Monitoring food safety risks using machines learning techniques
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Date
2024-06
Journal Title
Journal ISSN
Volume Title
Publisher
Mohamed Boudiaf University of M'sila
Abstract
In this thesis, six machine learning algorithms (simple linear regression, polynomial regression, logistic
regression, random forests, decision tree, and support vector machine) were explored to predict and
classify food safety risks. A comprehensive dataset including price data and weather data affecting food
safety risks was collected and used to train and test the models. Performance metrics such as accuracy,
precision, and recall were used to evaluate the models. The results showed that all algorithms performed
well, but the polynomial regression model achieved the best correlation coefficient for prediction models,
while the decision tree achieved the highest accuracy for classification models. This research highlights
the effectiveness of machine learning in predicting and classifying food safety risks and underscores the
importance of leveraging advanced techniques to mitigate these risks and ensure food safety for
consumers.
Description
Keywords
Food security, machine learning, food safety, risk prediction, regression, classification