis For E-commerse Site.-
• 6. A Novel Approach to Apply Different Algorithms to Predict Covid-19 Disease.-
• 7. Gene regulation via Bloom filter.-
• 8. A Novel Survey on Ubiquitous Computing.-
• 9. Emotion Recognition in Speech using Convolutional Neural Networks.
• (source: Nielsen Book Data)

The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2021), organized by SCAD College of Engineering and Technology, Tirunelveli, India, during July 16-17, 2021. This book discusses new cognitive informatics tools, algorithms, and methods that mimic the mechanisms of the human brain which leads to an impending revolution in understating a large amount of data generated by various smart applications. The book includes novel work in data intelligence domain which combines with the increasing efforts of artificial intelligence, machine learning, deep learning, and cognitive science to study and develop a deeper understanding of the information processing systems.
(source: Nielsen Book Data)

• Data mining and applications
• Deep Learning and applications
• Data-driven software engineering
• Health, NLP, and social media
• Image processing, machine learning, and semantic Web.

This book constitutes the refereed proceedings of the 8th International Conference on Information Management and Big Data, SIMBig 2021, held as a virtual event in December 2021. The 25 revised full papers and 2 revised short papers presented were carefully reviewed and selected from 67 submissions. The papers are organized in topical sections on data mining and applications; deep learning and applications; data-driven software engineering; health, NLP, and social media; image processing, machine learning, and semantic web.

• Genetic Algorithm based vaccine optimization technique to control COVID-19.- Ethereum MongoDB: Integrating Blockchain with Non-Relational Databases.- Attention based Discrimination of Mycoplasma Pneumonia.- Minimum Weight Dominating Set using Chemical Reaction Optimization Algorithm.- A Hybrid Domain Fragile Watermarking Techniques for Authentication of Color Images.- IOT based Carbon Monoxide Monitoring Model for Transportation Vehicles.- TWEERIFY: A Web based Sentiment Analysis System using Rule and Deep Learning Techniques.- Machine Learning Based Fake News Detection on Covid-19 Tweets Data.- ADVERSE EFFECT OF ELECTROMAGNETICFREQUENCY ON HUMAN BEING HEALTH UNDER REAL LIFE SCENARIO: A COMPARATIVE STUDY.
• (source: Nielsen Book Data)

This book covers various topics, including collective intelligence, intelligent transportation systems, fuzzy systems, Bayesian network, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, and speech processing. This book is a collection of high-quality research work on cutting-edge technologies and the most-happening areas of computational intelligence and data engineering. It includes selected papers from the International Conference on Computational Intelligence and Data Engineering (ICCIDE 2021).
(source: Nielsen Book Data)

• Applications of Knowledge Discovery
• Fuzzy World:A Tool Training Agent from Concept Cognitive to Logic Inference
• Collaborative Reinforcement Learning Framework to Model Evolution of Cooperation in Sequential Social Dilemmas
• SIGTRAN: Signature Vectors for Detecting Illicit Activities in Blockchain Transaction Networks
• VOA*: Fast Angle-Based Outlier Detection Over High-Dimensional Data Streams
• Learning Probabilistic Latent Structure for Outlier Detection from Multi-View Data
• GLAD-PAW: Graph-based Log Anomaly Detection by Position Aware Weighted Graph Attention Network
• CubeFlow: Money Laundering Detection with Coupled Tensors
• Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection
• Unsupervised Domain Adaptation for 3D Medical Image with High Efficiency
• A Hierarchical Structure-Aware Embedding Method for Predicting Phenotype-Gene Associations
• Autonomous Vehicle Path Prediction using Conditional Variational Autoencoder Networks
• Heterogeneous Graph Attention Network for Small and Medium-sized Enterprises Bankruptcy Prediction
• Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data
• Sim2Real for Metagenomes: Accelerating Animal Diagnostics with Adversarial Co-Training
• Attack Is the Best Defense: A Multi-Mode Poisoning PUF against Machine Learning Attacks
• Combining exogenous and endogenous signals with a semi-supervised co-attention network for early detection of COVID-19 fake tweets
• TLife-LSTM: Forecasting Future COVID-19 Progression with Topological Signatures of Atmospheric Conditions
• Lifelong Learning based Disease Diagnosis on Clinical Notes
• GrabQC: Graph based Query Contextualization for automated ICD coding
• Deep Gaussian Mixture Model on Multiple Interpretable Features of Fetal Heart Rate for Pregnancy Wellness
• Adverse Drug Events Detection, Extraction and Normalization from Online Comments of Chinese Patent Medicines
• Adaptive Graph Co-Attention Networks for Traffic Forecasting
• Dual-Stage Bayesian Sequence to Sequence Embeddings for Energy Demand Forecasting
• AA-LSTM: An Adversarial Autoencoder Joint Model for Prediction of Equipment Remaining Useful Life
• Data Mining of Specialized Data
• Analyzing Topic Transitions in Text-based Social Cascades using Dual-Network Hawkes Process
• HiPaR: Hierarchical Pattern-Aided Regression
• Improved Topology Extraction using Discriminative Parameter Mining of Logs
• Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion
• A k-MCST based Algorithm for Discovering Core-Periphery Structures in Graphs
• Detecting Sequentially Novel Classes with Stable Generalization Ability
• Learning-based Dynamic Graph Stream Sketch
• Discovering Dense Correlated Subgraphs in Dynamic Networks
• Fake News Detection with Heterogenous Deep Graph Convolutional Network
• Incrementally Finding the Vertices Absent from the Maximum Independent Sets
• Neighbours and Kinsmen: Hateful Users Detection with Graph Neural Network
• Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs
• A Meta-path based Graph Convolutional Network with Multi-Scale Semantic Extractions for Heterogeneous Event Classification
• Noise-Enhanced Unsupervised Link Prediction
• Weak Supervision Network Embedding for Constrained Graph Learning
• RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment
• Graph Attention Networks with Positional Embeddings
• Unified Robust Training for Graph Neural Networks against Label Noise
• Graph InfoClust: Maximizing Coarse-Grain Mutual Information in Graphs
• A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention
• Maximizing Explainability with SF-Lasso and Selective Inference for Video and Picture Ads. -Reliably Calibrated Isotonic Regression
• Multiple Instance Learning for Unilateral Data
• An Online Learning Algorithm for Non-Stationary Imbalanced Data by Extra-Charging Minority Class
• Locally Linear Support Vector Machines for Imbalanced Data Classification.
• Low-Dimensional Representation Learning from Imbalanced Data Streams
• PhotoStylist: Altering the Style of Photos based on the Connotations of Texts
• Gazetteer-Guided Keyphrase Generation from Research Papers
• Minits-AllOcc: An Efficient Algorithm for Mining Timed Sequential Patterns
• T^3N: Harnessing Text and Temporal Tree Network for Rumor Detection on Twitter
• AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection
• SCARLET: Explainable Attention based Graph Neural Network for Fake News spreader prediction
• Content matters: A GNN-based Model Combined with Text Semantics for Social Network Cascade Prediction
• TERMCast: Temporal Relation Modeling for Effective Urban Flow Forecasting
• Traffic Flow Driven Spatio-Temporal Graph Convolutional Network for Ride-hailing Demand Forecasting
• A Proximity Forest for Multivariate Time Series Classification
• C²-Guard: A Cross-Correlation Gaining Framework for Urban Air Quality Prediction
• Simultaneous multiple POI population patternanalysis system with HDP mixture regression
• Interpretable Feature Construction for Time Series Extrinsic Regression
• SEPC: Improving Joint Extraction of Entities and Relations by Strengthening Entity Pairs Connection.

The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.