Photovoltaic IOT based Priority Sense Method to diagnosis Disorders in Multi Cloud Environment

Abstract

This study presents a novel diagnostic approach that integrates solar cell measurement technology with a neural learning algorithm to enable real-time anxiety detection. Physiological signals are captured through a compact scanning circuit using a photovoltaic cell and carefully selected resistor values (1 kΩ, 64 kΩ, and 320 Ω). These signals are processed by a biologically inspired algorithm similar to the "Sense and Priority" model, which computes a Diagnostic Multiplier ranging from 0 to 1. Unlike traditional fixed models, this system dynamically evolves over time by reassessing priority weights based on historical data, allowing for individualized and context-aware assessments. Experimental evaluations demonstrated that the system achieves 98% accuracy and maintains a high area under the ROC curve (AUC), all while operating in under 50 milliseconds on energy-efficient microcontrollers. The findings highlight the method's suitability for wearable, edge-based mental health monitoring, offering a scalable, interpretable, and responsive solution.