Work

Predicting and Identifying Antimicrobial Resistance in the Marine Environment Using AI and Machine Learning

Year: 2024

Type: conference-paper

Abstract: Antimicrobial resistance (AMR) poses a serious threat to public health and serves as a vital reservoir for resistant microorganisms. Antimicrobial resistance (AMR) is an increasingly critical public health issue that requires precise and efficient methodologies to achieve prompt results. The accurate and early detection of AMR is crucial, as its absence can pose life-threatening risks to diverse ecosystems, including the marine environment. This study focuses on evaluating the diameters of the disc diffusion zone and employs Artificial Intelligence (AI) and Machine Learning (ML) techniques such as image segmentation, data augmentation, and deep learning methods to enhance accuracy in predicting microbial resistance.

Source: IEEE International Conference on Electronics, Circuits and Systems (ICECS)

DOI: 10.1109/icecs61496.2024.10849269

Authors: Faranak Fough, Yafan Zhao, Faisal Mehmood Shah

Institution: Robert Gordon University

Language: English

Topic: Pharmaceutical and Antibiotic Environmental Impacts

Subfield: Pollution

Field: Environmental Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4406895140