Enhancing Viral Disease Prediction and Detection Using ECNN and ERNN Techniques: Overcoming Limitations in T-Cell Response Data and Predictive Models

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Author(s): Asadi Srinivasulu (Indian Institute of Information Technology, Allahabad, India), Anupam Agrawal (Indian Institute of Information Technology, Allahabad, India), Anant Mohan (All India Institute of Medical Sciences, India)and A. V. Senthil Kumar (Hindusthan College of Arts and Science, India)
Copyright: 2026
Pages: 36
Source title: Intersecting AI and Medicine for Improved Care and Administrative Efficiency
Source Author(s)/Editor(s): Omar Ali (Abdullah Al Salem University, Kuwait), Abbas Amini (Abdullah Al Salem University, Kuwait)and Ahmad Al-Ahmad (Gulf University for Science and Technology, Kuwait)
DOI: 10.4018/979-8-3373-1772-4.ch009

Keywords: Medical Diagnosis and Practice / Medical Information Science Reference / Medical Technologies / Medicine & Healthcare

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Abstract

This study introduces a novel approach to improving viral disease prediction and detection through the use of Enhanced Convolutional Neural Networks (ECNN) and Enhanced Recurrent Neural Networks (ERNN). Our research addresses several key limitations in current systems, such as insufficient T-cell response data in elderly populations, inadequate predictive models for infectious diseases, privacy concerns in centralized data storage, low accuracy in COVID-19 detection, high false-positive rates, the lack of real-time processing, limited dataset availability, iterative learning complexity, limited model generalizability, and the absence of antiviral drug repurposing tools. By utilizing advanced AI and deep learning techniques, including federated learning, edge-cloud computing, and self-supervised learning, our proposed systems significantly enhance predictive accuracy, data privacy, and model generalizability.

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Enhancing Viral Disease Prediction and Detection Using ECNN and ERNN Techniques: Overcoming Limitations in T-Cell Response Data and Predictive Models

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