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Updated: May 12, 2026

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Hybrid optimal feature selection-based iterative deep convolution learning for COVID-19 classification system.

P Santosh Kumar Patra¹, Biswajit Tripathy²

¹Research Scholar, Department of Computer Science and Engineering, Biju Patnaik University of Technology, Rourkela, Odisha, 769015, India.

Computers in Biology and Medicine

|August 22, 2024

Summary

This summary is machine-generated.

This study introduces the Optimal Iterative COVID-19 Classification Network (OICC-Net) for accurate COVID-19 detection using Internet of Things (IoT) data. The AI-driven OICC-Net achieves high accuracy in identifying SARS-CoV-2, improving early diagnosis capabilities.

Keywords:

Black widow optimization COVID-19 pandemic Internet of things Iterative deep convolution learning Machine learning optimization Particle swarm optimization Remote patient monitoring

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Area of Science:

Medical Informatics
Artificial Intelligence in Healthcare
Computational Biology

Background:

The COVID-19 pandemic highlighted the need for rapid and accurate diagnostic tools.
Internet of Things (IoT) devices generate vast datasets crucial for healthcare applications.
Traditional Artificial Intelligence (AI) methods struggle with the complexity and scale of IoT data for disease prediction.

Purpose of the Study:

To develop an AI-based system for early COVID-19 detection using IoT data.
To address the limitations of traditional AI in analyzing complex IoT datasets for disease prediction.
To implement and evaluate the Optimal Iterative COVID-19 Classification Network (OICC-Net) for enhanced diagnostic accuracy.

Main Methods:

Dataset normalization using preprocessing techniques.
Feature extraction using the random forest infused particle swarm-based black widow optimization (RFI-PS-BWO) algorithm to identify disease-specific patterns.
Iterative deep convolution learning (IDCL) for enhanced feature selection and dimensionality reduction.
Classification using a one-dimensional convolutional neural network (1D-CNN) trained on a large COVID-19 dataset.

Main Results:

The RFI-PS-BWO algorithm successfully extracted disease-specific patterns, differentiating SARS-CoV-2 from similar viruses.
The IDCL method improved feature representation and reduced data dimensionality.
The OICC-Net achieved high performance metrics: 99.97% F1-score, 100% sensitivity, 100% specificity, 99.98% precision, and 99.99% recall.
The proposed OICC-Net demonstrated superior accuracy compared to existing methods.

Conclusions:

The OICC-Net system effectively utilizes AI and IoT data for accurate COVID-19 detection.
The combination of RFI-PS-BWO, IDCL, and 1D-CNN offers a robust approach for disease prediction.
This AI-driven method significantly enhances early detection and diagnostic capabilities for infectious diseases like COVID-19.