Computational Methods for Medical and Cyber Security

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Computing and Information Technology; a survey; academic performance; adversarial machine learning; ANOVA; behavioural finance; big data frameworks; blockchain; browser security; cloud computing; clustering; cognitive bias; comparative analysis; computational models; convolutional neural networks; cyber-attacks; cyber-security; cybersecurity; data communication; data science; database; deep learning; DeepLabV3plus; distributed frameworks; distributed ledger; educational data mining; electronic health record; environment; explainable machine learning; fault tolerance; financial technology; fintech; Flink; Hadoop; HL7; Hyperledger Composer; identity management; imbalanced datasets; intelligent tutoring system; internet of things; investor's profile; learning analytics; machine learning; machine translation; medical image segmentation; medical services; metaheuristics; natural language processing; password security; phishing detection; RAFT; regtech; Samza; SE block; secondary education; social media; social sciences; Spark; SPSS; stock market; Storm; stream processing systems; student field forecasting; supervised learning; Teheran Stock Exchange; text-to-SQL; transfer learning; U-net; unsupervised learning; website phishing
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication
• Physical Description: 1 online resource (228 p.)
• ISBN: 9783036551159; 9783036551166; books978-3-0365-5115-9
• Access: Open Access

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.