TY - BOOK ID - 145571584 TI - Computational Methods for Medical and Cyber Security AU - Luo, Suhuai AU - Shaukat, Kamran PY - 2022 PB - Basel MDPI Books DB - UniCat KW - fintech KW - financial technology KW - blockchain KW - deep learning KW - regtech KW - environment KW - social sciences KW - machine learning KW - learning analytics KW - student field forecasting KW - imbalanced datasets KW - explainable machine learning KW - intelligent tutoring system KW - adversarial machine learning KW - transfer learning KW - cognitive bias KW - stock market KW - behavioural finance KW - investor’s profile KW - Teheran Stock Exchange KW - unsupervised learning KW - clustering KW - big data frameworks KW - fault tolerance KW - stream processing systems KW - distributed frameworks KW - Spark KW - Hadoop KW - Storm KW - Samza KW - Flink KW - comparative analysis KW - a survey KW - data science KW - educational data mining KW - supervised learning KW - secondary education KW - academic performance KW - text-to-SQL KW - natural language processing KW - database KW - machine translation KW - medical image segmentation KW - convolutional neural networks KW - SE block KW - U-net KW - DeepLabV3plus KW - cyber-security KW - medical services KW - cyber-attacks KW - data communication KW - distributed ledger KW - identity management KW - RAFT KW - HL7 KW - electronic health record KW - Hyperledger Composer KW - cybersecurity KW - password security KW - browser security KW - social media KW - ANOVA KW - SPSS KW - internet of things KW - cloud computing KW - computational models KW - metaheuristics KW - phishing detection KW - website phishing UR - https://www.unicat.be/uniCat?func=search&query=sysid:145571584 AB - 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. ER -