dc.contributor.advisor | Rhaman, Md. Khalilur | |
dc.contributor.author | Islam, Md. Nazmul | |
dc.contributor.author | Al-Amin, Md. | |
dc.contributor.author | Hassan, Md. Zaed | |
dc.contributor.author | Islam, Tamzid | |
dc.date.accessioned | 2024-05-20T08:35:56Z | |
dc.date.available | 2024-05-20T08:35:56Z | |
dc.date.copyright | ©2024 | |
dc.date.issued | 2024-01 | |
dc.identifier.other | ID: 19301033 | |
dc.identifier.other | ID: 19301028 | |
dc.identifier.other | ID: 19301024 | |
dc.identifier.other | ID: 19301050 | |
dc.identifier.uri | http://hdl.handle.net/10361/22889 | |
dc.description | This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2024. | en_US |
dc.description | Cataloged from PDF version of thesis. | |
dc.description | Includes bibliographical references (pages 58-60). | |
dc.description.abstract | The increasing number of elderly individuals living alone has emerged as a pressing
global concern. Our research aims to address this issue by developing advanced
modules that can be integrated into a system that enhances the quality of life for
older adults. The modules focus on medicine detection, fall detection, reminders for
important tasks and events and providing companionship through friendly verbal
interactions. Through the integration of cutting-edge deep learning techniques, diverse
models and natural language processing (NLP), we have successfully designed
an effective medication and well-being assistant. These modules use computer vision
technology along with reinforced learning from human feedback and convolutional
neural networks (CNNs) to reach our goal. The modules can be integrated into systems
to empower elderly individuals to lead more active and fulfilling lives. Finally,
this research contributes to the well-being and happiness of the elderly, highlighting
the significance of comprehensive support systems in promoting their overall
well-being. | en_US |
dc.description.statementofresponsibility | Md. Nazmul Islam | |
dc.description.statementofresponsibility | Md. Al-Amin | |
dc.description.statementofresponsibility | Md. Zaed Hassan | |
dc.description.statementofresponsibility | Tamzid Islam | |
dc.format.extent | 72 pages | |
dc.language.iso | en | en_US |
dc.publisher | Brac University | en_US |
dc.rights | Brac 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.subject | Companion system | en_US |
dc.subject | Deep learning | en_US |
dc.subject | CNN | en_US |
dc.subject | Reinforced learning | en_US |
dc.subject | NLP | en_US |
dc.subject | Medication habit | en_US |
dc.subject.lcsh | Natural language processing (Computer science) | |
dc.subject.lcsh | Deep learning (Machine learning) | |
dc.title | Health trauma and well-being assistant for Bengali seniors in household: a multimodal approach | en_US |
dc.type | Thesis | en_US |
dc.contributor.department | Department of Computer Science and Engineering, Brac University | |
dc.description.degree | B.Sc in Computer Science and Engineering | |