LEADER 03582nam 22006495 450 001 9910768179903321 005 20251009094905.0 010 $a9789819974429 010 $a9819974429 024 7 $a10.1007/978-981-99-7442-9 035 $a(MiAaPQ)EBC30965601 035 $a(Au-PeEL)EBL30965601 035 $a(OCoLC)1410591900 035 $a(CKB)29026798000041 035 $a(DE-He213)978-981-99-7442-9 035 $a(EXLCZ)9929026798000041 100 $a20231122d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aActive Learning to Minimize the Possible Risk of Future Epidemics /$fby KC Santosh, Suprim Nakarmi 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (107 pages) 225 1 $aSpringerBriefs in Computational Intelligence,$x2625-3712 311 08$aPrint version: Santosh, K. C. Active Learning to Minimize the Possible Risk of Future Epidemics Singapore : Springer,c2023 327 $aIntroduction -- Active learning ? what, when, and where to deploy? -- Active learning ? review (cases) -- Active learning ? methodology -- Active learning ? validation -- Case study: Is my cough sound Covid-19?. 330 $aFuture epidemics are inevitable, and it takes months and even years to collect fully annotated data. The sheer magnitude of data required for machine learning algorithms, spanning both shallow and deep structures, raises a fundamental question: how big data is big enough to effectively tackle future epidemics? In this context, active learning, often referred to as human or expert-in-the-loop learning, becomes imperative, enabling machines to commence learning from day one with minimal labeled data. In unsupervised learning, the focus shifts toward constructing advanced machine learning models like deep structured networks that autonomously learn over time, with human or expert intervention only when errors occur and for limited data?a process we term mentoring. In the context of Covid-19, this book explores the use of deep features to classify data into two clusters (0/1: Covid-19/non-Covid-19) across three distinct datasets: cough sound, Computed Tomography (CT) scan, and chest x-ray (CXR). Not to be confused, our primary objective is to provide a strong assertion on how active learning could potentially be used to predict disease from any upcoming epidemics. Upon request (education/training purpose), GitHub source codes are provided. 410 0$aSpringerBriefs in Computational Intelligence,$x2625-3712 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aMachine learning 606 $aBig data 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aBig Data 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aBig data. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aBig Data. 676 $a006.3 700 $aSantosh$b K. C$01074647 701 $aNakarmi$b Suprim$01453434 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910768179903321 996 $aActive Learning to Minimize the Possible Risk of Future Epidemics$93656060 997 $aUNINA