Paper Contents
Abstract
In this paper, we use different deep learning techniques for the classification and detection of Escherichia coli (E. coli) through DNA analysis, deployed on Amazon Web Services to leverage cloud computing capabilities. E. coli is a common bacterium found in the intestines of humans and animals; while many strains are harmless, pathogenic variants can cause severe foodborne illnesses and outbreaks. Therefore, rapid and accurate detection is crucial for ensuring food safety and public health. Utilizing a convolutional neural network architecture, we analyze genomic sequences to accurately differentiate harmful E. coli strains from non-pathogenic ones. The model is trained on a diverse dataset of E. coli genomic data, achieving high accuracy in classification. By deploying this solution on AWS, we enable real-time processing and scalability, enhancing the speed and reliability of microbial detection. This innovative approach contributes to improving food safety and public health through efficient pathogen identification.
Copyright
Copyright © 2025 MANDALREDDY GEETHANJALI REDDY. This is an open access article distributed under the Creative Commons Attribution License.
