Detection of Selective Foodborne Pathogen by using Artificial Intelligence

Dayang Najwa, Awg Baki (2018) Detection of Selective Foodborne Pathogen by using Artificial Intelligence. [Final Year Project Report] (Unpublished)

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Abstract

Foodbome pathogens can cause a serious outbreak domestically and globally, therefore an early detection must be made to prevent and tackle the widespread of contaminated sources in the environment. A method of detection using Artificial Intelligence or AI has been used in this project by utilizing the images of selected foodbome pathogens under Light Microscope. A software called MATLAB was used to train Artificial Neural Network (ANN) into classifying Escherichia coli, Staphylococcus aureus and Bacillus cereus accordingly. The outcome of this study shows ANN successfully classifies the selected bacteria with minimal misclassification. Hopefully, this study can be an alternative method for microbiologist to detect foodbome pathogen based on their morphology and can be improvised for a better detection in the future.

Item Type: Final Year Project Report
Additional Information: Project report (B.Sc.) -- Universiti Malaysia Sarawak, 2018.
Uncontrolled Keywords: Artificial Intelligence, ANN, MATLAB, morphology.
Subjects: Q Science > Q Science (General)
Q Science > QR Microbiology
Divisions: Academic Faculties, Institutes and Centres > Faculty of Resource Science and Technology
Depositing User: Patrick
Date Deposited: 27 Apr 2021 09:34
Last Modified: 27 Apr 2021 09:34
URI: http://ir.unimas.my/id/eprint/35171

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