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COVID-19'un Ülke Bazlı Analizi ve Veri Görselleştirmesi

Yıl 2024, Cilt: 6 Sayı: 3

Öz

İnsan yaşamının her alanını etkileyen COVID-19 salgını, milyonlarca ölümle sonuçlanmıştır. Günümüzde adı tam olarak konulmamakla ve ölümcül olmamakla beraber COVID-19 günümüzde influenza olarak seyretmektedir. COVID-19 ve benzeri hastalıkların önlenmesinde hastalıklara ait verilerin görselleştirilmesi önemlidir. Özellikle hükümetlerin, iş yerlerine ve kurumlara güvenilir, anlaşılır ve kolay aktarılabilen bilgiler sunması, ilgili hastalıklarla mücadelede noktasında farkındalık sağlamaktadır. Bu çalışmanın amacı, veri görselleştirme yöntemi ile farklı kıta ve ülkelerdeki COVID-19’un etkisini karşılaştırmaktır. Doğrulanmış COVID-19 vakaları hakkındaki bilgilerin farkındalığı artıracağı düşünülmektedir. Bu salgını görselleştirmek için açık kaynaklı bir yazılım olan Elasticsearch kullanılmıştır. Veriler ABD, Çin, Türkiye gibi farklı ülkelerden elde edilmiştir. Veri başlıkları, doğrulanmış COVID-19 vaka sayısı, toplam ölüm sayısı ve kurtarılan toplam vaka sayısı olarak belirlenmiştir. COVID-19’un etkisi hakkında kapsamlı bir anlayış ortaya konulmaya çalışılmıştır. Ayrıca bu çalışma ile gerçek hayattaki birçok farklı uygulama ve hizmetten alınan büyük verilerin de görselleştirilmesine ışık tutacağı değerlendirilmiştir.

Kaynakça

  • 3M Visual Systems Division. Polishing your presentation, Austin, TX: 3M Visual Systems Division, 2020, https://3rd-force.org/pubs/meetingguide_pres.pdf
  • C. K. Leung, Y. Chen, C.S. Hoi, S. Shang, Y. Wen, A. Cuzzocrea, Big data visualization and visual analytics of COVID-19 data, 24th International Conference Information Visualisation (IV), IEEE, Australia, 2020, 415-420. doi:10.1109/IV51561.2020.00073
  • M. Hesami, M. Alizadeh, A.M.P. Jones, D. Torkamaneh, D, Machine learning: Its challenges and opportunities in plant system biology, Applied Microbiology and Biotechnology. 106(9-10) (2022), 3507-3530. doi:10.1007/s00253-022-11963-6
  • J. Yan, X. Wang, X, Unsupervised and semi‐supervised learning: the next frontier in machine learning for plant systems biology, The Plant Journal. 111(6) (2022), 1527-1538. doi:10.1111/tpj.15905
  • M. Naeem, T. Jamal, J. Diaz-Martinez, S.A. Butt, N. Montesano, M.I. Tariq, E. De-La-Hoz-Valdiris, Trends and future perspective challenges in big data, In Advances in Intelligent Data Analysis and Applications: Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, Springer, Arad, Romania, 2019, 309-325.
  • Y. Tang, J.J. Xiong, Y. Luo, Y.C. Zhang, How do the global stock markets Influence one another? Evidence from finance big data and granger causality directed network, International Journal of Electronic Commerce. 23(1) (2019), 85-109. doi:10.1080/10864415.2018.1512283
  • M. Pejić Bach, Ž. Krstić, S. Seljan, L. Turulja, Text mining for big data analysis in financial sector: A literature review, Sustainability. 11(5) (2019), 1277. doi:10.3390/su11051277
  • H. Sun, M.R. Rabbani, M. S., Sial, S. Yu, J.A. Filipe, J. Cherian, Identifying big data’s opportunities, challenges, and implications in finance, Mathematics. 8(10) (2020), 1738. doi:10.3390/math8101738
  • J.L. Jimenez-Marquez, I. Gonzalez-Carrasco, J.L. Lopez-Cuadrado, B. Ruiz-Mezcua, Towards a big data framework for analyzing social media content. International Journal of Information Management. 44 (2019), 1-12. doi:10.1016/j.ijinfomgt.2018.09.003
  • X. Kong, Y. Shi, S. Yu, J. Liu, F. Xia, Academic social networks: Modeling, analysis, mining and applications, Journal of Network and Computer Applications. 132 (2019), 86-103. doi:10.1016/j.jnca.2019.01.029
  • L. Nemes, A. Kiss, Social media sentiment analysis based on COVID-19, Journal of Information and Telecommunication. 5(1) (2021), 1-15. doi:10.1080/24751839.2020.1790793
  • I. Ahmed, M. Ahmad, G. Jeon, F. Piccialli, A framework for pandemic prediction using big data analytics, Big Data Research. 25 (2021), 100190. doi:10.1016/j.bdr.2021.100190
  • M. Elsotouhy, G. Jain, A. Shrivastava, Disaster Management during pandemic: A big data-centric approach, International Journal of Innovation and Technology Management. 18(04) (2021), 2140003. doi:10.1016/j.bdr.2021.100190
  • World Health Organization, Number of COVID-19 cases reported to WHO, 2020, covid19.who.int, 2020
  • S. Kunt, COVID-19 Pandemisinin turizme etkisi konusunda yapılan çalışmaların carrot² analizi ile değerlendirilmesi, Güncel Turizm Araştırma Dergisi. 5(1) (2021), 30-47. doi:10.32572/guntad.794537
  • M. Bektaş, A. Yavuz, F. Bulut, Salgın hastalıklarla mücadelede açık kaynak kodlu çözümler, İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 3(1), (2021), 99-105.
  • I. Ahmed, M. Ahmad, G. Jeon, F. Piccialli, A framework for pandemic prediction using big data analytics, Big Data Research. 25 (2021), 100190. doi:10.1016/j.bdr.2021.100190
  • A. Kurşun, Büyük veri ve sağlık hizmetlerinde büyük veri işleme araçları, Hacettepe Sağlık İdaresi Dergisi. 24(4), 921-940.
  • A.A. Ardakani, A. R. Kanafi , U. R. Acharya, N. Khadem , A. Mohammadi, Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks, Computers in Biology and Medicine. 121 (2020), 103795:1-103795:9. doi:10.1016/j.compbiomed.2020.103795
  • M.B. Jamshidi, et al., Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment, IEEE Access. 8 (2020), 109581-109595. doi:10.1109/ACCESS.2020.3001973
  • B. Robson, COVID-19 coronavirus spike protein analysis for synthetic vaccines, a peptidomimetic antagonist, and therapeutic drugs, and analysis of a proposed achilles' heel conserved region to minimize probability of escape mutations and drug resistance, Computers in Biology and Medicine. 121 (2020), 103749. doi:10.1016/j.compbiomed.2020.103749
  • Z. A. A. Alyasseri, M.A. Al‐Betar, I. A., Doush, M. A., Awadallah, A. K., Abasi, S. N. Makhadmeh, R.A. Zitar, Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches, Expert Systems. 39(3) (2022), e12759. doi:10.1111/exsy.12759
  • K. Moulaei, M. Shanbehzadeh, Z. Mohammadi-Taghiabad, H. Kazemi-Arpanahi, Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Medical Informatics and Decision Making. 22(1) (2022), 1-12. doi:10.1186/s12911-021-01742-0
  • D. Mhlanga, The role of artificial intelligence and machine learning amid the COVID-19 pandemic: What lessons are we learning on 4IR and the sustainable development goals, International Journal of Environmental Research and Public Health. 19(3) (2022), 1879. doi:10.3390/ijerph19031879
  • C. Comito, C. Pizzuti, Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review, Artificial Intelligence in Medicine. 128 (2022), 102286. doi:10.1016/j.artmed.2022.10228
  • P.G. Asteris, E. Gavriilaki, T. Touloumenidou, E.E. Koravou, M. Koutra, P.G. Papayanni, A. Anagnostopoulos, Genetic prediction of icu hospitalization and mortality in COVID-19 patients using artificial neural networks, Journal of Cellular and Molecular Medicine. 26(5) (2022), 1445-1455. doi:10.1111/jcmm.17098
  • S.I. O'Donoghue, Grand challenges in bioinformatics data visualization, Frontiers in Bioinformatics. 17 (2021), 669186. doi:10.3389/fbinf.2021.669186
  • S.I. O'Donoghue, B.F. Baldi, S.J. Clark, A.E. Darling, J.M. Hogan, S. Kaur, L. Maier-Hein, D. J. McCarthy, W.J. Moore, E. Stenau, J.R. Swedlow, J. Vuong, J.B. Procter, Visualization of biomedical data, Annual Review of Biomedical Data Science. 1 (2018), 275-304. doi:10.1146/annurev-biodatasci-080917-013424
  • B. Wong, Visualizing biological data, Nature Methods. 9: 1131. 2012. doi:10.1038/nmeth.2258
  • O. ByMatthew Ward, G. Grinstein, D. Keim, Interactive Data Visualization, Foundations, Techniques, and Applications, Second Edition, New York, A K Peters/CRC Press 2015. doi:10.1201/b18379
  • C. Ware, Information Visualization: Perception for Design. Third Edition., Morgan Kaufmann Publishers Inc., San Francisco, CA, United States, 2012.
  • F. Zuo, J. Wang, J. Gao, K. Ozbay, X.J. Ban, Y. Shen, H. Yang, S. Iyer, An interactive data visualization and analytics tool to evaluate mobility and sociability trends during COVID-19, arXiv preprint arXiv:2006.14882, 2020.
  • P.L. Bras, A. Gharavi, D.A. Robb, A.F. Vidal, S. Padilla, M.J. Chantler, Visualising COVID-19 research, CoRR abs/2005.06380, 2020.
  • J. Tu, M. Verhagen, B. Cochran, J. Pustejovsky, Exploration and discovery of the COVID-19 literature through semantic visualization, CoRR abs/2007.01800, 2020.
  • Worldometer, COVID-19 Coronavirus Pandemic, 2020. https://www.worldometers.info/coronavirus/ (13 April 2020)
  • F. Wolinski, Visualization of diseases at risk in the COVID-19 literature, CoRR abs/2005.00848, 2020.
  • F. Khanam, I. Nowrin, M. Mondal, Data visualization and analyzation of COVID-19, Journal of Scientific Research and Reports. 42-52 (2020). doi: 10.9734/JSRR/2020/v26i330234.
  • E. Dong, H. Du, L. Gardner, An interactive web-based dashboard to track COVID-19 in real time, The Lancet Infectious Diseases. 20(5) (2020), 533-534. doi:10.1016/S1473-3099(20)30120-1
  • C.K. Leung, Y. Chen, C.S. Hoi, S. Shang, Y. Wen, A. Cuzzocrea, Big data visualization and visual analytics of COVID-19 data. In 2020 24th International Conference Information Visualisation (IV) (pp. 415-420). IEEE.
  • I. Berry, J.P.R. Soucy, A. Tuite, D. Fisman, Open access epidemiologic data and an interactive dashboard to monitor the COVID-19 outbreak in Canada, Cmaj. 192(15) (2020)., E420-E420. doi:10.1503/cmaj.75262
  • F.B. Hamzah, C. Lau, H. Nazri, D.V. Ligot, G. Lee, C.I. Tan, M.H. Chung, Corona Tracker: worldwide COVID-19 outbreak data analysis and prediction, Bull World Health Organ. 1(32) (2020), 1-32. doi:10.2471/BLT.20.251561
  • F. Bahadır, F.S. Balık, H.S. Yalçınkaya, The impact of COVID-19 on the financial structure of the construction industry in Turkey. Necmettin Erbakan University Journal of Science and Engineering. 5(2) (2023), 134-149. doi:10.47112/neufmbd.2023.17
  • Y. Uzun, H. Ergün, E Şeker, Augmented reality approach for stories. Necmettin Erbakan University Journal of Science and Engineering. 4(2) (2022), 1-7. doi:10.47112/neufmbd.2022.1
  • A. Pektaş, O. İnan. Application of tree seed algorithm on clustering problems, Necmettin Erbakan University Journal of Science and Engineering. 4(1) (2022), 1-10.

Country-based Analysis and Data Visualization of COVID-19

Yıl 2024, Cilt: 6 Sayı: 3

Öz

The COVID-19 epidemic, which affects every aspect of human life, has resulted in millions of deaths. Today, COVID-19 is known as influenza, although its name is not fully known, and it is not fatal. Visualization of disease data is important in preventing COVID-19 and similar diseases. Governments provide reliable, understandable and easily transferable information to workplaces and institutions provides awareness in the fight against relevant diseases. The aim of this study is to compare the impact of COVID-19 in different continents and countries with the data visualization method. It is thought that information about confirmed COVID-19 cases will increase awareness. Elasticsearch, an open-source software, was used to visualize this outbreak. Data were obtained from different countries such as the USA, China and Türkiye. Data headlines are the number of confirmed COVID-19 cases, the total number of deaths, and the total number of cases recovered. An attempt has been made to provide a comprehensive understanding of the impact of COVID-19. In addition, it was evaluated that this study would shed light on the visualization of big data received from many different applications and services in real life.

Kaynakça

  • 3M Visual Systems Division. Polishing your presentation, Austin, TX: 3M Visual Systems Division, 2020, https://3rd-force.org/pubs/meetingguide_pres.pdf
  • C. K. Leung, Y. Chen, C.S. Hoi, S. Shang, Y. Wen, A. Cuzzocrea, Big data visualization and visual analytics of COVID-19 data, 24th International Conference Information Visualisation (IV), IEEE, Australia, 2020, 415-420. doi:10.1109/IV51561.2020.00073
  • M. Hesami, M. Alizadeh, A.M.P. Jones, D. Torkamaneh, D, Machine learning: Its challenges and opportunities in plant system biology, Applied Microbiology and Biotechnology. 106(9-10) (2022), 3507-3530. doi:10.1007/s00253-022-11963-6
  • J. Yan, X. Wang, X, Unsupervised and semi‐supervised learning: the next frontier in machine learning for plant systems biology, The Plant Journal. 111(6) (2022), 1527-1538. doi:10.1111/tpj.15905
  • M. Naeem, T. Jamal, J. Diaz-Martinez, S.A. Butt, N. Montesano, M.I. Tariq, E. De-La-Hoz-Valdiris, Trends and future perspective challenges in big data, In Advances in Intelligent Data Analysis and Applications: Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, Springer, Arad, Romania, 2019, 309-325.
  • Y. Tang, J.J. Xiong, Y. Luo, Y.C. Zhang, How do the global stock markets Influence one another? Evidence from finance big data and granger causality directed network, International Journal of Electronic Commerce. 23(1) (2019), 85-109. doi:10.1080/10864415.2018.1512283
  • M. Pejić Bach, Ž. Krstić, S. Seljan, L. Turulja, Text mining for big data analysis in financial sector: A literature review, Sustainability. 11(5) (2019), 1277. doi:10.3390/su11051277
  • H. Sun, M.R. Rabbani, M. S., Sial, S. Yu, J.A. Filipe, J. Cherian, Identifying big data’s opportunities, challenges, and implications in finance, Mathematics. 8(10) (2020), 1738. doi:10.3390/math8101738
  • J.L. Jimenez-Marquez, I. Gonzalez-Carrasco, J.L. Lopez-Cuadrado, B. Ruiz-Mezcua, Towards a big data framework for analyzing social media content. International Journal of Information Management. 44 (2019), 1-12. doi:10.1016/j.ijinfomgt.2018.09.003
  • X. Kong, Y. Shi, S. Yu, J. Liu, F. Xia, Academic social networks: Modeling, analysis, mining and applications, Journal of Network and Computer Applications. 132 (2019), 86-103. doi:10.1016/j.jnca.2019.01.029
  • L. Nemes, A. Kiss, Social media sentiment analysis based on COVID-19, Journal of Information and Telecommunication. 5(1) (2021), 1-15. doi:10.1080/24751839.2020.1790793
  • I. Ahmed, M. Ahmad, G. Jeon, F. Piccialli, A framework for pandemic prediction using big data analytics, Big Data Research. 25 (2021), 100190. doi:10.1016/j.bdr.2021.100190
  • M. Elsotouhy, G. Jain, A. Shrivastava, Disaster Management during pandemic: A big data-centric approach, International Journal of Innovation and Technology Management. 18(04) (2021), 2140003. doi:10.1016/j.bdr.2021.100190
  • World Health Organization, Number of COVID-19 cases reported to WHO, 2020, covid19.who.int, 2020
  • S. Kunt, COVID-19 Pandemisinin turizme etkisi konusunda yapılan çalışmaların carrot² analizi ile değerlendirilmesi, Güncel Turizm Araştırma Dergisi. 5(1) (2021), 30-47. doi:10.32572/guntad.794537
  • M. Bektaş, A. Yavuz, F. Bulut, Salgın hastalıklarla mücadelede açık kaynak kodlu çözümler, İstanbul Sabahattin Zaim Üniversitesi Fen Bilimleri Enstitüsü Dergisi. 3(1), (2021), 99-105.
  • I. Ahmed, M. Ahmad, G. Jeon, F. Piccialli, A framework for pandemic prediction using big data analytics, Big Data Research. 25 (2021), 100190. doi:10.1016/j.bdr.2021.100190
  • A. Kurşun, Büyük veri ve sağlık hizmetlerinde büyük veri işleme araçları, Hacettepe Sağlık İdaresi Dergisi. 24(4), 921-940.
  • A.A. Ardakani, A. R. Kanafi , U. R. Acharya, N. Khadem , A. Mohammadi, Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks, Computers in Biology and Medicine. 121 (2020), 103795:1-103795:9. doi:10.1016/j.compbiomed.2020.103795
  • M.B. Jamshidi, et al., Artificial intelligence and COVID-19: deep learning approaches for diagnosis and treatment, IEEE Access. 8 (2020), 109581-109595. doi:10.1109/ACCESS.2020.3001973
  • B. Robson, COVID-19 coronavirus spike protein analysis for synthetic vaccines, a peptidomimetic antagonist, and therapeutic drugs, and analysis of a proposed achilles' heel conserved region to minimize probability of escape mutations and drug resistance, Computers in Biology and Medicine. 121 (2020), 103749. doi:10.1016/j.compbiomed.2020.103749
  • Z. A. A. Alyasseri, M.A. Al‐Betar, I. A., Doush, M. A., Awadallah, A. K., Abasi, S. N. Makhadmeh, R.A. Zitar, Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches, Expert Systems. 39(3) (2022), e12759. doi:10.1111/exsy.12759
  • K. Moulaei, M. Shanbehzadeh, Z. Mohammadi-Taghiabad, H. Kazemi-Arpanahi, Comparing machine learning algorithms for predicting COVID-19 mortality. BMC Medical Informatics and Decision Making. 22(1) (2022), 1-12. doi:10.1186/s12911-021-01742-0
  • D. Mhlanga, The role of artificial intelligence and machine learning amid the COVID-19 pandemic: What lessons are we learning on 4IR and the sustainable development goals, International Journal of Environmental Research and Public Health. 19(3) (2022), 1879. doi:10.3390/ijerph19031879
  • C. Comito, C. Pizzuti, Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review, Artificial Intelligence in Medicine. 128 (2022), 102286. doi:10.1016/j.artmed.2022.10228
  • P.G. Asteris, E. Gavriilaki, T. Touloumenidou, E.E. Koravou, M. Koutra, P.G. Papayanni, A. Anagnostopoulos, Genetic prediction of icu hospitalization and mortality in COVID-19 patients using artificial neural networks, Journal of Cellular and Molecular Medicine. 26(5) (2022), 1445-1455. doi:10.1111/jcmm.17098
  • S.I. O'Donoghue, Grand challenges in bioinformatics data visualization, Frontiers in Bioinformatics. 17 (2021), 669186. doi:10.3389/fbinf.2021.669186
  • S.I. O'Donoghue, B.F. Baldi, S.J. Clark, A.E. Darling, J.M. Hogan, S. Kaur, L. Maier-Hein, D. J. McCarthy, W.J. Moore, E. Stenau, J.R. Swedlow, J. Vuong, J.B. Procter, Visualization of biomedical data, Annual Review of Biomedical Data Science. 1 (2018), 275-304. doi:10.1146/annurev-biodatasci-080917-013424
  • B. Wong, Visualizing biological data, Nature Methods. 9: 1131. 2012. doi:10.1038/nmeth.2258
  • O. ByMatthew Ward, G. Grinstein, D. Keim, Interactive Data Visualization, Foundations, Techniques, and Applications, Second Edition, New York, A K Peters/CRC Press 2015. doi:10.1201/b18379
  • C. Ware, Information Visualization: Perception for Design. Third Edition., Morgan Kaufmann Publishers Inc., San Francisco, CA, United States, 2012.
  • F. Zuo, J. Wang, J. Gao, K. Ozbay, X.J. Ban, Y. Shen, H. Yang, S. Iyer, An interactive data visualization and analytics tool to evaluate mobility and sociability trends during COVID-19, arXiv preprint arXiv:2006.14882, 2020.
  • P.L. Bras, A. Gharavi, D.A. Robb, A.F. Vidal, S. Padilla, M.J. Chantler, Visualising COVID-19 research, CoRR abs/2005.06380, 2020.
  • J. Tu, M. Verhagen, B. Cochran, J. Pustejovsky, Exploration and discovery of the COVID-19 literature through semantic visualization, CoRR abs/2007.01800, 2020.
  • Worldometer, COVID-19 Coronavirus Pandemic, 2020. https://www.worldometers.info/coronavirus/ (13 April 2020)
  • F. Wolinski, Visualization of diseases at risk in the COVID-19 literature, CoRR abs/2005.00848, 2020.
  • F. Khanam, I. Nowrin, M. Mondal, Data visualization and analyzation of COVID-19, Journal of Scientific Research and Reports. 42-52 (2020). doi: 10.9734/JSRR/2020/v26i330234.
  • E. Dong, H. Du, L. Gardner, An interactive web-based dashboard to track COVID-19 in real time, The Lancet Infectious Diseases. 20(5) (2020), 533-534. doi:10.1016/S1473-3099(20)30120-1
  • C.K. Leung, Y. Chen, C.S. Hoi, S. Shang, Y. Wen, A. Cuzzocrea, Big data visualization and visual analytics of COVID-19 data. In 2020 24th International Conference Information Visualisation (IV) (pp. 415-420). IEEE.
  • I. Berry, J.P.R. Soucy, A. Tuite, D. Fisman, Open access epidemiologic data and an interactive dashboard to monitor the COVID-19 outbreak in Canada, Cmaj. 192(15) (2020)., E420-E420. doi:10.1503/cmaj.75262
  • F.B. Hamzah, C. Lau, H. Nazri, D.V. Ligot, G. Lee, C.I. Tan, M.H. Chung, Corona Tracker: worldwide COVID-19 outbreak data analysis and prediction, Bull World Health Organ. 1(32) (2020), 1-32. doi:10.2471/BLT.20.251561
  • F. Bahadır, F.S. Balık, H.S. Yalçınkaya, The impact of COVID-19 on the financial structure of the construction industry in Turkey. Necmettin Erbakan University Journal of Science and Engineering. 5(2) (2023), 134-149. doi:10.47112/neufmbd.2023.17
  • Y. Uzun, H. Ergün, E Şeker, Augmented reality approach for stories. Necmettin Erbakan University Journal of Science and Engineering. 4(2) (2022), 1-7. doi:10.47112/neufmbd.2022.1
  • A. Pektaş, O. İnan. Application of tree seed algorithm on clustering problems, Necmettin Erbakan University Journal of Science and Engineering. 4(1) (2022), 1-10.
Toplam 44 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgi Modelleme, Yönetim ve Ontolojiler
Bölüm Makaleler
Yazarlar

Abdullah Erdal Tümer 0000-0001-7747-9441

Musab Kasım Doğan 0009-0002-8681-2845

Erken Görünüm Tarihi 18 Aralık 2024
Yayımlanma Tarihi
Gönderilme Tarihi 15 Mart 2024
Kabul Tarihi 7 Temmuz 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 6 Sayı: 3

Kaynak Göster

APA Tümer, A. E., & Doğan, M. K. (2024). COVID-19’un Ülke Bazlı Analizi ve Veri Görselleştirmesi. Necmettin Erbakan Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 6(3).
AMA Tümer AE, Doğan MK. COVID-19’un Ülke Bazlı Analizi ve Veri Görselleştirmesi. NEU Fen Muh Bil Der. Aralık 2024;6(3).
Chicago Tümer, Abdullah Erdal, ve Musab Kasım Doğan. “COVID-19’un Ülke Bazlı Analizi Ve Veri Görselleştirmesi”. Necmettin Erbakan Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi 6, sy. 3 (Aralık 2024).
EndNote Tümer AE, Doğan MK (01 Aralık 2024) COVID-19’un Ülke Bazlı Analizi ve Veri Görselleştirmesi. Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi 6 3
IEEE A. E. Tümer ve M. K. Doğan, “COVID-19’un Ülke Bazlı Analizi ve Veri Görselleştirmesi”, NEU Fen Muh Bil Der, c. 6, sy. 3, 2024.
ISNAD Tümer, Abdullah Erdal - Doğan, Musab Kasım. “COVID-19’un Ülke Bazlı Analizi Ve Veri Görselleştirmesi”. Necmettin Erbakan Üniversitesi Fen ve Mühendislik Bilimleri Dergisi 6/3 (Aralık 2024).
JAMA Tümer AE, Doğan MK. COVID-19’un Ülke Bazlı Analizi ve Veri Görselleştirmesi. NEU Fen Muh Bil Der. 2024;6.
MLA Tümer, Abdullah Erdal ve Musab Kasım Doğan. “COVID-19’un Ülke Bazlı Analizi Ve Veri Görselleştirmesi”. Necmettin Erbakan Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, c. 6, sy. 3, 2024.
Vancouver Tümer AE, Doğan MK. COVID-19’un Ülke Bazlı Analizi ve Veri Görselleştirmesi. NEU Fen Muh Bil Der. 2024;6(3).


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