LEADER 04139nam  2201153z- 450 
001 9910595066903321
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035   $a(CKB)5680000000080864
035   $a(oapen)https://directory.doabooks.org/handle/20.500.12854/92138
035   $a(EXLCZ)995680000000080864
100   $a20202209d2022      |y             0
101 0 $aeng
135   $aurmn|---annan
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aComputational Methods for Medical and Cyber Security
210   $aBasel$cMDPI Books$d2022
215   $a1 electronic resource (228 p.)
311   $a3-0365-5115-8 
311   $a3-0365-5116-6 
330   $aOver 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.
610   $afintech
610   $afinancial technology
610   $ablockchain
610   $adeep learning
610   $aregtech
610   $aenvironment
610   $asocial sciences
610   $amachine learning
610   $alearning analytics
610   $astudent field forecasting
610   $aimbalanced datasets
610   $aexplainable machine learning
610   $aintelligent tutoring system
610   $aadversarial machine learning
610   $atransfer learning
610   $acognitive bias
610   $astock market
610   $abehavioural finance
610   $ainvestor?s profile
610   $aTeheran Stock Exchange
610   $aunsupervised learning
610   $aclustering
610   $abig data frameworks
610   $afault tolerance
610   $astream processing systems
610   $adistributed frameworks
610   $aSpark
610   $aHadoop
610   $aStorm
610   $aSamza
610   $aFlink
610   $acomparative analysis
610   $aa survey
610   $adata science
610   $aeducational data mining
610   $asupervised learning
610   $asecondary education
610   $aacademic performance
610   $atext-to-SQL
610   $anatural language processing
610   $adatabase
610   $amachine translation
610   $amedical image segmentation
610   $aconvolutional neural networks
610   $aSE block
610   $aU-net
610   $aDeepLabV3plus
610   $acyber-security
610   $amedical services
610   $acyber-attacks
610   $adata communication
610   $adistributed ledger
610   $aidentity management
610   $aRAFT
610   $aHL7
610   $aelectronic health record
610   $aHyperledger Composer
610   $acybersecurity
610   $apassword security
610   $abrowser security
610   $asocial media
610   $aANOVA
610   $aSPSS
610   $ainternet of things
610   $acloud computing
610   $acomputational models
610   $ametaheuristics
610   $aphishing detection
610   $awebsite phishing
700   $aLuo$b Suhuai$4edt$01322438
702   $aShaukat$b Kamran$4edt
702   $aLuo$b Suhuai$4oth
702   $aShaukat$b Kamran$4oth
906   $aBOOK
912   $a9910595066903321
996   $aComputational Methods for Medical and Cyber Security$93035003
997   $aUNINA