2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 1 , PP: 342-359, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Zaid Derea 1 * , Ammar Kazm 2 , Manar Bashar Mortatha 3 , Oday Ali Hassen 4 , Esraa Saleh Alomari 5
Doi: https://doi.org/10.54216/JISIoT.170124
Rapid spread of Corona virus 2019 (COVID-19) is predictable to create high contact on healthcare organization. Early detection of this disease is required to make precise treatment that further helps to increase the survival rate of humans. However, detecting the COVID-19 at beginning stage is one of a major challenge in the world because of rapid disease spread. Various existing methods are developed to detect the disease, but generating accurate result at the beginning stage still poses a complex task in the medical research community. Hence, an effective mechanism is modeled in this research to predict the pandemic at early with the time-series data using proposed Water Poor and Rich optimization-based Deep Recurrent Neural network (WPRO-based Deep RNN). Accordingly, proposed method is highly effective in generating the most appropriate results through deep learning classifier based on the high dimension features. However, the high dimensional data is generated through the data augmentation process by employing oversampling technique. The proposed method is more robust and increases the efficiency of the optimization algorithm by attaining global convergence results based on the fitness measure. Accordingly, the technical features of time series data to improve effectiveness of developed model. However, the proposed WPRO-based Deep RNN produced minimum Root Mean Square Error (RMSE) as well as MSE values of 0.4 and 0.1714 for confirmed cases, and obtained lesser MSE and RMSE values of 0.1887 and 0.433 for the cases of death. Moreover, proposed model achieved minimal RMSE and MSE of 0.447 and 0.1901 for the recovered cases.
Deep learning , Water Cycle Algorithm (WCA) , Epidemic prediction , Time-series , Water Poor and Rich optimization (WPRO)
[1] R. G. Babukarthik, V. A. K. Adiga, G. Sambasivam, D. Chandramohan, and J. Amudhavel, "Prediction of COVID-19 using genetic deep learning convolutional neural network (GDCNN)," IEEE Access, vol. 8, pp. 177647–177666, 2020.
[2] F. Shahid, A. Zameer, and M. Muneeb, "Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM," Chaos, Solitons Fractals, vol. 140, p. 110212, 2020.
[3] H. Abbasimehr and R. Paki, "Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization," Chaos, Solitons Fractals, vol. 142, p. 110511, 2021.
[4] D. Ayris et al., "Deep learning models for early detection and prediction of the spread of Novel Coronavirus (COVID-19)," arXiv preprint, arXiv: 2008.01170, 2020.
[5] M. Alazab, A. Awajan, A. Mesleh, A. Abraham, V. Jatana, and S. Alhyari, "COVID-19 prediction and detection using deep learning," Int. J. Comput. Inf. Syst. Ind. Manag. Appl., vol. 12, pp. 168–181, 2020.
[6] C.-J. Huang, Y. Shen, P.-H. Kuo, and Y.-H. Chen, "Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019," Socioecon. Plann. Sci., p. 100976, 2020.
[7] L. S. De Oliveira, S. B. Gruetzmacher, and J. P. Teixeira, "COVID-19 time series prediction," Proc. Comput. Sci., vol. 181, pp. 973–980, 2021.
[8] Y.-S. Niu, W. Ding, J. Hu, W. Xu, and S. Canu, "Spatio-temporal neural network for fitting and forecasting COVID-19," arXiv preprint, arXiv:2103.11860, 2021.
[9] Zeroual, F. Harrou, A. Dairi, and Y. Sun, "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons Fractals, vol. 140, p. 110121, 2020.
[10] Zheng et al., "Deep learning-based detection for COVID-19 from chest CT using weak label," medRxiv, 2020.
[11] L. Brunese, F. Mercaldo, A. Reginelli, and A. Santone, "Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays," Comput. Methods Programs Biomed, vol. 196, p. 105608, 2020.
[12] S. Minaee, R. Kafieh, M. Sonka, S. Yazdani, and G. J. Soufi, "Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning," Med. Image Anal., vol. 65, p. 101794, 2020.
[13] S. Kumar, S. Mishra, and S. K. Singh, "Deep transfer learning-based COVID-19 prediction using chest X-rays," medRxiv, 2020.
[14] M. A. Achterberg et al., "Comparing the accuracy of several network-based COVID-19 prediction algorithms," Int. J. Forecast., 2020.
[15] Rajesh, H. Pai, V. Roy, S. Samanta, and S. Ghosh, "CoVID-19 prediction for India from the existing data and SIR (D) model study," medRxiv, 2020.
[16] M. N. Sohail, R. Jiadong, M. M. Uba, and M. Irshad, "A comprehensive looks at data mining techniques contributing to medical data growth: A survey of researcher reviews," in Recent Developments in Intelligent Computing, Communication and Devices. Singapore: Springer, 2019, pp. 21–26.
[17] C. W. Song, H. Jung, and K. Chung, "Development of a medical big-data mining process using topic modeling," Cluster Comput., vol. 22, no. 1, pp. 1949–1958, 2019.
[18] M. Nikooghadam, A. Ghazikhani, and M. Saeedi, "COVID-19 prediction classifier model using hybrid algorithms in data mining," Int. J. Pediatr., vol. 9, no. 1, pp. 12723–12737, 2021.
[19] S. H. Yoo et al., "Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging," Front. Med., vol. 7, p. 427, 2020.
[20] M. A. Rahman, M. S. Hossain, and A. Ghoneim, "Federated deep learning approach for COVID-19 prediction using IoT and wearable data," Future Generation Computer Systems, vol. 135, pp. 130–141, 2022.
[21] 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.
[22] 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.
[23] S. Vaiz and M. Ramaswami, "A study on technical indicators in stock price movement prediction using decision tree algorithms," Amer. J. Eng. Res., vol. 5, no. 12, pp. 207–212, 2016.
[24] "Novel Coronavirus (COVID-19) Cases Dataset," Humanitarian Data Exchange, 2021. [Online]. Available: https://data.humdata.org/dataset/novel-coronavirus-2019-ncov-cases
[25] S. Minaee, R. Kafieh, M. Sonka, S. Yazdani, and G. J. Soufi, "Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning," Medical Image Analysis, vol. 65, pp. 101794, 2020.
[26] S. Bhattacharya et al., "Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey," Sustain. Cities Soc., vol. 65, p. 102589, 2021.
[27] D. Liu, Z. Wu, and S. Sun, "Study on subway passenger flow prediction based on deep recurrent neural network," Multimedia Tools Appl., pp. 1–14, 2020.
[28] R. Thomas and M. J. S. Rangachar, "Hybrid optimization based DBN for face recognition using low-resolution images," Multimedia Res., vol. 1, no. 1, pp. 33–43, 2018.
[29] K. M. Gangappa, C. Kiran Mai, and P. Sammulal, "Enhanced crow search optimization algorithm and hybrid NN-CNN classifiers for classification of land cover images," Multimedia Res., vol. 2, no. 3, pp. 12–22, 2019.
[30] A. Mohammed, A. B. et al., "Developing plastic recycling classifier by deep learning and directed acyclic graph residual network," Eastern-European Journal of Enterprise Technologies, vol. 2, no. 10, pp. 116, 2022.
[31] B. Mohammed et al., "Developing plastic recycling classifier by deep learning and directed acyclic graph residual network," East.-Eur. J. Enterp. Technol., vol. 2, no. 10, p. 116, 2022.
[32] M. A. Rahman, M. S. Hossain, and A. Ghoneim, "Federated deep learning approach for COVID-19 prediction using IoT and wearable data," Future Gener. Comput. Syst., vol. 135, pp. 130–141, 2022.
[33] H. O. Ahmad and S. U. Umar, "Sentiment analysis of financial textual data using machine learning and deep learning models," Informatica, vol. 47, no. 5, 2023.
[34] H. Adhab and N. A. H. Ali, "Influence of wireless novel routing protocol by using MDPC algorithm," in Proc. 4th Int. Conf. Technol. Adv. Comput. Sci. (ICTACS), 2024.
