Various Deep Learning Algorithms in Computational Intelligence

Dear Colleagues,

Deep learning (Dl) is an essential topic of increasing interest in science, industry, and academia. Unlike traditional and machine learning methods, Dl methods can process large volumes of unstructured data. It is rapidly becoming a tool for modeling and solving complex and difficult problems in different fields of science and technology. For example, in medicine in breast cancer, COVID-19 detection and diabetes detection and prediction; in autonomous vehicles in various tasks such as perception, mapping and localization; in astronomy, for classifying and detecting stars and galaxies; in the design of future wireless networks, and others. Although Dl has been successfully applied in many fields and there are some theoretical developments, there are still many challenging problems in theory and applications that need to be solved for improving these techniques. For instance, it is important to find new methods for training the massive number of parameters required by Dl architectures, solve over-fitting and transfer learning problems, and others. This Special Issue aims to contribute to the state-of-the-art progress on Dl with theoretical, practical, and creative insights that provide vanguard solutions to challenging problems or can demonstrate competitive performance.

Prof. Dr. Oscar Humberto Ross
Guest Editor

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