Computational Methods for Medical and Cyber Security

• Other Authors: Luo, Suhuai (Editor), Shaukat, Kamran (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Computing & information technology; fintech; financial technology; blockchain; deep learning; regtech; environment; social sciences; machine learning; learning analytics; student field forecasting; imbalanced datasets; explainable machine learning; intelligent tutoring system; adversarial machine learning; transfer learning; cognitive bias; stock market; behavioural finance; investor's profile; Teheran Stock Exchange; unsupervised learning; clustering; big data frameworks; fault tolerance; stream processing systems; distributed frameworks; Spark; Hadoop; Storm; Samza; Flink; comparative analysis; a survey; data science; educational data mining; supervised learning; secondary education; academic performance; text-to-SQL; natural language processing; database; machine translation; medical image segmentation; convolutional neural networks; SE block; U-net; DeepLabV3plus; cyber-security; medical services; cyber-attacks; data communication; distributed ledger; identity management; RAFT; HL7; electronic health record; Hyperledger Composer; cybersecurity; password security; browser security; social media; ANOVA; SPSS; internet of things; cloud computing; computational models; metaheuristics; phishing detection; website phishing
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.