Title Active learning to minimize the possible risk of future epidemics / KC Santosh, Suprim Nakarmi

Published Singapore : Springer, [2023] ©2023

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Description 1 online resource (xvi, 96 pages) : illustrations Series SpringerBriefs in applied sciences and technology, 2191-5318 SpringerBriefs in computational intelligence, 2625-3712 SpringerBriefs in applied sciences and technology. SpringerBriefs in applied sciences and technology. Computational intelligence. Contents 1. Introduction -- 2. Active learning: what, when, and where to deploy? -- 3. Active learning: review -- 4. Active learning: methodology -- 5. Active learning: validation -- 6. Case study #1: Is my cough sound Covid-19? -- 7. Case study #2: Reading/analyzing CT scans -- 8. Case study #3: Reading/analyzing chest X-rays -- 9. Summary and take-home messages Summary 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 dataa 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.-- Provided by publisher Bibliography Includes bibliographical references Notes Professor K.C. Santosh is chair of the Department of Computer Science, University of South Dakota. Suprim Nakarmi is a research fellow for the Applied AI Research Lab, Department of Computer Science, University of South Dakota Print version record Subject Machine learning. Machine learning -- Case studies Data mining. Data mining -- Case studies Epidemiology -- Data processing Epidemiology -- Data processing -- Case studies Machine Learning Communicable Diseases -- epidemiology Data Mining Data mining. Epidemiology -- Data processing. Machine learning. Genre/Form Case studies. Études de cas. Form Electronic book Author Nakarmi, Suprim, author ISBN 9789819974429 9819974429

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