Intelligent Data Engineering and Automated Learning - IDEAL 2019

This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019.

The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI.

Inhalt

Special Session on Fuzzy Systems and Intelligent Data Analysis.- Computational Generalization in Taxonomies Applied to: (1) Analyze Tendencies of Research and (2) Extend User Audiences.- Unsupervised Initialization of Archetypal Analysis and Proportional Membership Fuzzy Clustering.- Special Session on Machine Learning towards Smarter Multimodal Systems.- Multimodal Web Based Video Annotator with Real-Time Human Pose Estimation.- New Interfaces for Classifying Performance Gestures in Music.- Special Session on Data Selection in Machine Learning.- Classifying Ransomware Using Machine Learning Algorithms.- Artificial Neural Networks in Mathematical Mini-Games for Automatic Students Learning Styles Identification: A First Approach.- The Use of Uni ed Activity Records to Predict Requests Made by Applications for External Services.- Fuzzy Clustering Approach to Data Selection for Computer Usage in Headache Disorders.- Multitemporal Aerial Image Registration Using Semantic Features.- Special Session on Machine Learning in Healthcare.- Brain Tumor Classification Using Principal Component Analysis and Kernel Support Vector Machine.- Modelling survival by machine learning methods in liver transplantation: application to the UNOS dataset.- Design and Development of an Automatic Blood Detection System for Capsule Endoscopy Images.- Comparative Analysis for Computer-Based Decision Support: Case Study of Knee Osteoarthritis.- A Clustering-Based Patient Grouper for Burn Care.- A comparative assessment of Feed-Forward and Convolutional Neural Networks for the classification of prostate lesions.- Special Session on Machine Learning in Automatic Control.- A Method based on Filter Bank Common Spatial Pattern for Multiclass Motor Imagery BCI.- Safe Deep Neural Network-driven Autonomous Vehicles Using Software Safety Cages.- Wave and viscous resistance estimation by NN.- Neural controller of UAVs with inertia variations.- Special Session on Finance and Data Mining.- A Metric Framework for quantifying Data Concentration.- Adaptive Machine Learning-Based Stock Prediction using Financial Time Series Technical Indicators.- Special Session on Knowledge Discovery from Data.- Exploiting Online Newspaper Articles Metadata for Profiling City Areas.- Modelling the Social Interactions in Ant Colony Optimization.- An Innovative Deep-Learning Algorithm for Supporting the Approximate Classication of Workloads in Big Data Environments.- Control-flow Business Process Summarization via Activity Contraction.- Classifying Flies Based on Reconstructed Audio Signals.- Studying the Evolution of the 'Circular Economy' Concept using Topic Modelling.- Mining Frequent Distributions in Time Series.- Time Series Display for Knowledge Discovery on Selective Laser Melting Machines.- Special Session on Machine Learning Algorithms for Hard Problems.- Using Prior Knowledge to Facilitate Computational Reading of Arabic Calligraphy.- SMOTE Algorithm Variations in Balancing Data Streams.- Multi-Class Text Complexity Evaluation via Deep Neural Networks.- Imbalance reduction techniques applied to ECG classification problem.- Machine Learning Methods for Fake News Classification.- A genetic-based ensemble learning applied to imbalanced data classification.- The feasibility of deep learning use for adversarial model extraction in the cybersecurity domain.