Design and Implementation of LBW – A Mental Health Application for Children

dc.contributor.advisorSchmidt, Douglas C
dc.contributor.advisorWhite, Christopher J
dc.creatorWu, Sexi
dc.creator.orcid0000-0003-0203-6309
dc.date.accessioned2022-01-10T16:46:53Z
dc.date.created2021-12
dc.date.issued2021-11-19
dc.date.submittedDecember 2021
dc.date.updated2022-01-10T16:46:53Z
dc.description.abstractAs COVID-19 continues to spread around the world, it is increasingly important to pay attention to the mental health of those who are isolated at home. Love In A Big World (LBW) offers a content-driven mental health solution for middle childhood (ages 8-14), their families, and their educators that is scalable and suitable for a COVID and post COVID world. The key differentiators of our LBW app over our competition are that we are kid-centric, and our platform is supported by deep learning-based NLP methods. Three NLP models have been applied in the Android Platform. The first SBRERT model focuses on sentence encoding and enables similarity comparison of contents. The second Toxic-BERT handles potential threatening texts for kids. The third GPT-3 model that is compared with fine-tuned GPT-2 model provides a conversational chatbot. The technologies and strategies used at both the client-side and server-side are discussed. The process of functional mobile application development is concealed from the design stage to implementation stage in this paper.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1803/16986
dc.language.isoen
dc.subjectAndroid Application, React Native, Machine Learning
dc.titleDesign and Implementation of LBW – A Mental Health Application for Children
dc.typeThesis
dc.type.materialtext
local.embargo.lift2022-12-01
local.embargo.terms2022-12-01
thesis.degree.disciplineComputer Science
thesis.degree.grantorVanderbilt University Graduate School
thesis.degree.levelMasters
thesis.degree.nameMS

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