2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 2 , PP: 433-457, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Zaid Derea 1 , Ammar Kazm 2 , Jasim Mohammed 3 , Oday Ali Hassen 4 * , Esraa Saleh Alomari 5
Doi: https://doi.org/10.54216/FPA.210227
SARS-CoV2 virus has affected the peoples in worldwide with several issues, like health and economy. Moreover, mathematical definition of fractal dimension affords a method for calculating the non-linear dynamic behaviour difficulty revealed through time series of countries. The fuzzy logic model illustrates and manages the characteristic uncertainty of classification issue. In this paper, an effectual SARS-CoV2model is developed using optimized Deep learning model through time series data. The derived features are derived from the input sequential data for disease forecasting. Moreover, over sampling scheme is exploited for data augmentation, which enhances the prediction process. Fuzzy systems and various distance measures are calculated for choosing most significant features. The Deep Recurrent Neural network (DRNN) is applied for performing SARS-CoV2prediction, in which DRNN is trained through designed Fractional Water Poor and Rich Optimization (FrWPRO) method. Meanwhile, the training process of DRNN using hybrid optimization model from scratch proves that, the designed SARS-CoV2prediction method accomplishes better performance compared to other existing approaches with Mean Square Error (MSE), Root MSE (RMSE), and Mean Absolute Percentage Error (MAPE) of 0.1425, and 0.3775, and 0.3467 respectively.
Fuzzy inference , Deep learning , Weighted Moving Average , Time series data , Lee distance
References
[1] H. Abbasimehr and R. Paki, “Prediction of SARS-COV2 confirmed cases combining deep learning methods and Bayesian optimization,” Chaos, Solitons Fractals, vol. 142, 2021, Art. no. 110511.
[2] Y.-S. Niu, W. Ding, J. Hu, W. Xu, and S. Canu, “Spatio-temporal neural network for fitting and forecasting COVID-19,” arXiv Prepr, arXiv: 2103.11860, 2021.
[3] R. G. Babukarthik, V. A. K. Adiga, G. Sambasivam, D. Chandramohan, and J. Amudhavel, “Prediction of SARS-COV2 using genetic deep learning convolutional neural network (GDCNN),” IEEE Access, vol. 8, pp. 177647–177666, 2020.
[4] M. Alazab, A. Awajan, A. Mesleh, A. Abraham, V. Jatana, and S. Alhyari, “SARS-COV2 prediction and detection using deep learning,” Int. J. Comput. Inf. Syst. Ind. Manag. Appl., vol. 12, pp. 168–181, 2020.
[5] F. A. Chyon, M. N. H. Suman, M. R. I. Fahim, and M. S. Ahmmed, “Time series analysis and predicting SARS-COV2 affected patients by ARIMA model using machine learning,” J. Virol. Methods, vol. 301, 2022, Art. no. 114433.
[6] S. Namasudra, S. Dhamodharavadhani, and R. Rathipriya, “Nonlinear neural network based forecasting model for predicting SARS-COV2 cases,” Neural Process. Lett, pp. 1–21, Apr. 2021.
[7] N. Kumar and S. Susan, “Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19,” Appl. Soft Comput., vol. 110, 2021, Art. no. 107611.
[8] O. Castillo and P. Melin, “A novel method for a SARS-CoV2 classification of countries based on an intelligent fuzzy fractal approach,” Healthcare, vol. 9, no. 2, 2021, Art. no. 196.
[9] İ. Kırbaş, A. Sözen, A. D. Tuncer, and F. Ş. Kazancıoğlu, “Comparative analysis and forecasting of SARS-COV2 cases in various European countries with ARIMA, NARNN and LSTM approaches,” Chaos, Solitons Fractals, vol. 138, 2020, Art. no. 110015.
[10] O. Torrealba-Rodriguez, R. A. Conde-Gutiérrez, and A. L. Hernández-Javier, “Modeling and prediction of SARS-COV2 in Mexico applying mathematical and computational models,” Chaos, Solitons Fractals, vol. 138, 2020, Art. no. 109946.
[11] F. Demir, “DeepCoroNet: A deep LSTM approach for automated detection of SARS-COV2 cases from chest X-ray images,” Appl. Soft Comput., vol. 103, 2021, Art. no. 107160.
[12] J. Cao, X. Xu, Y. Wang, and H. Zhu, “Fuzzy inference system with interpretable fuzzy rules optimized by firefly algorithm for COVID-19 time series prediction,” Inf. Sci., vol. 660, pp. 119–135, 2024.
[13] R. Sujath, J. M. Chatterjee, and A. E. Hassanien, “A machine learning forecasting model for SARS-COV2 pandemic in India,” Stoch. Environ. Res. Risk Assess, vol. 34, no. 7, pp. 959–972, Jul. 2020.
[14] Ali et al., “DeepBalance: A deep reinforcement learning framework for dynamic load balancing in software-defined networks,” J. Intell. Syst. Internet Things, vol. 17, no. 1, 2025.
[15] M. John and H. Shaiba, “Main factors influencing recovery in MERS Co-V patients using machine learning,” J. Infect. Public Health, vol. 12, no. 5, pp. 700–704, Sep. 2019.
[16] S. Pradeepa et al., “DRFS: Detecting risk factor of stroke disease from social media using machine learning techniques,” Neural Process. Lett, pp. 1–19, Jun. 2020.
[17] O. Castillo and P. Melin, “A new method for fuzzy estimation of the fractal dimension and its applications to time series analysis and pattern recognition,” in Proc. 19th Int. Conf. North Am. Fuzzy Inf. Process. Soc. (NAFIPS), 2000, pp. 451–455.
[18] Utku, M. H. Sahin, and H. Ince, “Deep learning based hybrid prediction model for COVID-19 cross-country spread,” Expert Syst. Appl., vol. 228, 2023, Art. no. 120377.
[19] S. R. Mohamedsaeed, A. K. Fakhrabadi, and M. S. Helfroush, “A novel deep learning approach for predicting COVID-19 severity using chest X-ray images,” Sci. Rep., vol. 12, no. 1, pp. 1–12, 2022.
[20] H. Adhab, E. M. Kalik, and A. K. Al-Ani, “Designing a smart e-government application using a proposed hybrid architecture model dependent on edge and cloud computing,” Electron. Gov., Int. J., vol. 18, no. 3, pp. 340–353, 2022.
[21] O. Faust et al., “Deep learning for healthcare applications based on physiological signals: A review,” Comput. Methods Programs Biomed, vol. 161, pp. 1–13, 2018.
[22] J. T. C. Ming et al., “Lung disease classification using reticular pattern scoring and five class features with greedy stepwise based on GLCM,” in Proc. IEEE Region 10 Conf. (TENCON), 2017, pp. 182–186.
[23] T. Tuncer, S. Dogan, and F. Ozyurt, “An automated residual exemplar local binary pattern and iterative ReliefF based SARS-COV2 detection method using chest X-ray image,” Chemometr. Intell. Lab. Syst., vol. 203, 2020, Art. no. 104054.
[24] “Novel coronavirus (COVID-19) cases dataset,” Hum. Data Exch., 2021. [Online]. Available: https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases. Accessed: Oct. 2021.
[25] K. Fakhrabadi, S. Hashemi, A. R. K. Khalilabad, and M. S. Helfroush, “A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity,” Sci. Rep., vol. 15, no. 1, 2025, Art. no. 14032.
[26] J. Cao, Y. Wang, and H. Zhu, “Enhanced prediction of COVID-19 cases using hybrid machine learning models,” Inf. Sci., vol. 660, pp. 119–135, 2024.
[27] H. Eskandar, A. Sadollah, A. Bahreininejad, and M. Hamdi, “Water cycle algorithm–A novel metaheuristic optimization method for solving constrained engineering optimization problems,” Comput. Struct, vol. 110, pp. 151–166, 2012.
[28] S. H. S. Moosavi and V. K. Bardsiri, “Poor and rich optimization algorithm: A new human-based and multi populations algorithm,” Eng. Appl. Artif. Intell, vol. 86, pp. 165–181, 2019.
[29] P. R. Bhaladhare and D. C. Jinwala, “A clustering approach for the 𝑙-diversity model in privacy preserving data mining using fractional calculus-Bacterial,” Adv. Comput. Eng., vol. 2014, 2014, Art. no. 893198.
[30] R. Rajeswari, P. Sudhakar, and V. Sugumaran, “FWLICM-Deep Learning: Fuzzy weighted local information C-means with RMDL for COVID-19 prediction using chest X-ray images,” J. Digit. Imag., vol. 35, pp. 1185–1198, 2022.
[31] D. Ayris et al., “A deep sequential prediction model (DSPM) to model and predict the COVID-19 outbreak,” 2022.
[32] M. Shanbehzadeh et al., “Design of an artificial neural network to predict mortality risk due to COVID-19,” 2022.
[33] L. Xu et al., “Forecasting COVID-19 new cases using deep learning methods (CNN, LSTM, CNN-LSTM),” Comput. Biol. Med., vol. 144, 2022, Art. no. 105372.
[34] D. Baihaqi, N. Yudistira, and E. Santoso, “Prediction of COVID-19 by Its Variants using Multivariate Data-driven Deep Learning Models,” 2023.
