DLSDHMS: Design of a deep learning-based analysis model for secure and distributed hospital management using context-aware sidechains.

Designing an efficient hospital management solution requires the integration of multidomain operations that include secure storage, alert system modelling, infrastructure management, staff management, report analysis, and feedback-based learning tasks. Existing hospital management models are either highly complex or do not incorporate comprehensive deep learning analysis, which limits their deployment capabilities. Moreover, most of these models use mutable storage solutions, which restricts their trust levels under multi-patient to multi-doctor mapping scenarios.

Object Detection in Adverse Weather for Autonomous Driving through Data Merging and YOLOv8.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle's environment through sensors. Perception is challenging, wherein it suffers from dynamic objects and continuous environmental changes. The issue grows worse due to interrupting the quality of perception via adverse weather such as snow, rain, fog, night light, sand storms, strong daylight, etc.