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

Abstract 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, Convolut...

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Autores principales:	Noha E. El-Attar, Mohamed K. Hassan, Othman A. Alghamdi, Wael A. Awad
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7e383497f32f422b8e8dbe46b3ce1c22
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spelling	oai:doaj.org-article:7e383497f32f422b8e8dbe46b3ce1c222021-12-02T16:08:37ZDeep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt10.1038/s41598-020-78449-12045-2322https://doaj.org/article/7e383497f32f422b8e8dbe46b3ce1c222020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78449-1https://doaj.org/toc/2045-2322Abstract 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.Noha E. El-AttarMohamed K. HassanOthman A. AlghamdiWael A. AwadNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Noha E. El-Attar Mohamed K. Hassan Othman A. Alghamdi Wael A. Awad Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt
description	Abstract 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.
format	article
author	Noha E. El-Attar Mohamed K. Hassan Othman A. Alghamdi Wael A. Awad
author_facet	Noha E. El-Attar Mohamed K. Hassan Othman A. Alghamdi Wael A. Awad
author_sort	Noha E. El-Attar
title	Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt
title_short	Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt
title_full	Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt
title_fullStr	Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt
title_full_unstemmed	Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt
title_sort	deep learning model for classification and bioactivity prediction of essential oil-producing plants from egypt
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/7e383497f32f422b8e8dbe46b3ce1c22
work_keys_str_mv	AT nohaeelattar deeplearningmodelforclassificationandbioactivitypredictionofessentialoilproducingplantsfromegypt AT mohamedkhassan deeplearningmodelforclassificationandbioactivitypredictionofessentialoilproducingplantsfromegypt AT othmanaalghamdi deeplearningmodelforclassificationandbioactivitypredictionofessentialoilproducingplantsfromegypt AT waelaawad deeplearningmodelforclassificationandbioactivitypredictionofessentialoilproducingplantsfromegypt
_version_	1718384479399051264

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

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