A Novel Approach Based to Neural Network and Flower Pollination Algorithm to Predict Number of COVID-19 Cases

Yıl 2021, Cilt: 9 Sayı: 4, 327 - 336, 30.10.2021

Ceren Baştemur Kaya Ebubekir Kaya

Öz

Flower Pollination Algorithm (FPA) is one of the popular heuristic algorithms that model pollination in the natural environment. Since 2012, it has been used in the solution of many difficult real world problems and successful results have been achieved. In this study, FPA is used for the training of neural network to predict number of COVID-19 cases. Namely, a model based on FPA and neural network (FPA_NN) is proposed. Within the scope of application, the data belonging to Turkey are estimated using the proposed model. A data set is created with the data between 1 April 2020 and 15 September 2020. A time series is created with these data and the nonlinear dynamic systems are obtained to model the problem. In order to determine the performance of the proposed model, RMSE (root mean square error) are found. The output graphs of the results are also examined in detail. The results are compared with neural network approaches based on PSO and HS. The Wilcoxon signed rank test is utilized to determine the significance of the results. The results show that FPA is generally more effective than PSO and HS to predict number of COVID-19 cases based on neural network.

Anahtar Kelimeler

COVID-19, flower pollination algorithm, neural network, swarm intelligence.

Kaynakça

• [1] A. B. Younes, Z. Hasan. “COVID-19: modeling, prediction, and control.” Applied Sciences, vol. 10, no. 11, 2020, pp. 3666.
• [2] L. Duran-Lopez, J. P. Dominguez-Morales, J. Corral-Jaime, S. Vicente-Diaz, A. Linares-Barranco. “COVID-XNet: A custom deep learning system to diagnose and locate COVID-19 in Chest X-ray images.” Applied Sciences, vol. 10, no. 16, 2020, pp. 5683.
• [3] R. Pal, A.A. Sekh, S. Kar, D.K. Prasad. “Neural network based country wise risk prediction of COVID-19.” Applied Sciences, vol. 10, no. 18, 2020, pp. 6448.
• [4] D. Ezzat, A.E. Hassanien, H.A. Ella. “An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization.” Applied Soft Computing, 2020, pp. 106742.
• [5] A.M. Ismael, A. Şengür. “Deep learning approaches for COVID-19 detection based on chest X-ray images.” Expert Systems with Applications, 2020, pp. 114054.
• [6] C. Zhan, Y. Zheng, Z. Lai, T. Hao, B. Li. “Identifying epidemic spreading dynamics of COVID-19 by pseudocoevolutionary simulated annealing optimizers.” Neural Computing and Applications, 2020, pp. 1-14.
• [7] M.A. Al-Qaness, A.A. Ewees, H. Fan, M. Abd El Aziz. “Optimization method for forecasting confirmed cases of COVID-19 in China.” Journal of Clinical Medicine, vol. 9, no. 3, 2020, pp. 674.
• [8] P. Melin, J.C. Monica, D. Sanchez, O. Castillo, “Multiple ensemble neural network models with fuzzy response aggregation for predicting COVID-19 time series: The case of Mexico.” In Healthcare, vol. 8, no. 2, 2020, June, pp. 181, Multidisciplinary Digital Publishing Institute.
• [9] M.A. Al-Qaness, A.A. Ewees, H. Fan, L. Abualigah, M. Abd Elaziz. “Marine predators algorithm for forecasting confirmed cases of COVID-19 in Italy, USA, Iran and Korea.” International Journal of Environmental Research and Public Health, vol. 17, no. 10, 2020, pp. 3520.
• [10] A.I. Saba, A.H. Elsheikh. “Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks.” Process Safety and Environmental Protection, 2020.
• [11] S. Lalmuanawma, J. Hussain, L. Chhakchhuak. “Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review.” Chaos, Solitons & Fractals, 2020, pp. 110059.
• [12] F. Shi, J. Wang, J. Shi, Z. Wu, Q. Wang, Z. Tang, ... , D. Shen. “Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for Covid-19.” IEEE Reviews in Biomedical Engineering, 2020.
• [13] R. Vaishya, M. Javaid, I. H. Khan, A. Haleem. “Artificial Intelligence (AI) applications for COVID-19 pandemic.” Diabetes & Metabolic Syndrome: Clinical Research & Reviews, vol. 14, no. 4, 2020.
• [14] Y. Mohamadou, A. Halidou, P.T. Kapen. “A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19.” Applied Intelligence, vol. 1, no. 13, 2020.
• [15] I.E. Agbehadji, B.O. Awuzie, A.B. Ngowi, R.C. Millham. “Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing.” International Journal of Environmental Research and Public Health, vol. 17, no. 15, 2020, pp. 5330.
• [16] S. Dreiseitl, L. Ohno-Machado. “Logistic regression and artificial neural network classification models: A methodology review.” Journal of Biomedical Informatics, vol. 35, no. 5-6, 2002, pp. 352-359.
• [17] P. J. Lisboa, A.F. Taktak. “The use of artificial neural networks in decision support in cancer: A systematic review.” Neural Networks, vol. 19, no. 4, 2006, pp. 408-415.
• [18] A. Tealab. “Time series forecasting using artificial neural networks methodologies: A systematic review.” Future Computing and Informatics Journal, vol. 3, no. 2, 2018, pp. 334-340.
• [19] R. K. Dase, D.D. Pawar.” Application of artificial neural network for stock market predictions: A review of literature.” International Journal of Machine Intelligence, vol. 2, no. 2, 2010, pp. 14-17.
• [20] A. Dhillon, G.K. Verma. “Convolutional neural network: A review of models, methodologies and applications to object detection.” Progress in Artificial Intelligence, vol. 9, no. 2, 2020, pp. 85-112.
• [21] C. Harpham, C.W. Dawson, M.R. Brown. “A review of genetic algorithms applied to training radial basis function networks.” Neural Computing & Applications, vol. 13, no. 3, 2004, pp. 193-201.
• [22] Y. Azimi, S.H. Khoshrou, M. Osanloo. “Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network.” Measurement, vol. 147, 2019, pp. 106874.
• [23] K. Taheri, M. Hasanipanah, S.B. Golzar, M.Z. Abd Majid. “A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration.” Engineering with Computers, vol. 33, no. 3, 2017, pp. 689-700.
• [24] D. Karaboga, B. Akay, C. Ozturk. “Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks.” International conference on modeling decisions for artificial intelligence, Springer, Berlin, Heidelberg, August 2007, pp. 318-329.
• [25] J. Yu, S. Wang, L. Xi. ”Evolving artificial neural networks using an improved PSO and DPSO.” Neurocomputing, vol. 71, no. 4-6, 2008, pp. 1054-1060.
• [26] S.K. Satapathy, S. Dehuri, A.K. Jagadev. “EEG signal classification using PSO trained RBF neural network for epilepsy identification.“ Informatics in Medicine Unlocked, vol. 6, 2017, pp. 1-11.
• [27] J. Saadat, P. Moallem, H. Koofigar. “Training echo state neural network using harmony search algorithm.” Int. J. Artif. Intell, vol. 15, no. 1, 2017, pp. 163-179.
• [28] S. Kulluk, L. Ozbakir, A. Baykasoglu. “Training neural networks with harmony search algorithms for classification problems.” Engineering Applications of Artificial Intelligence, vol. 25, no.1, 2012, pp. 11-19.
• [29] J. Ilonen, J.K. Kamarainen, J. Lampinen. “Differential evolution training algorithm for feed-forward neural networks.” Neural Processing Letters, vol. 17, no. 1, 2003, pp. 93-105.
• [30] N. Chauhan, V. Ravi, D.K. Chandra. “Differential evolution trained wavelet neural networks: Application to bankruptcy prediction in banks.” Expert Systems with Applications, vol. 36, no. 4, 2009, pp. 7659-7665.
• [31] J. Nayak, B. Naik, D. Pelusi, A.V. Krishna. “A comprehensive review and performance analysis of firefly algorithm for artificial neural networks.” In Nature-Inspired Computation in Data Mining and Machine Learning, 2020, Springer, Cham, pp. 137-159.
• [32] E. Valian, S. Mohanna, S. Tavakoli. “Improved cuckoo search algorithm for feedforward neural network training.” International Journal of Artificial Intelligence & Applications, vol. 2, no.3, 2011, pp. 36-43.
• [33] X. Liang, W. Liang, J. Xiong. “Intelligent diagnosis of natural gas pipeline defects using improved flower pollination algorithm and artificial neural network.” Journal of Cleaner Production, 2020, 121655.
• [34] P.A. Kowalski, K. Wadas. “Triggering probabilistic neural networks with flower pollination algorithm.” Computational Intelligence and Mathematics for Tackling Complex Problems, 2020, Springer, Cham, pp. 107-113.
• [35] P. Dutta, A. Kumar. “Modeling and optimization of a liquid flow process using an artificial neural network-based flower pollination algorithm.” Journal of Intelligent Systems, vol. 29, no.1, 2018, pp. 787-798.
• [36] G.S. Shehu, N. Çetinkaya. “Flower pollination–feedforward neural network for load flow forecasting in smart distribution grid.” Neural Computing and Applications, vol. 31, no. 10, 2019, pp. 6001-6012.
• [37] M.H.B.A. Yazid, M.S. Talib, M.H. Satria. “Flower pollination neural network for heart disease classification.” IOP Conference Series: Materials Science and Engineering, IOP Publishing, vol. 551, no. 1, August 2019, pp. 012072.
• [38] L. Pan, X. Feng, F. Sang, L. Li, M. Leng, X. Chen. “An improved back propagation neural network based on complexity decomposition technology and modified flower pollination optimization for short-term load forecasting.” Neural Computing and Applications, vol. 31, no. 7, 2019, pp. 2679-2697.
• [39] Y. Ren, H. Li, H.C. Lin. “Optimization of feedforward neural networks using an improved flower pollination algorithm for short-term wind speed prediction.” Energies, vol. 12, no. 21, 2019, pp. 4126.
• [40] S. Chatterjee, B. Datta, N. Dey. “Hybrid neural network based rainfall prediction supported by flower pollination algorithm.” Neural Network World, vol. 28, no. 6, 2018, pp. 497-510.
• [41] M. Abdel-Basset, L.A. Shawky. “Flower pollination algorithm: A comprehensive review.” Artificial Intelligence Review, vol. 52, no. 4, 2019, pp. 2533-2557.
• [42] A.E. Kayabekir, G. Bekdaş, S.M. Nigdeli, X.S. Yang. “A comprehensive review of the flower pollination algorithm for solving engineering problems.” Nature-Inspired Algorithms and Applied Optimization, Springer, Cham, 2018, pp. 171-188.
• [43] X.S. Yang. “Flower pollination algorithm for global optimization.” Unconventional Computation and Natural Computation, edited by J. Durand-Lose and N. Jonoska, Berlin: Springer, vol. 7445 of Lecture Notes in Computer Science, 2012, pp. 240–249.
• [44] K. Lachhwani. “Application of neural network models for mathematical programming problems: A state of art review.” Archives of Computational Methods in Engineering, vol. 27, no. 1, 2020, pp. 171-182.
• [45] T. K. Gupta, K. Raza. “Optimization of ANN architecture: A review on nature-inspired techniques.” Machine Learning in Bio-Signal Analysis and Diagnostic Imaging, Academic Press, 2019, pp. 159-182.