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Mitigating Social Challenges Among V...

Tabar, Maryam.

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Mitigating Social Challenges Among Vulnerable Communities With Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mitigating Social Challenges Among Vulnerable Communities With Machine Learning./
作者:	Tabar, Maryam.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	128 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
Contained By:	Dissertations Abstracts International85-03A.
標題:	Sparsity. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30551240
ISBN:	9798380257480

Mitigating Social Challenges Among Vulnerable Communities With Machine Learning.
Tabar, Maryam.

Mitigating Social Challenges Among Vulnerable Communities With Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 128 p.

Source: Dissertations Abstracts International, Volume: 85-03, Section: A.

Thesis (Ph.D.)--The Pennsylvania State University, 2023.

This item must not be sold to any third party vendors.

There are various environmental and social challenges that disproportionately affect vulnerable communities in society. Extensive research has been conducted in various fields, such as agricultural sciences and social sciences, to understand some of those challenges and design intervention/prevention programs. However, effective/efficient implementation of mitigation plans is usually highly challenging in the field. Inspired by recent advances in Machine Learning (ML), this dissertation mainly focuses on the adaptation of ML-based techniques in certain real-world domains under various challenges to help address several social problems in a more effective/efficient manner. In fact, it focuses on two real-world domains, AI for Agriculture and AI for Social Welfare of Housing-Insecure Low-Income Americans, and addresses some challenges by proposing solutions tailored to the characteristics of the motivating problem domain. For example, to address the challenge of a lack of ground-truth labels, it proposes a label generation approach that translates the findings of social science research to high-quality labels to facilitate training ML models. Additionally, it proposes a loss function to improve the learning of neural networks when only coarse-grained ground-truth labels are available. In conclusion, this dissertation aims to adapt ML algorithms in specific real-world domains with particular challenges and characteristics.

ISBN: 9798380257480Subjects--Topical Terms:

3680690
Sparsity.
Subjects--Index Terms:

Social challenges

Mitigating Social Challenges Among Vulnerable Communities With Machine Learning.
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650 4 $a Forecasting. $3 547120
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