01600oam 2200433Ia 450 991069342810332120230902161734.0(CKB)5470000002395843(OCoLC)65378538(EXLCZ)99547000000239584320060331d2005 ua 0engurmn|||||||||txtrdacontentcrdamediacrrdacarrierIntelligence in the Civil War[electronic resource]Washington, DC :Central Intelligence Agency, Office of Public Affairs,[2005?]1 online resource (50 pages) illustrations, maps, portraitsTitle from home page (CIA, viewed Mar. 31, 2006)."The Office of Public Affairs (OPA) wishes to thank Thomas Allen for his work in drafting and preparing this publication"--P. 3.1-929667-12-4 Military intelligenceUnited StatesHistory19th centuryIntelligence serviceUnited StatesSpiesUnited StatesHistory19th centuryUnited StatesHistoryCivil War, 1861-1865Military intelligenceMilitary intelligenceHistoryIntelligence serviceSpiesHistoryAllen Thomas164382United States.Central Intelligence Agency.Public Affairs Office.SINSINOCLCQGPOBOOK9910693428103321Intelligence in the Civil War3476993UNINA04139nam 2201153z- 450 991059506690332120231214133325.0(CKB)5680000000080864(oapen)https://directory.doabooks.org/handle/20.500.12854/92138(EXLCZ)99568000000008086420202209d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierComputational Methods for Medical and Cyber SecurityBaselMDPI Books20221 electronic resource (228 p.)3-0365-5115-8 3-0365-5116-6 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.fintechfinancial technologyblockchaindeep learningregtechenvironmentsocial sciencesmachine learninglearning analyticsstudent field forecastingimbalanced datasetsexplainable machine learningintelligent tutoring systemadversarial machine learningtransfer learningcognitive biasstock marketbehavioural financeinvestor’s profileTeheran Stock Exchangeunsupervised learningclusteringbig data frameworksfault tolerancestream processing systemsdistributed frameworksSparkHadoopStormSamzaFlinkcomparative analysisa surveydata scienceeducational data miningsupervised learningsecondary educationacademic performancetext-to-SQLnatural language processingdatabasemachine translationmedical image segmentationconvolutional neural networksSE blockU-netDeepLabV3pluscyber-securitymedical servicescyber-attacksdata communicationdistributed ledgeridentity managementRAFTHL7electronic health recordHyperledger Composercybersecuritypassword securitybrowser securitysocial mediaANOVASPSSinternet of thingscloud computingcomputational modelsmetaheuristicsphishing detectionwebsite phishingLuo Suhuaiedt1322438Shaukat KamranedtLuo SuhuaiothShaukat KamranothBOOK9910595066903321Computational Methods for Medical and Cyber Security3035003UNINA