Computational Intelligence and Machine Learning: Models and Applications: 3rd Edition
A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Artificial Intelligence".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 1679
Editors
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
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. CI and ML have very broad applications, including 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 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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
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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