A Deep Learning-Powered FastAPI Web Application for Diabetes Mellitus Prediction:Model Development and Evaluation

dc.contributor.authorTasfi, Labib Al
dc.date.accessioned2026-05-03T09:26:01Z
dc.date.available2026-05-03T09:26:01Z
dc.date.issued2025-09-21
dc.descriptionThesis Report
dc.description.abstractThis project proposes an IoT-based dual power mobile network monitoringsystemdesigned to ensure continuous connectivity during power outages. It involvestwonetwork towers—one connected only to the grid and the other integrated withbothgrid and solar power sources. A physical switch is used to simulate load sheddingconditions. Upon load shedding, both towers initially drop from4Gto 2G. Thetowerequipped with solar power automatically recovers and restores the networkfrom2Gback to 4G using solar energy. The system includes a display that visuallyrepresents the current network status and power source. It also features anIoTplatform that updates the real-time condition of each tower, showing messages suchas "LOAD SHEDDING, Taking Power From Solar Panel" or "NO LOADSHEDDING, Taking Power From Grid." This project demonstrates an efficient approachto powerredundancy, network stability, and remote monitoring for future-ready telecominfrastructure
dc.identifier.citationSWT
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17128
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/17128
dc.language.isoen_US
dc.publisherDaffodil International University
dc.sourceDIU Institutional Repository
dc.subjectMachine Learning Model
dc.subjectDiabetes Prediction System
dc.subjectDeep Learning
dc.subjectWeb Application
dc.subjectFastAPI Deployment Healthcare
dc.titleA Deep Learning-Powered FastAPI Web Application for Diabetes Mellitus Prediction:Model Development and Evaluation
dc.typeThesis

Files

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
P30220.pdf.txt
Size:
58.49 KB
Format:
Adobe Portable Document Format

Collections