Open Access
Issue
BCAS
Volume 36, 2022
Article Number 2022003
Number of page(s) 10
DOI https://doi.org/10.1051/bcas/2022003
Published online 09 November 2022
  • Adeloye, A.J., Rustum, R. & Kariyama, I.D. (2011). Kohonen self-organizing map estimator for the reference crop evapotranspiration. Water Resources Research, 47(8), 192–198. [CrossRef] [Google Scholar]
  • Alqurneh, A., Mustapha, A. & Sharef, N.M. (2020). A partitioning-based approach for clustering COVID-19 drugs and co-medication for safe use. International Journal of Integrated Engineering, 12(5). doi: 10.30880/ijie.2020.12.05.029. [CrossRef] [Google Scholar]
  • Asadzadeh, A., Pakkhoo, S. & Saeidabad M., et al. (2020). Information technology in emergency management of COVID-19 outbreak. Informatics in Medicine Unlocked, 21, 100475. [CrossRef] [PubMed] [Google Scholar]
  • Booton, R.D., Macgregor, L. & Vass, L., et al. (2021). Estimating the COVID-19 epidemic trajectory and hospital capacity requirements in South West England: A mathematical modelling framework. BMJ Open, 11(1), e041536. [CrossRef] [PubMed] [Google Scholar]
  • Casiraghi, E., Malchiodi, D. & Trucco, G., et al. (2020). Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments. IEEE Access, 8, 196299–196325. [CrossRef] [PubMed] [Google Scholar]
  • Chatterjee, P., Nagi, N. & Agarwal, A., et al. (2020). The 2019 novel coronavirus disease (COVID-19) pandemic: A review of the current evidence. The Indian Journal of Medical Research, 151(2–3). [Google Scholar]
  • Chen, N., Ribeiro, B. & Vieira, A., et al. (2013). Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert Systems with Application, 40(1), 385–393. [CrossRef] [Google Scholar]
  • Chen, N., Chen, L. & Ma, Y., et al. (2018). Regional disaster risk assessment of China based on self-organizing map: Clustering, visualization and ranking. International Journal of Disaster Risk Reduction, 33, 196–206. [Google Scholar]
  • Chen, N., Ma, Y. & Tang, C., et al. (2020). Risk assessment and comparison of regional natural disasters in China using clustering. Intelligent Decision Technologies, 14(3), 349–357. [CrossRef] [Google Scholar]
  • Dewan, P., Ganti, R., & Srivatsa, M. (2017). SOM-TC: Self-organizing map for hierarchical trajectory clustering. IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 1042–1052. doi: 10.1109/ICDCS.2017.244. [Google Scholar]
  • Fantazzini, D. (2020). Short-term forecasting of the COVID-19 pandemic using Google trends data: Evidence from 158 countries. Applied Econometrics, 59, 33–54. [CrossRef] [Google Scholar]
  • Householder, J., Householder, A. & Gomez-Reed, J.P., et al. (2020). Clustering COVID-19 lung scans. arXiv e-prints, arXiv:2009.09899. [Google Scholar]
  • Hu, S., Gao, Y. & Niu, Z., et al. (2020). Weakly supervised deep learning for COVID-19 infection detection and classification from CT images. IEEE Access, 1–1. [CrossRef] [Google Scholar]
  • Huang, D.W., Gentili, R.J. & Katz, G.E., et al. (2016). A limit-cycle self-organizing map architecture for stable arm control. Neural networks: the official journal of the International Neural Network Society, 85(C), 165–181. [Google Scholar]
  • Kohonen, T., Schroeder, M.R. & Huang, T.S. (2001). Self-organizing maps. Springer Berlin Heidelberg. [CrossRef] [Google Scholar]
  • Kraus, S., Clau, T. & Breier, M., et al. (2020). The economics of COVID-19: Initial empirical evidence on how family firms in five European countries cope with the corona crisis. International Journal of Entrepreneurial Behaviour & Research, 26(5), 1067–1092. [CrossRef] [Google Scholar]
  • Li, Z., Fang, H. & Huang, M., et al. (2018). Data-driven bearing fault identification using improved hidden Markov model and self-organizing map. Computers & Industrial Engineering, 116, 37–46. [CrossRef] [Google Scholar]
  • Ling, C. & Delmelle, E.C. (2016). Classifying multidimensional trajectories of neigh-bourhood change: A self-organizing map and k-means approach. Annals of GIS, 22(3), 1–14. [CrossRef] [Google Scholar]
  • Neubauer, T., Hassler, W. & Puffing, R. (2020). Ice shape roughness assessment based on a three-dimensional self-organizing map approach. AIAA Aviation Forum. doi: 10.2514/6.2020-2805. [Google Scholar]
  • Qi, J., Liu, H., Liu, X., et al. (2019). Spatiotemporal evolution analysis of time-series land use change using self-organizing map to examine the zoning and scale effects. Computers, Environment and Urban Systems, 76, 11–23. [CrossRef] [Google Scholar]
  • Qin, L., Sun, Q. & Wang Y., et al. (2020). Prediction of the number of new cases of 2019 novel coronavirus (COVID-19) using a social media search index. Social Science Electronic Publishing, SSRN Electronic Journal. doi: 10.2139/ssrn.3552829. [Google Scholar]
  • Rani, S. & Kumar, M. (2020). Social media video summarization using multi-visual features and Kohnen’s self organizing map. Information Processing & Management, 57(3): 102190.1–102190.17. [CrossRef] [MathSciNet] [Google Scholar]
  • Shaikh, F., Dehmeshki, J. & Bisdas, S., et al. (2021). Artificial intelligence-based clinical decision support systems using advanced medical imaging and radiomics. Current Problems in Diagnostic Radiology, 50(2), 262–267. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • Tamang, S.K., Singh, P.D. & Datta, B. (2020). Forecasting of COVID-19 cases based on prediction using artificial neural network curve fitting technique. Global Journal of Environmental Science and Management, 6(Special Issue (COVID-19)), 53–64. [Google Scholar]
  • Vesanto, J. & Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Transactions on Neural Networks, 11(3), 586–600. [CrossRef] [PubMed] [Google Scholar]
  • Warda, R., Song, W. & Kaif, G. et al. (2020). COVID-19 spread prediction and its correlation with social distancing, available health facilities using GIS mapping data models in Lahore, Pakistan. Technium Social Sciences Journal, 10. doi: 10.47577/tssj.v10i1.1291. [Google Scholar]
  • Woo, S.H., Rios-Diaz, A.J. & Kubey, A.A., et al. (2020). Development and validation of a Web-based severe COVID-19 risk prediction model. Cold Spring Harbor Laboratory Press 2020(4). doi: 10.1101/2020.07.16.20155739. [Google Scholar]
  • Zarikas, V., Poulopoulos, S.G. & Gareiou, Z., et al. (2020). Clustering analysis of countries using the COVID-19 cases dataset. Data in Brief, 31: 105787, https://doi.org/10.1016/j.dib.2020.105787. [CrossRef] [PubMed] [Google Scholar]
  • Zturk, A., Zkaya, U. & Barstuan, M. (2020). Classification of Coronavirus (COVID) from X-ray and CT images using shrunken features. International Journal of Imaging Systems and Technology, 31(1): 5–15. doi: 10.1002/ima.22469. [Google Scholar]

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