Computational Methods for Medical and Cyber Security
Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorith...
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Other Authors: | , |
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Format: | Electronic Book Chapter |
Language: | English |
Published: |
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MDPI - Multidisciplinary Digital Publishing Institute
2022
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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Summary: | Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields. |
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Physical Description: | 1 electronic resource (228 p.) |
ISBN: | books978-3-0365-5115-9 9783036551166 9783036551159 |
Access: | Open Access |