A Data-Driven Diversity Scorecard Framework for Global Workforce Management

Abstract

The primary objective of this programme is to create deep learning models that examine pupillary dynamics in the detection of genetic diseases. Key neurological and physiological indicators that may be associated with underlying hereditary disorders include variations in pupil size, reflex speed, and light adaptation patterns. The objective of this research is to evaluate and understand pupillary activity as measured by eye-tracking and image sensors by utilising state-of-the-art deep learning techniques, including CNNs, RNNs, and LSTM models. The proposed method, which aims to enhance the precision, effectiveness, and early detection of genetic abnormalities in comparison to current clinical assessment procedures, involves the extraction of precise spatial and temporal information from pupillary movement data. A non-invasive, cost-effective, and dependable diagnostic framework that integrates AI with neurological and ocular indicators is now available to healthcare providers, providing them with personalised treatment plans and predictive genetic analysis.