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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| Format: | Online |
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| Language: | English |
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MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Online Access: | ONIX_20230220_9783036551166_75 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-97472 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-974722024-04-14T10:29:02Z Computational Methods for Medical and Cyber Security Luo, Suhuai Shaukat, Kamran 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 thema EDItEUR::U Computing and Information Technology 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. 2023-02-20T16:46:10Z 2023-02-20T16:46:10Z 2022 book ONIX_20230220_9783036551166_75 9783036551166 9783036551159 https://directory.doabooks.org/handle/20.500.12854/97472 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6015 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-5115-9 10.3390/books978-3-0365-5115-9 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036551166 9783036551159 228 Basel open access |
| spellingShingle | 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 thema EDItEUR::U Computing and Information Technology Computational Methods for Medical and Cyber Security |
| title | Computational Methods for Medical and Cyber Security |
| title_full | Computational Methods for Medical and Cyber Security |
| title_fullStr | Computational Methods for Medical and Cyber Security |
| title_full_unstemmed | Computational Methods for Medical and Cyber Security |
| title_short | Computational Methods for Medical and Cyber Security |
| title_sort | computational methods for medical and cyber security |
| topic | 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 thema EDItEUR::U Computing and Information Technology |
| topic_facet | 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 thema EDItEUR::U Computing and Information Technology |
| url | ONIX_20230220_9783036551166_75 |