Enhancing healthcare classification with hybrid multimedia data processing and deep learning TNBO FCNN approach in IoT-enabled environments.

ABSTRACT: The rapid expansion of IoT-based healthcare systems has led to an explosion of multimodal patient data (e.g., voice, images, signals), raising concerns about accurate classification, real-time processing, and data security. Existing machine learning approaches often suffer from limited feature extraction, high computational demands, and inadequate privacy protection, particularly in decentralised and resource-constrained environments. Furthermore, the lack of robust routing and data integrity mechanisms hinders their deployment in real-world healthcare applications. To address these challenges, we propose a hybrid deep learning framework that integrates Tunable Nonlinear Bayesian Optimisation (TNBO) for efficient routing and hyper-parameter tuning with a Fully Connected Neural Network (FCN

SUBMITTER: Chourasiya L 

PROVIDER: S-EPMC12835001 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature