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Active learning to minimize the possible risk of future epidemics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Active learning to minimize the possible risk of future epidemicsby KC Santosh, Suprim Nakarmi.
Author:	Santosh, K. C.
other author:	Nakarmi, Suprim.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xvi, 96 p. :illustrations, digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-981-99-7442-9
ISBN:	9789819974429$q(electronic bk.)

Active learning to minimize the possible risk of future epidemics
Santosh, K. C.

Active learning to minimize the possible risk of future epidemics[electronic resource] /by KC Santosh, Suprim Nakarmi. - Singapore :Springer Nature Singapore :2023. - xvi, 96 p. :illustrations, digital ;24 cm. - SpringerBriefs in computational intelligence,2625-3712. - SpringerBriefs in computational intelligence..

Introduction -- 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?.

Future 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.

ISBN: 9789819974429$q(electronic bk.)

Standard No.: 10.1007/978-981-99-7442-9doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .S26 2023

Dewey Class. No.: 006.31

Active learning to minimize the possible risk of future epidemics
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