Enhancing healthcare classification with hybrid multimedia data processing and deep learning TNBO FCNN approach in IoT-enabled environments
Leeladhar Chourasiya, Umesh Kumar Lillohre, Abhishek Kumar Pandey, Sarita Simaiya, Shimaa A. Hussien, Ehab Seif Ghith, Monish Khan · 2025
Read Enhancing healthcare classification with hybrid multimedia data processing and deep learning TNBO FCNN approach in IoT-enabled environments by Leeladhar Chourasiya, Umesh Kumar Lillohre, Abhishek Kumar Pandey, Sarita Simaiya, Shimaa A. Hussien, Ehab Seif Ghith, Monish Khan (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12835001.txt.
Description
Clinics now collect voice clips, images, and signals from patients through linked devices, and sorting that flood fast and safely is hard. Leeladhar Chourasiya and co-authors built a sorting system for that task in work from twenty twenty-five. Their setup blends a tuned neural net with a smart search method that also picks the best data routes.
A shared ledger guards patient data as it moves between sites, keeping a clear and fixed record. Tests on known data sets beat two older models on the main scores for correct calls. The team tuned the setup for low energy use and quick replies, with remote care in mind. They also flag moral questions and next steps for wider use in smart clinics.
Catalog record
| CREATOR | Leeladhar Chourasiya, Umesh Kumar Lillohre, Abhishek Kumar Pandey, Sarita Simaiya, Shimaa A. Hussien, Ehab Seif Ghith, Monish Khan |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 133 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |