자료유형  

 학위논문

Control Number  

0016933733

International Standard Book Number  

9798380257480

Dewey Decimal Classification Number  

330

Main Entry-Personal Name  

Tabar, Maryam.

Publication, Distribution, etc. (Imprint  

[S.l.] : The Pennsylvania State University., 2023

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2023

Physical Description  

1 online resource(128 p.)

General Note  

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

General Note  

Advisor: Lee, Dongwon;Yadav, Amulya.

Dissertation Note  

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

Restrictions on Access Note  

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

Summary, Etc.  

요약There 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.

Subject Added Entry-Topical Term  

Sparsity.

Subject Added Entry-Topical Term  

Agricultural production.

Subject Added Entry-Topical Term  

Forecasting.

Subject Added Entry-Topical Term  

Substance use disorder.

Subject Added Entry-Topical Term  

Agriculture.

Subject Added Entry-Topical Term  

Poverty.

Subject Added Entry-Topical Term  

Homeless people.

Subject Added Entry-Topical Term  

Low income groups.

Subject Added Entry-Topical Term  

Neural networks.

Subject Added Entry-Topical Term  

Farms.

Subject Added Entry-Topical Term  

Drug use.

Subject Added Entry-Topical Term  

Abiotic stress.

Subject Added Entry-Topical Term  

Neighborhoods.

Subject Added Entry-Topical Term  

Crowdsourcing.

Subject Added Entry-Topical Term  

Moving & housing expenses.

Subject Added Entry-Topical Term  

Social structure.

Subject Added Entry-Topical Term  

Sociology.

Subject Added Entry-Topical Term  

Sustainability.

Index Term-Uncontrolled  

Social challenges

Index Term-Uncontrolled  

Vulnerable communities

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Machine Learning

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Social sciences

Index Term-Uncontrolled  

Real-world domains

Added Entry-Corporate Name  

The Pennsylvania State University.

Host Item Entry  

Dissertations Abstracts International. 85-03A.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:641390