Volume 9 , Issue 1 , PP: 54-59, 2020 | Cite this article as | XML | Html | PDF | Full Length Article
A.A. Salama 1 , Mohamed Fazaa 2 , Mohamed Yahya 3 , M. Kazim 4
Doi: https://doi.org/10.54216/IJNS.090105
The idea for this paper is based on the use of a computer-connected microscope associated with Deep Learning, using Convolutional Neural Network (CNN). CNN is a mathematical type of Deep Learning used to recognize and diagnose images. After that, we photograph blood samples, as well as samples, were taken from the mouth and nose, as well as it is possible to photograph the throat from the inside of a large number of injured and uninfected people as well as suspected of infection and provide a large number of references for this program for each type of those different samples. It is possible to perform this process in few minutes, save time and money, make analyzes for the largest possible number of people, and provide results in an accurate and documented manner, which is through the Neutrosophic time series. The basis and analysis of dealing with all data, whether specific or not, that can be taken by time series values, then we present the linear model for the neutrosophic time series, and we test the significance of its coefficient based on patients distribution. Finally, from the above, we can provide a patient neutrosophic time series according to the linear model through which we can accurately predict the program will give degrees of verification and degrees of the uncertainty of the data.
COVID-19, Corona Virus, Neutrosophic Systems, Neutrosophic Domain, Deep Learning, Convolutional Neural Network
[1] Salama, A. A., Smarandache, F. .Neutrosophic Crisp Set Theory, Educational. Education Publishing 1313 Chesapeake, Avenue, Columbus, Ohio 43212, 2015.
[2] Smarandache, Florentin., Neutrosophy and Neutrosophic Logic , First International Conference on Neutrosophy , NeutrosophicLogic , Set, Probability, and Statistics University of New Mexico, Gallup, NM 87301, USA, 2002.
[3] Smarandache,F. A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability, American Research Press, Rehoboth, NM, 1999.
[4] Salama, A. A., Haitham A. El-Ghareeb, Ayman M. Manie, Florentin Smarandache. Introduction to Develop Some Software Programs for Dealing with Neutrosophic Sets, Neutrosophic Sets and Systems, vol. 3, pp. 51-52, 2014.
[5] Salama, A. A., Abdelfattah, M., El-Ghareeb, H. A., & Manie, A. M. Design and implementation of neutrosophic data operations using object oriented programming. International Journal of Computer Application, 4(10), 2014.
[6] Elwahsh, H., Gamal, M., Salama, A. A., & El-Henawy, I. M. Modeling neutrosophic data by self-organizing feature map: MANETs data case study. Procedia computer science, 121, 152-159, 2017.
[7] Salama, A. A., Eisa, M., ElGhawalby, H., & Fawzy, A. E. A New Approach in Content-Based Image Retrieval Neutrosophic Domain. In Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets (pp. 361-369). Springer, Cham, 2019.
[8] Alhabib, R., & Salama, A. A. The Neutrosophic Time Series-Study Its Models (Linear-Logarithmic) and test the Coefficients Significance of Its linear model. Neutrosophic Sets and Systems, 33, pp105-115, 2020.
[9] Elwahsh, H., Gamal, M., Salama, A. A., & El-Henawy, I. M. A novel approach for classifying Manets attacks with a neutrosophic intelligent system based on genetic algorithm. Security and Communication Networks, 2018.
[10] Coronavirus disease (COVID-19) pandemic, https://www.who.int/emergencies/diseases/novel-coronavirus-2019
[11] Ibrahim Yasser, Abeer Twakol, A. A. Abd El-Khalek, Ahmed Samrah, A. A. Salama, 5COVID-X: Novel Health-Fog Framework Based on Neutrosophic Classifier for Confrontation Covid-19, Neutrosophic Sets and Systems, 35 (Accpeted )
