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Produktbild: Intelligent Data Engineering and Automated Learning – IDEAL 2019

Intelligent Data Engineering and Automated Learning – IDEAL 2019 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part II

Aus der Reihe Information Systems and Applications, incl. Internet/Web, and HCI

51,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

24.10.2019

Herausgeber

Hujun Yin + weitere

Verlag

Springer

Seitenzahl

364

Maße (L/B/H)

23,5/15,5/2,1 cm

Gewicht

587 g

Auflage

1st ed. 2019

Sprache

Englisch

ISBN

978-3-030-33616-5

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

24.10.2019

Herausgeber

Verlag

Springer

Seitenzahl

364

Maße (L/B/H)

23,5/15,5/2,1 cm

Gewicht

587 g

Auflage

1st ed. 2019

Sprache

Englisch

ISBN

978-3-030-33616-5

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Intelligent Data Engineering and Automated Learning – IDEAL 2019
  • 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 Unified 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.