Active learning to minimize the possible risk of future epidemics / KC Santosh, Suprim Nakarmi.
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...
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Full Text (via Springer) |
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| Main Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Singapore :
Springer,
[2023]
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| Series: | SpringerBriefs in applied sciences and technology.
SpringerBriefs in applied sciences and technology. Computational intelligence. |
| Subjects: |
MARC
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| 100 | 1 | |a Santosh, K. C., |e author. |0 http://id.loc.gov/authorities/names/n2019017022 | |
| 245 | 1 | 0 | |a Active learning to minimize the possible risk of future epidemics / |c KC Santosh, Suprim Nakarmi. |
| 264 | 1 | |a Singapore : |b Springer, |c [2023] | |
| 264 | 4 | |c ©2023 | |
| 300 | |a 1 online resource (xvi, 96 pages) : |b illustrations. | ||
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| 545 | 0 | |a 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. | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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.-- |c Provided by publisher. | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Machine learning |v Case studies. | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 0 | |a Data mining |v Case studies. | |
| 650 | 0 | |a Epidemiology |x Data processing. | |
| 650 | 0 | |a Epidemiology |x Data processing |v Case studies. | |
| 700 | 1 | |a Nakarmi, Suprim, |e author. | |
| 776 | 0 | 8 | |i Print version: |a Santosh, K. C. |t Active learning to minimize the possible risk of future epidemics. |d Singapore : Springer, [2023] |z 9789819974412 |w (OCoLC)1397313128 |
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