1 Affiliation : Master's Student - Damascus University - College of Science – Syria
Email : mohammed.shikho1995@ damascusuniversity.edu.sy
2 Affiliation : Faculty member, Damascus University, Faculty of Science , Syria
Email : firstname.lastname@example.org
3 Affiliation : Laboratory of Information Processing, Faculty of Science Ben M’Sik, University Hassan II, B.P 7955,
Email : email@example.com
The world has always suffered and from diseases and epidemics, and the coronavirus is one of the most dangerous viruses that threatened human life that requires the use of all scientific methods and means to respond to it and reduce its spread by early detection of infections and taking necessary measures In view of the significant role that artificial intelligence plays in most fields of science, it has become one of the most important scientific methods used to resolve complex issues and has been harnessed in medical diagnosis, one of the most complex areas. Many AI and machine learning algorithms have been used to diagnose and detect diseases in general and coronavirus in particular. The support vector machine (svm) machine algorithm was one of the most important algorithms in this area and is one of the most effective compilations used in the knowledge extraction process In spite of all this, the results they present remain incomplete because classification issues do not deal with cognitive uncertainties such as ambiguity, neutrality and inconsistency associated with perception of human thinking, This adversely affects the work of a classic support vector machine and affects the accurate diagnosis of the disease To solve this problem, we have done this research using a Neutrosophic Support Vector Machine because it takes into account all possible cases during the study of the sample and it reduces the impact of extreme values. This increases the accuracy of the results when diagnosing coronavirus symptoms. The study was conducted according to the following steps:
1. We extract features from chest radiographs based on GLCM
2. We form a neutrosophic dataset.
3. We train Neutrosophic Support Machine N-SVM on new data.
4. We record the results.
Comparing the results, we got using the upgraded N-SVM algorithm with the classic SVM algorithm results we found that it gives a more accurate diagnosis of the disease.
Corona virus; Gray-level Co-Occurrence matrix; Neutrosophic Support Vector Machine algorithm.
 WHO. Director-General’s Opening Remarks at the Media Briefing on COVID-19–11 March 2020.
 people-at-higher-risk .Coronavirus Disease 2019 (COVID-19). [Online]. Available:
 Rabaan AA, Al-Ahmed SH, Haque S, Sah R, Tiwari R, Malik YS, Dhama K, Yatoo MI, BonillaAldana DK, Rodriguez-Morales AJ. SARS-CoV-2, SARS-CoV, and MERS-COV: A comparative
overview. Infez Med. 2020 Ahead Of Print Jun 1;28(2):174-184. PMID: 32275259,2020
 J. H. U. Medicine. (2020). Coronavirus COVID-19 Global Cases by the Center for Systems Science and
Engineering (CSSE) at Johns Hopkins Uni- versity (JHU). [Online]. Available:
 W. Wang, Y. Xu, R. Gao, R. Lu, K. Han, G. Wu, and W. Tan, ‘‘Detection of SARS-CoV-2 in different
types of clinical specimens,’’ Jama, vol. 323, no. 18, pp. 1843–1844, 2020
 N. Wetsman. Coronavirus Testing Shouldn’T Be This Complicated.
 M. Dahmani, M. E. H. Chowdhury, A. Khandakar, T. Rahman,
 K. Al-Jayyousi, A. Hefny, and S. Kiranyaz, ‘‘An intelligent and low- cost eye-tracking system for
motorized wheelchair control,’’ 2020, arXiv:2005.02118. [Online]. Available:
 T. Rahman, M. E. H. Chowdhury, A. Khandakar, K. R. Islam, K. F. Islam,
 Z. B. Mahbub, M. A. Kadir, and S. Kashem, ‘‘Transfer learning with deep convolutional neural
network (CNN) for pneumonia detection using chest X-ray,’’ Appl. Sci., vol. 10, no. 9, p. 3233, May
 F. Smarandache. Introduction to Neutrosophic statistics, Sitech & Education Publishing, 2014.
 Smarandache, F, Neutrosophy and Neutrosophic Logic, First International Conference on Neutrosophy ,
Neutrosophic Logic, Set, Probability, and Statistics University of New Mexico, Gallup, NM 87301,
 Smarandache, F. A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set,
Neutrosophic Probability. American Research Press, Rehoboth, NM, 1999.
 Smarandache, F, Neutrosophic set a generalization of the intuitionistic fuzzy sets. Inter. J. Pure Appl.
Math., 24, 287 – 297, 2005.
 Salama, A. A, Smarandache, F, and Kroumov, V, Neutrosophic crisp Sets & Neutrosophic crisp
Topological Spaces. Sets and Systems, 2(1), 25-30, 2014.
 Broumi S. and Smarandache, F., Correlation coefficient of interval neutrosophic set, Appl. Mech.
Mater., 436:511–517, 2013.
 Broumi, S.; Smarandache, F.; Talea, M.; Bakali, A. Operations on Interval Valued Neutrosophic
Graphs; Infinite Study; Modern Science Publisher: New York, NY, USA, 2016.
 Abdel-Basst, M., Mohamed, R., Elhoseny, M., " A model for the effective COVID-19 identification in
uncertainty environment using primary symptoms and CT scans." Health Informatics Journal, 2020.
 Smarandache, F., Khalid, H., "Neutrosophic Precalculus and Neutrosophic Calculus (second enlarged
edition) ", Pons Publishing House / Pons asbl, pp.20-22, 2018.
 Jdid .M, Salama.A.A , Alhabib.R ,Khalid .H, and Alsuleiman .F, Neutrosophic Treatment of the static
model of inventory management with deficit , International Journal of Neutrosophic Science (IJNS),
Volume 18, Issue 1, PP: 20-29, 2022.
 Jdid .M, Alhabib.R and Salama.A.A, Fundamentals of Neutrosophical Simulation for Generating
Random Numbers Associated with Uniform Probability Distribution, Neutrosophic Sets and Systems,
 Jdid .M, Alhabib.R ,Khalid .H, and Salama.A.A, the Neutrosophic Treatment of the static model for the
inventory management with safety reserve , International Journal of Neutrosophic Science (IJNS),
Volume 18, Issue 2, PP: 262-271, 2022.
 Jdid .M, Salama.A.A and Khalid .H, Neutrosophic handling of the simplex direct algorithm to define
the optimal solution in linear programming , International Journal of Neutrosophic Science (IJNS),
Volume 18, Issue 1, PP: 30-41, 2022.
 Jdid .M, and Khalid .H, mysterious Neutrosophic linear models , International Journal of Neutrosophic
Science (IJNS), Volume 18, Issue 2, PP: 243-253, 2022.
 Maissam Jdid, Basel Shahin, Fatima Al Suleiman, Important Neutrosophic Rules for Decision-Making
in the Case of Uncertain Data, International Journal of Neutrosophic Science (IJNS), Volume 18,
Issue3, PP: 166-176, 2022.
 Wang, W., Xu, Y., Gao, R., Lu, R., Han, K., Wu, G., et al.: Detection of SARS-CoV-2 in different
types of clinical specimens. Jama ,2020
 Vadakkenveettil, Bino. (2012). Grey Level Co-Occurrence Matrices: Generalisation and Some New
Features. International Journal of Computer Science, Engineering and Information Technology. 2. 151 -157. 10.5121/ijcseit.2012.2213.
 Djunaidi, Karina & Agtriadi, Herman & Kuswardani, Dwina & Purwanto, Yudhi. (2021). Gray level
co-occurrence matrix feature extraction and histogram in breast cancer classification with
ultrasonographic imagery. Indonesian Journal of Electrical Engineering and Computer Science. 22. 795.
 Ju, Wen & Cheng, H.D. A novel neutrosophic logic SVM (N-SVM) and its application to image
categorization. New Mathematics and Natural Computation. 09, 2013.
 Turhan, Muhammed & Şengür, Dönüş & Karabatak, Songül & Guo, Yanhui & Smarandache, Florentin.
Neutrosophic Weighted Support Vector Machines for the Determination of School Administrators Who
Attended an Action Learning Course Based on Their Conflict-Handling Styles,2018.
 Evgeniou, Theodoros & Pontil, Massimiliano.Support Vector Machines: Theory and Applications.
2049. 249-257. 10.1007/3-540-44673-7_12. 2001.
 Ghosh, Debdas & Singh, Abhishek & Shukla, Kuldeep Kumar & Manchanda, Kartik. Extended
Karush-Kuhn-Tucker Condition for Constrained Interval Optimization Problems and its Application in
Support Vector Machines. Information Sciences. 504. 10.1016/j.ins.2019.
 Rohith.N.Reddy.COVID-19Detection using SVM Classifier .Reddy2020COVID19DU,2020
|MLA||Mohammed Alshikho,Maissam Jdid,Said Broumi. "Artificial Intelligence and Neutrosophic Machine learning in the Diagnosis and Detection of COVID 19." Prospects for Applied Mathematics and Data Analysis, Vol. 1, No. 2, 2023 ,PP. 17-27.|
|APA||Mohammed Alshikho,Maissam Jdid,Said Broumi. (2023). Artificial Intelligence and Neutrosophic Machine learning in the Diagnosis and Detection of COVID 19. Prospects for Applied Mathematics and Data Analysis, 1 ( 2 ), 17-27.|
|Chicago||Mohammed Alshikho,Maissam Jdid,Said Broumi. "Artificial Intelligence and Neutrosophic Machine learning in the Diagnosis and Detection of COVID 19." Prospects for Applied Mathematics and Data Analysis, 1 no. 2 (2023): 17-27.|
|Harvard||Mohammed Alshikho,Maissam Jdid,Said Broumi. (2023). Artificial Intelligence and Neutrosophic Machine learning in the Diagnosis and Detection of COVID 19. Prospects for Applied Mathematics and Data Analysis, 1 ( 2 ), 17-27.|
|Vancouver||Mohammed Alshikho,Maissam Jdid,Said Broumi. Artificial Intelligence and Neutrosophic Machine learning in the Diagnosis and Detection of COVID 19. Prospects for Applied Mathematics and Data Analysis, (2023); 1 ( 2 ): 17-27.|
|IEEE||Mohammed Alshikho,Maissam Jdid,Said Broumi, Artificial Intelligence and Neutrosophic Machine learning in the Diagnosis and Detection of COVID 19, Prospects for Applied Mathematics and Data Analysis, Vol. 1 , No. 2 , (2023) : 17-27 (Doi : https://doi.org/10.54216/PAMDA.010202)|