Towards a semantic structure for classifying IoT agriculture sensor datasets : An approach based on machine learning and web semantic technologies
With the increase in the number of IoT farming datasets, it has become so difficult to identify the right data for IoT agriculture applications. Therefore, a meaningful structure is needed to well understand, interpret and index IoT farming datasets. This paper proposes a new IoT farming ontology th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823002549 |
| _version_ | 1797663885639024640 |
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| author | Djakhdjakha Lynda Farou Brahim Seridi Hamid Cissé Hamadoun |
| author_facet | Djakhdjakha Lynda Farou Brahim Seridi Hamid Cissé Hamadoun |
| author_sort | Djakhdjakha Lynda |
| collection | DOAJ |
| description | With the increase in the number of IoT farming datasets, it has become so difficult to identify the right data for IoT agriculture applications. Therefore, a meaningful structure is needed to well understand, interpret and index IoT farming datasets. This paper proposes a new IoT farming ontology that allows the organization, the understanding, and the classification of IoT agriculture datasets knowledge as well as meta-data storage. For this, we have developed a new IoT agriculture taxonomy that helps to identify an IoT agriculture application based on the combination of various IoT agriculture sensors. The evaluation of the semantic IoT agriculture datasets classification, based on the background knowledge provided by the proposed ontology, was achieved using Machine Learning algorithms, including Logistic Regression, Decision Tree Classifier, K-Neighbors Classifier, Linear Discriminant Analysis, Gaussian NB, SVM, and Random Forest Regressor. The obtained results clearly show the effectiveness of the proposed ontology to classify IoT agriculture datasets with high performances and accuracy (0.98), (0.99) using Decision tree classifier and SVM respectively. |
| first_indexed | 2024-03-11T19:21:17Z |
| format | Article |
| id | doaj.art-932c11ccf5314feaaa84e792e63c18c1 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-11T19:21:17Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-932c11ccf5314feaaa84e792e63c18c12023-10-07T04:34:06ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-09-01358101700Towards a semantic structure for classifying IoT agriculture sensor datasets : An approach based on machine learning and web semantic technologiesDjakhdjakha Lynda0Farou Brahim1Seridi Hamid2Cissé Hamadoun3LabSTIC laboratory, Guelma University, 8 mai 1945 University, P. O. Box 401, 24000 Guelma, Algeria; Corresponding author.LabSTIC laboratory, Guelma University, 8 mai 1945 University, P. O. Box 401, 24000 Guelma, AlgeriaLabSTIC laboratory, Guelma University, 8 mai 1945 University, P. O. Box 401, 24000 Guelma, AlgeriaGuelma University, 8 mai 1945 University, P. O. Box 401, 24000 Guelma, AlgeriaWith the increase in the number of IoT farming datasets, it has become so difficult to identify the right data for IoT agriculture applications. Therefore, a meaningful structure is needed to well understand, interpret and index IoT farming datasets. This paper proposes a new IoT farming ontology that allows the organization, the understanding, and the classification of IoT agriculture datasets knowledge as well as meta-data storage. For this, we have developed a new IoT agriculture taxonomy that helps to identify an IoT agriculture application based on the combination of various IoT agriculture sensors. The evaluation of the semantic IoT agriculture datasets classification, based on the background knowledge provided by the proposed ontology, was achieved using Machine Learning algorithms, including Logistic Regression, Decision Tree Classifier, K-Neighbors Classifier, Linear Discriminant Analysis, Gaussian NB, SVM, and Random Forest Regressor. The obtained results clearly show the effectiveness of the proposed ontology to classify IoT agriculture datasets with high performances and accuracy (0.98), (0.99) using Decision tree classifier and SVM respectively.http://www.sciencedirect.com/science/article/pii/S1319157823002549IoT agriculture datasetSemantic webOntologyOWLSWRLPrecision agriculture |
| spellingShingle | Djakhdjakha Lynda Farou Brahim Seridi Hamid Cissé Hamadoun Towards a semantic structure for classifying IoT agriculture sensor datasets : An approach based on machine learning and web semantic technologies Journal of King Saud University: Computer and Information Sciences IoT agriculture dataset Semantic web Ontology OWL SWRL Precision agriculture |
| title | Towards a semantic structure for classifying IoT agriculture sensor datasets : An approach based on machine learning and web semantic technologies |
| title_full | Towards a semantic structure for classifying IoT agriculture sensor datasets : An approach based on machine learning and web semantic technologies |
| title_fullStr | Towards a semantic structure for classifying IoT agriculture sensor datasets : An approach based on machine learning and web semantic technologies |
| title_full_unstemmed | Towards a semantic structure for classifying IoT agriculture sensor datasets : An approach based on machine learning and web semantic technologies |
| title_short | Towards a semantic structure for classifying IoT agriculture sensor datasets : An approach based on machine learning and web semantic technologies |
| title_sort | towards a semantic structure for classifying iot agriculture sensor datasets an approach based on machine learning and web semantic technologies |
| topic | IoT agriculture dataset Semantic web Ontology OWL SWRL Precision agriculture |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823002549 |
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