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

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

Manuscript Submission Information

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