Salgın Hastalıklarla Mücadelede Açık Kaynak Kodlu Çözümler

Öz

İnsanlık tarihi boyunca salgın hastalıklar birçok can kaybına neden olmuştur. Bilgi teknolojileri ve Endüstri 4.0 çağında bu hastalıklarla mücadelenin farklı boyutları vardır. Tıbbi yaklaşımlar, kimyevi çözümler, laboratuvar çalışmaları elbette bu işin en önemli boyutu ve olmazsa olmazıdır. Bunun yanında istatistik, matematik ve veri bilimi ile elde edilecek analizler, fikirler ve öngörüler, salgın hastalıklar ile mücadelede önemli bir rol oynamaktadır. Bu alanda açık kaynak kodlu yazılımlar ve çözümlerle, salgın hastalıklarla daha iyi bir mücadele sergilenebilmektedir. Farklı algoritmik yaklaşımları içeren açık kaynak kodlu yazılımlar özgür geliştiricilerin desteği ile daha da ileri seviyelere götürülebilmektedir. Ayrıca bu tür yazılımlar ülkelere ve bölgelere göre özgünleştirilebilir. Bu çalışmada, salgın hastalıklarla mücadelede kullanılan istatistiksel ve veri bilimi yöntemlerinin açık kaynak kodlu yazılımlarda nasıl kullanıldığı kategorilere ayrılarak incelenmiştir.

Anahtar Kelimeler

• Corona
• İstatistiksel Analizler
• Sınıflandırma
• Kümeleme
• İlişkilendirme
• Görüntü İşleme

Open Source Based Solutions in Combating Epidemics

Öz

Epidemics have caused numerous casualties throughout human history. There are different dimensions of combating these types of diseases in the age of information technologies and Industry 4.0. Medical approaches, chemical solutions, laboratory studies are of course the most important aspect of this subject. In addition, analyzes, inferences and predictions obtained from statistics, mathematics and data science play crucial roles in combating epidemics. In this field, a better fight against epidemics can be conducted with open source software and their solutions. Open source software containing different algorithmic approaches can be taken to further levels with the support of free developers. In addition, such software can be customized according to countries and regions. In this study, statistical and data science methods in combating epidemic diseases in the open source software field are examined and categorised.

Kaynakça

1. [1] Vaidyanathan, S., & Azar, A. T. (2016). Adaptive control and synchronization of Halvorsen circulant chaotic systems. In Advances in chaos theory and intelligent control (pp. 225-247). Springer, Cham.
2. [2] Abdulmunem, A., A., Metip, M., H., & Abdulhussein, M., F. (2020). CORONA VIRUS DETECTION USING IMAGE SEGMENTATION TECHNIQUES. II. INTERNATIONAL CONFERENCE ON COVID-19 STUDIES. Erişim Tarihi 01.10.2020. URL: https://369485e5-78d9-4695-8ee7-77e624124993.filesusr.com/ugd/ 614b1f_28cb4d090c5143ec80b8d45876d24bd5 .pdf
3. [3] Yıldırım, Ö., & Fındık O. (2020). DERİN ÖĞRENME YÖNTEMLERİ KULLANILARAK TÜRKİYE’DE COVID-19 YAYILIMI TAHMİNİ. II. INTERNATIONAL CONFERENCE ON COVID-19 STUDIES. Alınma Tarihi: 01/10/2020 URL: https://369485e5-78d9-4695-8ee7-77e624124993.filesusr.com/ugd/614b1f_ 0ef2c6d37e44437f8f66ee8cc185d 545.pdf
4. [4] Randhawa, G. S., Soltysiak, M. P., El Roz, H., de Souza, C. P., Hill, K. A., & Kari, L. (2020). Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study. Plos one, 15(4), e0232391.
5. [5] Elaziz, M. A., Hosny, K. M., Salah, A., Darwish, M. M., Lu, S., & Sahlol, A. T. (2020). New machine learning method for image-based diagnosis of COVID-19. Plos one, 15(6), e0235187.
6. [6] Barstugan, M., Ozkaya, U., & Ozturk, S. (2020). Coronavirus (covid-19) classification using ct images by machine learning methods. arXiv preprint arXiv:2003.09424.
7. [7] Tekerek, A. (2011). Veri madenciliği süreçleri ve açık kaynak kodlu veri madenciliği araçları. Akademik Bilişim, 11, 2-4.
8. [8] Nesteruk, I. (2020). Statistics-based predictions of coronavirus epidemic spreading in mainland China.

9. [9] Wikipedia. (2020). Alınma Tarihi: 20/12/2020 https://en.wikipedia.org/wiki/File:Turkey_total_COVID-19_cases_by_NUTS-1_regions.png
10. [10] Sağlık Bakanlığı, (2020), https://covid19.saglik.gov.tr/, Alınma tarihi: 01/10/2020
11. [11] Roser, M., Ritchie, H., Ortiz-Ospina, E., & Hasell, J. (2020). Coronavirus disease (COVID-19)–Statistics and research. Our World in data.
12. [12] Pankowski, R., & Dzięgielewski, J. (2020). COVID-19 crisis and hate speech: Transnational report.
13. [13] Sabic, I., Dodig, L., & Luić, L. (2020, November). COVID-19 IMPACT ON THE USE OF ICT TOOLS IN THE CROATIAN EDUCATION SYSTEM. In Proceedings of ICERI2020 Conference (Vol. 9, p. 10th).
14. [14] Yadav, M., Perumal, M., & Srinivas, M. (2020). Analysis on novel coronavirus (COVID-19) using machine learning methods. Chaos, Solitons & Fractals, 139, 110050.
15. [15] Sariman, G., & Mutaf, E. Covid-19 Süreci̇nde Twitter Mesajlarinin Duygu Anali̇zi̇.
16. [16] Zhang, J., Xie, Y., Li, Y., Shen, C., & Xia, Y. (2020). Covid-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv preprint arXiv:2003.12338.
17. [17] Bulut, F. (2017). A new clinical decision support system with instance based ensemble classifiers. Journal of the Faculty of Engineering and Architecture of Gazi University, 32(1), 65-76.
18. [18] Bütüner, R., & Calp, M. H. (2020). COVID-19 Detection from Lung Tomography Images Using Deep Learning and Machine Learning Methods (No. 4097).
19. [19] Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020). Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Computer Methods and Programs in Biomedicine, 196, 105608.
20. [20] DEMİRCİOĞLU, M., & EŞİYOK, S. COVID–19 SALGINI İLE MÜCADELEDE KÜMELEME ANALİZİ İLE ÜLKELERİN SINIFLANDIRILMASI. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 19(37), 369-389.
21. [21] Singhal, A., Singh, P., Lall, B., & Joshi, S. D. (2020). Modeling and prediction of COVID-19 pandemic using Gaussian mixture model. Chaos, Solitons & Fractals, 138, 110023.
22. [22] Garufi, G., Carbognin, L., Orlandi, A., Tortora, G., & Bria, E. (2020). Smoking habit and hospitalization for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-related pneumonia: The unsolved paradox behind the evidence. European Journal of Internal Medicine.
23. [23] Esper, F., Shapiro, E. D., Weibel, C., Ferguson, D., Landry, M. L., & Kahn, J. S. (2005). Association between a novel human coronavirus and Kawasaki disease. The Journal of infectious diseases, 191(4), 499-502.
24. [24] Bonow, R. O., Fonarow, G. C., O’Gara, P. T., & Yancy, C. W. (2020). Association of coronavirus disease 2019 (COVID-19) with myocardial injury and mortality. JAMA cardiology.
25. [25] Gozes, O., Frid-Adar, M., Sagie, N., Zhang, H., Ji, W., & Greenspan, H. (2020). Coronavirus detection and analysis on chest ct with deep learning. arXiv preprint arXiv:2004.02640.
26. [26] Bhattacharya, S., Maddikunta, P. K. R., Pham, Q. V., Gadekallu, T. R., Chowdhary, C. L., Alazab, M., & Piran, M. J. (2020). Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: A Survey. Sustainable cities and society, 102589.
27. [27] Afshar, P., Heidarian, S., Naderkhani, F., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2020). Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images. arXiv preprint arXiv:2004.02696.
28. [28] Elaziz, M. A., Hosny, K. M., Salah, A., Darwish, M. M., Lu, S., & Sahlol, A. T. (2020). New machine learning method for image-based diagnosis of COVID-19. Plos one, 15(6), e0235187.