Computational Intelligence and Machine Learning: Models and Applications: 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 April 2025 | Viewed by 61

Special Issue Editors


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Guest Editor
Department of Automatic Control, Electrical Engineering and Optoelectronics, Faculty of Electrical Engineering, Częstochowa University of Technology, Al. Armii Krajowej 17, 42-200 Częstochowa, Poland
Interests: machine learning; evolutionary computation; artificial intelligence; pattern recognition; data mining and applications in forecasting, classification, regression, and optimization problems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Information Technology, Lodz University of Technology, al. Politechniki 8, 93-590 Lodz, Poland
Interests: image analysis; machine learning; computer vision; artificial intelligence; deep learning; structured prediction; pattern recognition; active contours
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational intelligence (CI) and machine learning (ML) are some of the most exciting fields in computing today. In recent decades, they have become an entrenched part of everyday life and have been successfully used to solve practical problems. The application area of CI and ML is very broad and includes engineering, industry, business, finance, medicine and many other domains. They cover a wide range of computational and learning algorithms, including classical ones such as linear regression, k-nearest neighbors and decision trees, as well as fuzzy systems, genetic, swarm and evolutionary algorithms, support vector machines and neural networks, and newly developed algorithms such as deep learning and boosted tree models. In practice, it is quite challenging to properly determine the appropriate architecture and parameters for CI and ML models so that the resulting model achieves a sound performance in both learning and generalization. Practical applications of CI and ML bring additional challenges, such as dealing with big, missing, distorted and uncertain data. In addition, interpretability is a paramount quality that CI and ML methods should achieve if they are to be applied in practice. Interpretability allows us to understand the model operation and raises confidence in its results.

This Special Issue focuses on CI and ML models and their applications in a diverse range of fields and problems. We welcome papers reporting substantive results on a wide range of computational and learning methods, discussing the conceptualization of a problem, data representation, feature engineering, CI and ML models, critical comparisons with existing techniques and interpretation of results. Specific attention will be given to recently developed CI and ML methods such as deep learning and boosted tree models.

Prof. Dr. Grzegorz Dudek
Dr. Arkadiusz Tomczyk
Guest Editors

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Keywords

  • computational intelligence
  • machine learning
  • artificial intelligence
  • soft computing
  • fuzzy logic
  • evolutionary computing
  • neural networks
  • decision trees
  • deep learning
  • expert systems
  • data mining
  • supervised learning
  • unsupervised learning
  • reinforcement learning
  • probabilistic methods
  • knowledge representation
  • forecasting
  • big data
  • pattern recognition
  • natural language processing
  • computer vision
  • bioinformatics
  • information retrieval
  • sentiment analysis
  • recommendation systems
  • speech recognition

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