Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt

Reliance on deep learning techniques has become an important trend in several science domains 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 Neu...

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Bibliographic Details
Published in:Scientific reports 2020-12, Vol.10 (1), p.21349-21349, Article 21349
Main Authors: El-Attar, Noha E., Hassan, Mohamed K., Alghamdi, Othman A., Awad, Wael A.
Format: Article
Language:English
Subjects:
631/114
631/449
631/45
Algorithms
Biological activity
Chemical composition
Deep Learning
Egypt
Endemic plants
Essential oils
Humanities and Social Sciences
Learning algorithms
Machine learning
multidisciplinary
Neural networks
Neural Networks, Computer
Oils & fats
Oils, Volatile - analysis
Plant Oils - analysis
Science
Science (multidisciplinary)
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Summary:Reliance on deep learning techniques has become an important trend in several science domains 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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-78449-1