The week's pick
Reseach Article
An Artificial Intelligence System for Classification of COVID-19 Suspicious Person using Support Vector Machine (SVM) Classifier
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 37 |
| Year of Publication: 2020 |
| Authors: Nitin M. Shivale, Gauri Virkar, Tejas L. Bhosale |
10.5120/ijca2020920470
|
Nitin M. Shivale, Gauri Virkar, Tejas L. Bhosale . An Artificial Intelligence System for Classification of COVID-19 Suspicious Person using Support Vector Machine (SVM) Classifier. International Journal of Computer Applications. 176, 37 ( Jul 2020), 16-19. DOI=10.5120/ijca2020920470
Abstract
The outbreak of corona virus disease 2019 (COVID-19), caused by severe acute respiratory syndrome (SARS) corona virus 2 (SARS-CoV-2), has till date(April 2020) killed over 825 people, 5939 recovered and infected over 26,496 in India and elsewhere in the world, resulting in destruction for humans. However, COVID-19 has lower severity and mortality than SARS but is much more transmissive and affects more elderly individuals, youth and more men than women. In response to the rapidly increasing number of infected count of the emerging disease in urban area and people in urban areas have no jobs due to lockdown so they start migration from urban to rural area which may create lots of problem in rural area even though the lower density of rural areas may help keep transmission rates of the disease down. This research claims to provide better accuracy since the data received is verified by the reliable source. Further, this paper attempts to provide an Artificial Intelligence System for classification of COVID-19 suspicious person using different machine algorithms to break the chain of novel corona virus outbreak. The rural areas can be kept secured from getting infected once the chain of transmission is broken. Although many questions still require answers, this paper helps in the identifying the suspicious person and eradication of the threatening disease.
References
- En.wikipedia.org. 2020. Village. [online] Available at: https://en.wikipedia.org/wiki/Village
- Wen, Z.; Li, B.; Kotagiri, R.; Chen, J.; Chen, Y.; and Zhang, R. 2016. Improving efficiency of SVM k-fold cross-validation by alpha seeding. Technical Report arXiv:1611.07659
- Z. Nematzadeh, R. Ibrahim and A. Selamat, "Comparative studies on breast cancer classifications with k-fold cross validations using machine learning techniques," 2015 10th Asian Control Conference (ASCC), Kota Kinabalu, 2015, pp. 1-6, doi: 10.1109/ASCC.2015.7244654.
- Chang, C.-C., and Lin, C.-J. 2011. LIBSVM: a library for support vector machines. TIST 2(3):27.
- Chu, B.-Y.; Ho, C.-H.; Tsai, C.-H.; Lin, C.-Y.; and Lin, C.-J. 2015. Warm start for parameter selection of linear classifiers. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 149-158. ACM
- Schott, J. R. 2005. Matrix analysis for statistics.
- Wen, Z.; Li, B.; Kotagiri, R.; Chen, J.; Chen, Y.; and Zhang, R. 2016. Improving efficiency of SVM k-fold cross-validation by alpha seeding. Technical Report arXiv:1611.07659 [cs.LG], arXiv
- International Journal of Modern Trends in Engineering & Research, 2017. Comparative Analysis of Linear Regression, Multilayer Perceptron and Support Vector Machines for its utilization in Stock Price Prediction. 4(6), pp.7-12.
Index Terms
Keywords