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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

DeepCov: Effective Prediction Model of COVID-19 Using CNN Algorithm.

Mohammad Diqi¹, Sri Hasta Mulyani¹, Rike Pradila²

¹Universitas Respati Yogyakarta, Yogyakarta, Indonesia.

SN Computer Science

|May 23, 2023

Summary

This summary is machine-generated.

This study introduces a Convolutional Neural Network (CNN) model for accurate COVID-19 outbreak prediction. The model effectively forecasts new cases and deaths, aiding early prevention strategies.

Keywords:

CNN COVID-19 Deep learning Prediction

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Area of Science:

Epidemiology
Data Science
Machine Learning

Background:

Predicting COVID-19 outbreaks presents significant challenges due to large datasets.
Conventional prediction methods often fall short in accurately reflecting disease trends.

Purpose of the Study:

To develop an accurate model for predicting long-term COVID-19 outbreaks.
To leverage deep learning for early prevention strategies.

Main Methods:

Utilized a Convolutional Neural Network (CNN) model.
Analyzed features from a comprehensive COVID-19 dataset.
Employed regression analysis to quantify prediction accuracy.

Main Results:

Achieved high accuracy with minimal loss in predicting new COVID-19 cases.
Reported a Root Mean Square Error (RMSE) of 0.00070 and Mean Absolute Percentage Error (MAPE) of 0.02440 for case prediction.
Obtained an RMSE of 0.00468 and MAPE of 0.06446 for predicting new COVID-19 deaths.

Conclusions:

The proposed CNN model accurately predicts the trend of positive COVID-19 cases and deaths.
The model's performance supports its utility in early outbreak prevention.