including biological science, due to its proven efficiency in manipulating big data that are often
characterized by their non-linear processes and complicated relationships. In this study, Convolutional
Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in
classifying and predicting the biological activities of the essential oil-producing plant/s through their
chemical compositions. The model is established based on the available chemical composition’s
information of a set of endemic Egyptian plants and their biological activities. Another type of
machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same
Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN
model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%,
respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and
predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final
prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could
be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing
plants. |
