Paper Title
Rapid Identification of Avian Flu Using Logistic Regression and Convolutional Neural Networks

Abstract
In order to enhance the identification of avian flu outbreaks, this study compares CNN and logistic regression techniques. It seeks to improve rapid identification and general prediction by evaluating their efficacy and providing ideas for more effective response tactics When it came to the rapid detection and broad prediction of avian flu epidemics, Logistic Regression's algorithm fared better than CNN's, with an accuracy of 89.67% as opposed to CNN's 79.44%. According to the study's findings, Logistic Regression outperforms CNN in the job of quickly identifying and widely predicting avian flu epidemics, with an accuracy of 89.67% as opposed to CNN's accuracy of 79.44%. This demonstrates how well Logistic Regression works to improve techniques of detecting bird flu epidemics, highlighting its potential to enhance public health response plans and readiness.This study compares Convolutional Neural Networks (CNN) and Logistic Regression techniques to evaluate their efficacy in detecting avian flu cases.The study also explores potential strategies for optimizing detection methods, ultimately contributing to more efficient disease surveillance and response systems. Identification, avian flu outbreaks, comparison, CNN, logistic regression, rapid identification.