Classification And Prediction Of Essential Oils Using Mobile Nets

Abstract

Due to its demonstrated effectiveness in handling massive data, deep learning techniques have become a significant development in various research fields. They can conduct non-linear processes and complex interactions, including biological science. Convolutional Neural Networks (CNN) were employed in this study to detect and predict the biological activities of essential oil-producing plants. The same Essential Oils (EO) dataset has also been subjected to a different class of machine learning methods, the Multiclass Neural Network. This is being done to assess the suggested CNN model's performance objectively. According to CNN and MNN's test results, the accuracy of the testing method is 98% and 81%, respectively. As a result, the EO-containing plants' bioactivities may now be classified and predicted using CNN. According to the final prediction method, the total accuracy is claimed to be around 97 per cent. A valuable model for predicting the bioactivities of EO-producing plants, at least in Egypt, is the deep learning model described here.