Computational Methods for Medical and Cyber Security

Dear Colleagues,

Over the past decade, the use of computational methods, machine learning (ML), and deep learning (DL) has been exponentially growing in developing solutions for various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, they have also highlighted many shortcomings, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets, to name a few. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning and generative adversarial networks, have been successfully applied to solve various tasks in these fields. Therefore, it is crucial to apply these new methods to life-critical missions and measure these less-traditional algorithms' success when used in these fields.

Authors are welcome to submit their papers focusing on but not limited to the following topics: machine learning, explainable machine learning, adversarial machine learning, cyber security, imbalanced datasets, bioinformatics, medical diagnosis, financial risk management, finance, asset return forecasting, stock exchange, educational data mining, learning analytics, student performance prediction, and intelligent tutoring systems.

Prof. Dr. Suhuai Luo
Mr. Kamran Shaukat
Guest Editors

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