dc.contributor.advisor | Schmidt, Douglas C | |
dc.contributor.advisor | White, Christopher J | |
dc.creator | Wu, Sexi | |
dc.date.accessioned | 2022-01-10T16:46:53Z | |
dc.date.created | 2021-12 | |
dc.date.issued | 2021-11-19 | |
dc.date.submitted | December 2021 | |
dc.identifier.uri | http://hdl.handle.net/1803/16986 | |
dc.description.abstract | As 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.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | Android Application, React Native, Machine Learning | |
dc.title | Design and Implementation of LBW – A Mental Health Application for Children | |
dc.type | Thesis | |
dc.date.updated | 2022-01-10T16:46:53Z | |
dc.type.material | text | |
thesis.degree.name | MS | |
thesis.degree.level | Masters | |
thesis.degree.discipline | Computer Science | |
thesis.degree.grantor | Vanderbilt University Graduate School | |
local.embargo.terms | 2022-12-01 | |
local.embargo.lift | 2022-12-01 | |
dc.creator.orcid | 0000-0003-0203-6309 | |