MOODBOOST

bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorKarim, Dewan Ziaul
dc.contributor.advisorRahman, Rafeed
dc.contributor.authorKhan, Sami
dc.contributor.authorDas, Arnab
dc.contributor.authorTasnim, Zarin
dc.contributor.authorShahnewaz, MD Golam
dc.contributor.authorDebu, Soumitro Sharkar
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2025-09-15T05:12:25Z
dc.date.available2025-09-15T05:12:25Z
dc.date.copyright2025
dc.date.issued2025-06
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 64-65).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.en_US
dc.description.abstractAn AI-Powered App for the Future. An App For Mental Health — Revolutionizing How we approach Mental Wellness. What is MoodBoost? MoodBoost is an AI-Powered application designed to revolutionize the mental health space by providing personalized, real-time interventions based on user emotional needs. In a time of mental health crises, which affect over 970 million people across the globe (WHO, 2022) with problems like stress, anxiety, and depression, current solutions provide static content and generalized recommendations that are often not enough. MoodBoost fills this gap by weaving in Gemini’s state-of-the-art Natural Language Processing (NLP)—which analyzes user inputs in real time—to allow the app to create contextually relevant affirmations, actionable recommendations, and therapeutic content. The app’s central innovation is its ability to dynamically adjust to users’ emotional states. For instance, a user who logs a ”stressed” mood may receive a curated meditation guide, while one who feels ”motivated” could be offered suggestions for goal-setting activities. This level of personalization is enabled by a sophisticated technical architecture like Frontend Built with Flutter for crossplatform performance on Android and iOS, For Backend, Utilizes Django REST Framework to manage API logic, handle user authentication, and validate data. For Database, The Firebase is used for structured and scalable storage of user profiles, mood logs, and AI generated content. For AI Integration, We are using Gemini, it uses NLP models to process mood data for insights and affirmations — replies are relevant and empathetic. The app’s approach integrates the latest advancements in AI with an emphasis on user-centered design, positioning it as a vital component in the landscape of contemporary mental health care, providing a forward-looking, data-guided experience with the potential to supplant traditional offerings.en_US
dc.description.degreeBachelor of Science in Computer Science
dc.description.statementofresponsibilitySami Khan
dc.description.statementofresponsibilityArnab Das
dc.description.statementofresponsibilityZarin Tasnim
dc.description.statementofresponsibilityMD Golam Shahnewaz
dc.description.statementofresponsibilitySoumitro Sharkar Debu
dc.format.extent81 pages
dc.identifier.otherID 22341083
dc.identifier.otherID 19301029
dc.identifier.otherID 19301119
dc.identifier.otherID 20101466
dc.identifier.otherID 20301063
dc.identifier.urihttp://hdl.handle.net/10361/26731
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectAI-powered appen_US
dc.subjectMental wellnessen_US
dc.subjectMental healthen_US
dc.subjectMoodboosten_US
dc.subjectDeepSeeken_US
dc.subjectNatural language processingen_US
dc.subjectUser-centric designen_US
dc.subjectReal-time interventionsen_US
dc.subjectPersonalized interventionsen_US
dc.subjectTherapeutic contenten_US
dc.subjectAI integrationen_US
dc.subjectDjango REST frameworken_US
dc.subject.lcshNatural language processing (Computer science).
dc.subject.lcshArtificial intelligence--Medical applications.
dc.subject.lcshMental health services--Technological innovations.
dc.subject.lcshPsychiatry--Data processing.
dc.subject.lcshHealth informatics.
dc.titleMOODBOOSTen_US
dc.typeThesisen_US

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