Computational Methods for Medical and Cyber Security

Edited by
August 2022
228 pages
  • ISBN978-3-0365-5116-6 (Hardback)
  • ISBN978-3-0365-5115-9 (PDF)

This book is a reprint of the Special Issue Computational Methods for Medical and Cyber Security that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Environmental & Earth Sciences
Physical Sciences

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.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
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; machine learning; adversarial machine learning; imbalanced datasets; 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; deep learning; text-to-SQL; natural language processing; database; machine learning; machine translation; medical image segmentation; convolutional neural networks; SE block; U-net; DeepLabV3plus; blockchain; 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; cybersecurity; internet of things; cloud computing; computational models; deep learning; metaheuristics; phishing detection; website phishing