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research-article

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain

Authors:

Ertugrul Ayyildiz,

Melike Erdogan,

Alev TaskinAuthors Info & Claims

Volume 139, Issue C

https://doi.org/10.1016/j.compbiomed.2021.105029

Published: 01 December 2021 Publication History

Abstract

This study introduces a forecasting model to help design an effective blood supply chain mechanism for tackling the COVID-19 pandemic. In doing so, first, the number of people recovered from COVID-19 is forecasted using the Artificial Neural Networks (ANNs) to determine potential donors for convalescent (immune) plasma (CIP) treatment of COVID-19. This is performed explicitly to show the applicability of ANNs in forecasting the daily number of patients recovered from COVID-19. Second, the ANNs-based approach is further applied to the data from Italy to confirm its robustness in other geographical contexts. Finally, to evaluate its forecasting accuracy, the proposed Multi-Layer Perceptron (MLP) approach is compared with other traditional models, including Autoregressive Integrated Moving Average (ARIMA), Long Short-term Memory (LSTM), and Nonlinear Autoregressive Network with Exogenous Inputs (NARX). Compared to the ARIMA, LSTM, and NARX, the MLP-based model is found to perform better in forecasting the number of people recovered from COVID-19. Overall, the findings suggest that the proposed model is robust and can be widely applied in other parts of the world in forecasting the patients recovered from COVID-19.

Highlights

•

The number of recovered COVID-19 patients in Turkey is tried to forecast.

•

The predictors for daily number of recovered patients of COVID-19 are determined.

•

Daily number of recovered patients is forecasted via MLP.

•

The robustness of the algorithm is evaluated by analyzing data from Italy.

•

The number of deaths from COVID-19 in Turkey is also forecasted.

References

[1]

Y. Quintero, D. Ardila, E. Camargo, F. Rivas, J. Aguilar, Machine learning models for the prediction of the SEIRD variables for the COVID-19 pandemic based on a deep dependence analysis of variables, Comput. Biol. Med. 134 (Jul. 2021) 104500,.

Digital Library

[2]

B. Shi, et al., Evolutionary warning system for COVID-19 severity: colony predation algorithm enhanced extreme learning machine, Comput. Biol. Med. (Jul. 2021) 104698,.

Digital Library

[3]

K. Açiksari, K. Kinik, Experience in an emergency department of research and training hospital during the course of COVID-19 outbreak in Turkey, Anadolu Klin. Tıp Bilim. Derg. 25 (May 2020) 263–283,. Special Issue on COVID 19.

[4]

S. Rozenberg, J. Vandromme, and M. Charlotte, “Are we equal in adversity? Does Covid-19 affect women and men differently?,” Maturitas, vol. 138, pp. 62–68, Aug. 2020.

[5]

K. Duan, et al., Effectiveness of convalescent plasma therapy in severe COVID-19 patients, Proc. Natl. Acad. Sci. Unit. States Am. 117 (17) (Apr. 2020) 9490–9496,.

[6]

H.C. Sullivan, J.D. Roback, Convalescent plasma: therapeutic hope or hopeless strategy in the SARS-CoV-2 pandemic, Transfus. Med. Rev. 34 (3) (Jul. 2020) 145–150,.

[7]

L. Li, et al., Effect of convalescent plasma therapy on time to clinical improvement in patients with severe and life-threatening COVID-19: a randomized clinical trial, J. Am. Med. Assoc. 324 (5) (Aug. 2020) 460–470,.

[8]

Ö. Özdemir, H.E.M. Arsoy, Convalescent (immune) plasma therapy with all aspects: yesterday, today and COVID-19, Erciyes Med. J. 42 (2020).

[9]

M.P. Biju, M. Prajitha Biju, Convalescent plasma as a potential therapy for treating COVID-19 patients, Pharm. Reson. 3 (2020) 1.

[10]

P.W. Askenase, COVID-19 therapy with mesenchymal stromal cells (MSC) and convalescent plasma must consider exosome involvement: do the exosomes in convalescent plasma antagonize the weak immune antibodies?, J. Extracell. Vesicles 10 (1) (Nov. 2020) e12004,.

[11]

B. Bajelan, et al., Convalescent plasma successfully treats a severe COVID-19 patient with multi-organ failure, Biomed. Res. Ther. 7 (10) (Oct. 2020) 4022–4025,.

[12]

D. Moher, A. Liberati, J. Tetzlaff, D.G. Altman, T.P. Group, Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement, PLoS Med. 6 (7) (Jul. 2009) e1000097,.

[13]

R.P. Santi, H. Putra, A systematic literature review of business intelligence technology, contribution and application for higher education, 2018 Int. Conf. Inf. Technol. Syst. Innov. ICITSI 2018 - Proc. (Jul. 2018) 404–409,.

[14]

D. Satria, D.I. Sensuse, H. Noprisson, A systematic literature review of the improved agile software development, 2017 Int. Conf. Inf. Technol. Syst. Innov. ICITSI 2017 - Proc. 2018 (Jul. 2017) 94–99,. Janua.

[15]

I. Ahmad and S. M. Asad, “Predictions of coronavirus COVID-19 distinct cases in Pakistan through artificial neural network,” Epidemiol. Infect., vol. 148, 2020.

[16]

S. Bodapati, H. Bandarupally, M. Trupthi, COVID-19 time series forecasting of daily cases, deaths caused and recovered cases using long short term memory networks, in: In 2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020, Oct. 2020, pp. 525–530,.

[17]

A.I. Saba, A.H. Elsheikh, Forecasting the prevalence of COVID-19 outbreak in Egypt using nonlinear autoregressive artificial neural networks, Process Saf. Environ. Protect. 141 (Sep. 2020) 1–8,.

[18]

O. Istaiteh, T. Owais, N. Al-Madi, S. Abu-Soud, Machine learning approaches for COVID-19 forecasting,” in 2020 international Conference on intelligent data science Technologies and applications, IDSTA (Oct. 2020) 50–57,. 2020.

[19]

M.A.A. Al-qaness, et al., Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil, Process Saf. Environ. Protect. 149 (May 2021) 399–409,.

[20]

H. Abbasimehr, R. Paki, Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization, Chaos, Solit. Fractals (Nov. 2020) 110511,.

[21]

A.H. Elsheikh, et al., Deep learning-based forecasting model for COVID-19 outbreak in Saudi Arabia, Process Saf. Environ. Protect. 149 (May 2021) 223–233,.

[22]

N.N. Hamadneh, W.A. Khan, W. Ashraf, S.H. Atawneh, I. Khan, B.N. Hamadneh, Artificial neural networks for prediction of covid-19 in Saudi Arabia, Comput. Mater. Continua (CMC) 66 (3) (2021) 2787–2796,.

[23]

L. Moftakhar, M. Seif, M.S. Safe, Exponentially increasing trend of infected patients with covid-19 in Iran: a comparison of neural network and arima forecasting models, Iran. J. Public Health 49 (Apr. 2020) 92–100,. Supple 1.

[24]

R. Ünlü, E. Namli, Machine learning and classical forecasting methods based decision support systems for covid-19, Comput. Mater. Continua (CMC) 64 (3) (Jun. 2020) 1383–1399,.

[25]

H.R. Niazkar, M. Niazkar, Application of artificial neural networks to predict the COVID-19 outbreak, Glob. Heal. Res. Policy 5 (1) (Dec. 2020),.

[26]

N.N. Hamadneh, M. Tahir, W.A. Khan, Using artificial neural network with prey predator algorithm for prediction of the COVID-19: the case of Brazil and Mexico, Math. 9 (2) (Jan. 2021) 180,. 2021, Vol. 9, Page 180.

[27]

G. Toğa, B. Atalay, M.D. Toksari, COVID-19 prevalence forecasting using autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN): case of Turkey, J. Infect. Public Health 14 (7) (Jul. 2021) 811–816,.

[28]

P. Kumari, D. Toshniwal, Real-time estimation of COVID-19 cases using machine learning and mathematical models-The case of India, 2020 IEEE 15th Int. Conf. Ind. Inf. Syst. ICIIS 2020 - Proc. (Nov. 2020) 369–374,.

[29]

R.A. Conde-Gutiérrez, D. Colorado, S.L. Hernández-Bautista, Comparison of an artificial neural network and Gompertz model for predicting the dynamics of deaths from COVID-19 in México, Nonlinear Dynam. 104 (4) (Apr. 2021) 4655–4669,. 2021 1044.

[30]

S.K. Safi, O.I. Sanusi, A hybrid of artificial neural network, exponential smoothing, and ARIMA models for COVID-19 time series forecasting, Model Assisted Statistics Appl. 16 (1) (Jan. 2021) 25–35,.

[31]

M. Wieczorek, J. Siłka, D. Połap, M. Woźniak, R. Damaševičius, Real-time neural network based predictor for cov19 virus spread, PLoS One 15 (12) (Dec. 2020) e0243189,.

[32]

M. de B. Braga, et al., Artificial neural networks for short-term forecasting of cases, deaths, and hospital beds occupancy in the COVID-19 pandemic at the Brazilian Amazon, PLoS One 16 (3) (2021) e0248161,.

[33]

R.P. Shetty, P.S. Pai, Forecasting of COVID 19 cases in Karnataka state using artificial neural network (ANN), J. Inst. Eng. Ser. B (2021) 1,.

[34]

S.K. Tamang, P.D. Singh, B. Datta, Forecasting of Covid-19 cases based on prediction using artificial neural network curve fitting technique, Glob. J. Environ. Sci. Manag. 6 (Aug. 2020) 53–64,. Special Issue (Covid-19).

[35]

P. Melin, D. Sánchez, J.C. Monica, O. Castillo, Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction, Soft Comput. (Jan. 2021) 1–38,. 2021.

Digital Library

[36]

S.F. Ardabili, et al., COVID-19 outbreak prediction with machine learning, medRxiv 13 (10) (Apr. 2020),. 2020.04.17.20070094.

[37]

A.S. Ahmar, E. Boj, Application of neural network time series (Nnar) and arima to forecast infection fatality rate (ifr) of covid-19 in Brazil, Int. J. Informatics Vis. 5 (1) (2021) 8–10,.

[38]

S. Ardabili, A. Mosavi, S.S. Band, A.R. Varkonyi-Koczy, Coronavirus disease (COVID-19) global prediction using hybrid artificial intelligence method of ANN trained with grey wolf optimizer, CANDO-EPE 2020 - Proceedings, IEEE 3rd Int. Conf. Work. Obuda Electr. Power Eng. (Nov. 2020) 251–254,.

[39]

T. Alsmadi, N. Alqudah, H. Najadat, Prediction of Covid-19 patients states using Data mining techniques, 2021 Int. Conf. Inf. Technol. ICIT 2021 - Proc. (Jul. 2021) 251–256,.

[40]

L.S. de Oliveira, S.B. Gruetzmacher, J.P. Teixeira, COVID-19 time series prediction, Procedia Comput. Sci. 181 (Jan. 2021) 973–980,.

[41]

S. Alkadri, et al., Utilizing a multilayer perceptron artificial neural network to assess a virtual reality surgical procedure, Comput. Biol. Med. 136 (Sep. 2021) 104770,.

Digital Library

[42]

N.N.A. Mangshor, S. Ibrahim, N. Sabri, S.A. Kamaruddin, Students' learning habit factors during COVID-19 pandemic using multilayer perceptron (MLP), Int. J. Adv. Technol. Eng. Explor. 8 (74) (2021) 190–198,.

[43]

M. Mukaka, A guide to appropriate use of Correlation coefficient in medical research, Malawi Med. J. 24 (3) (2012) 69–71,.

[44]

J. Lee Rodgers, W. Alan Nice Wander, Thirteen ways to look at the correlation coefficient, Am. Statistician 42 (1) (1988) 59–66,.

[45]

J. Ziółkowski, M. Oszczypała, J. Małachowski, J. Szkutnik-Rogoż, Use of artificial neural networks to predict fuel consumption on the basis of technical parameters of vehicles, Energies 14 (9) (May 2021) 2639,.

[46]

R. Taylor, Interpretation of the correlation coefficient: a basic review, J. Diagn. Med. Sonogr. 6 (1) (Jan. 1990) 35–39,.

[47]

X. Yao, Evolving artificial neural networks, Proc. IEEE 87 (9) (1999) 1423–1447,.

[48]

A.F. Guneri, A.T. Gumus, The usage of artificial neural networks for finite capacity planning, Int. J. Ind. Eng. Theory, Appl. Pract. 15 (1) (2008) 16–25.

[49]

A. Arslan, R. Ince, The neural network approximation to the size effect in fracture of cementitious materials, Eng. Fract. Mech. 54 (2) (May 1996) 249–261,.

[50]

S. Ozsahin, M. Murat, Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks, Eur. J. Wood Wood Prod. 76 (2) (Mar. 2018) 563–572,.

[51]

G.C. Akkaya, E.D. ve, Ü.H. Yakut, E. Demi̇reli̇, H. Yakut, İşletmelerde fi?nansal basarisizlik tahmi?nlemesi?: yapay si?ni?r aglari modeli? i?le i?mkb üzeri?ne bi?r UYGULAMA, Eskişehir Osmangazi Üniversitesi Sos. Bilim. Derg. 10 (2) (Jun. 2009) 187–216.

[52]

S. Amid, T. Mesri Gundoshmian, Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models, Environ. Prog. Sustain. Energy 36 (2) (Mar. 2017) 577–585,.

[53]

M. Madhiarasan, S.N. Deepa, Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting, Artif. Intell. Rev. 48 (4) (Dec. 2017) 449–471,.

Digital Library

[54]

S. Mahdevari, K. Shahriar, M. Sharifzadeh, D.D. Tannant, Stability prediction of gate roadways in longwall mining using artificial neural networks, Neural Comput. Appl. 28 (11) (Nov. 2017) 3537–3555,.

Digital Library

[55]

S. Agatonovic-Kustrin, R. Beresford, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research, J. Pharmaceut. Biomed. Anal. 22 (5) (Jun. 01, 2000) 717–727,. Elsevier.

[56]

A. Graves, M. Liwicki, S. Fernández, R. Bertolami, H. Bunke, and J. Schmidhuber, “A Novel Connectionist System for Unconstrained Handwriting Recognition.”.

[57]

A. Elkenawy, A.M. El-Nagar, M. El-Bardini, N.M. El-Rabaie, Full-state neural network observer-based hybrid quantum diagonal recurrent neural network adaptive tracking control, Neural Comput. Appl. (Jan. 2021) 1–20,.

Digital Library

[58]

J. Mata, Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models, Eng. Struct. 33 (3) (Mar. 2011) 903–910,.

[59]

P.S.G. De Mattos Neto, et al., Neural-based ensembles for particulate matter forecasting, IEEE Access 9 (2021) 14470–14490,.

[60]

C. Van Der Malsburg, “Frank Rosenblatt, Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms,” in Brain Theory , Springer Berlin Heidelberg, 1986, pp. 245–248.

[61]

H. Demuth, M. Beale, Neural Network Toolbox User's Guide, 2000.

[62]

J.C.R. Whittington, R. Bogacz, Theories of error back-propagation in the brain, Trends Cognit. Sci. 23 (3) (Mar. 2019) 235–250,.

[63]

X. Yu, N.K. Loh, W.C. Miller, New acceleration technique for the backpropagation algorithm, 1993 IEEE Int. Conf. Neural Networks (1993) 1157–1161,.

[64]

H. Karimi, F. Yousefi, Application of artificial neural network–genetic algorithm (ANN–GA) to correlation of density in nanofluids, Fluid Phase Equil. 336 (Dec. 2012) 79–83,.

[65]

R. Hecht-Nielsen, “Neurocomputer Applications,” in Neural Computers , Springer Berlin Heidelberg, Berlin, Heidelberg, 1989, pp. 445–453.

[66]

T. Al-Saba, I. El-Amin, Artificial neural networks as applied to long-term demand forecasting, Artif. Intell. Eng. 13 (2) (Apr. 1999) 189–197,.

[67]

M. Seyhan, Y.E. Akansu, M. Murat, Y. Korkmaz, S.O. Akansu, Performance prediction of PEM fuel cell with wavy serpentine flow channel by using artificial neural network, Int. J. Hydrogen Energy 42 (40) (Oct. 2017) 25619–25629,.

[68]

I.A. Basheer, M. Hajmeer, Artificial neural networks: fundamentals, computing, design, and application, J. Microbiol. Methods 43 (1) (Dec. 2000) 3–31,.

[69]

I. Juhos, L. Makra, B. Tóth, The behaviour of the multi-layer perceptron and the support vector regression learning methods in the prediction of NO and NO 2 concentrations in Szeged, Hungary, Neural Comput. Appl. 18 (2) (Feb. 2009) 193–205,.

Digital Library

[70]

S. Amid, T. Mesri Gundoshmian, Prediction of output energies for broiler production using linear regression, ANN (MLP, RBF), and ANFIS models, Environ. Prog. Sustain. Energy 36 (2) (Mar. 2017) 577–585,.

[71]

C.W. Dawson, R. Wilby, An artificial neural network approach to rainfall-runoff modelling, Hydrol. Sci. J. 43 (1) (1998) 47–66,.

[72]

C. Mo, D. Gerontitis, P.S. Stanimirović, Solving the time-varying tensor square root equation by varying-parameters finite-time Zhang neural network, Neurocomputing 445 (Jul. 2021) 309–325,.

[73]

A.N. Refenes, M. Azema-Barac, L. Chen, S.A. Karoussos, Currency exchange rate prediction and neural network design strategies, Neural Comput. Appl. 1 (1) (Mar. 1993) 46–58,.

[74]

S.B. Sarmah, et al., Numerical and experimental investigation of state of health of Li-ion battery, Int. J. Green Energy 17 (8) (Jun. 2020) 510–520,.

[75]

M. Urquidi-Macdonald, B.R. Tittmann, M.G. Koopman, Artificial neural networks to interpret acoustic emission signals to detect early delamination during carbonization of pre-fabricated components of carbon-carbon composite material, Proc. IEEE Ultrason. Symp. 2 (1994) 1303–1306,.

[76]

A.U. Mazlan, N.A. binti Sahabudin, M.A. Remli, N.S.N. Ismail, M.S. Mohamad, N.B.A. Warif, Supervised and unsupervised machine learning for cancer classification: recent development, 2021 IEEE Int. Conf. Autom. Control Intell. Syst. I2CACIS 2021 - Proc. (Jun. 2021) 392–395,.

[77]

P. Lauret, F. Heymes, S. Forestier, L. Aprin, A. Pey, M. Perrin, Forecasting powder dispersion in a complex environment using Artificial Neural Networks, Process Saf. Environ. Protect. 110 (2017) 71–76,.

[78]

R. González Perea, E. Camacho Poyato, P. Montesinos, J.A. Rodríguez Díaz, Optimisation of water demand forecasting by artificial intelligence with short data sets, Biosyst. Eng. 177 (2019) 59–66,.

[79]

S. Zhu, M. Ptak, Z. M. Yaseen, J. Dai, and B. Sivakumar, “Forecasting surface water temperature in lakes: a comparison of approaches,” J. Hydrol., vol. 585, 2020.

[80]

Z. Ali, et al., Forecasting drought using multilayer perceptron artificial neural network model, Adv. Meteorol. 2017 (2017),.

[81]

F.Y. Dtissibe, A.A.A. Ari, C. Titouna, O. Thiare, A.M. Gueroui, Flood forecasting based on an artificial neural network scheme, Nat. Hazards 104 (2) (2020) 1211–1237,.

[82]

Z. Liu, C.K. Loo, K. Pasupa, A novel error-output recurrent two-layer extreme learning machine for multi-step time series prediction, Sustain. Cities Soc. 66 (Nov. 2020) 102613,.

[83]

J. Yu, X. Zhang, L. Xu, J. Dong, L. Zhangzhong, A hybrid CNN-GRU model for predicting soil moisture in maize root zone, Agric. Water Manag. 245 (Feb. 2021) 106649,.

[84]

F. Gao, X. Shao, Forecasting annual natural gas consumption via the application of a novel hybrid model, Environ. Sci. Pollut. Res. (Jan. 2021) 1–14,.

[85]

M.A. Al Amin, M.A. Hoque, Comparison of ARIMA and SVM for short-term load forecasting, in: In IEMECON 2019 - 9th Annual Information Technology, Electromechanical Engineering And Microelectronics Conference , Mar. 2019, pp. 205–210,.

[86]

Ö.F. Ertuğrul, M.E. Tağluk, Forecasting financial indicators by generalized behavioral learning method, Soft Comput. 22 (24) (Dec. 2018) 8259–8272,.

Digital Library

[87]

A.M.A. Sattar, Ö.F. Ertuğrul, B. Gharabaghi, E.A. McBean, J. Cao, Extreme learning machine model for water network management, Neural Comput. Appl. 31 (1) (Jan. 2019) 157–169,.

Digital Library

[88]

Ö.F. Ertuğrul, H. Tekin, R. Tekin, A novel regression method in forecasting short-term grid electricity load in buildings that were connected to the smart grid, Electr. Eng. 103 (1) (Feb. 2021) 717–728,.

[89]

F. Cosentino, et al., On the role of material properties in ascending thoracic aortic aneurysms, Comput. Biol. Med. 109 (Jun. 2019) 70–78,.

Digital Library

[90]

S. Kim, H. Kim, A new metric of absolute percentage error for intermittent demand forecasts, Int. J. Forecast. 32 (3) (Jul. 2016) 669–679,.

[91]

R. Byrne, Beyond traditional time-series: using demand sensing to improve forecasts in volatile times, J. Bus. Forecast. 31 (2) (2012) 13.

[92]

Ö.F. Ertuğrul, Ş. Altun, Determining optimal artificial neural network training method in predicting the performance and emission parameters of a biodiesel-fueled diesel generator, Int. J. Automot. Eng. Technol. 7 (1) (Apr. 2018) 7–17,.

[93]

M. Nevendra, P. Singh, Defect count prediction via metric-based convolutional neural network, Neural Comput. Appl. (Jun. 2021) 1–26,.

Digital Library

[94]

Y. Tao, G. Yue, X. Wang, Dual-attention network with multitask learning for multistep short-term speed prediction on expressways, Neural Comput. Appl. 33 (12) (Jun. 2021) 7103–7124,.

Digital Library

[95]

N. Zhang, S.L. Shen, A. Zhou, Y.S. Xu, Investigation on performance of neural networks using quadratic relative error cost function, IEEE Access 7 (2019) 106642–106652,.

[96]

S.A. Fatemi, A. Kuh, M. Fripp, Parametric methods for probabilistic forecasting of solar irradiance, Renew. Energy 129 (Dec. 2018) 666–676,.

[97]

P.P. Brahma, Prediction of sunspot number using minimum error entropy cost based kernel adaptive filters, in: In Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015, Mar. 2016, pp. 325–329,.

[98]

D. Zhai, Y.C. Soh, Balancing indoor thermal comfort and energy consumption of air-conditioning and mechanical ventilation systems via sparse Firefly algorithm optimization, in: In Proceedings Of the International Joint Conference On Neural Networks , Jun. 2017, pp. 1488–1494,. 2017-May.

[99]

S. Del Favero, A. Facchinetti, C. Cobelli, A glucose-specific metric to assess predictors and identify models, IEEE Trans. Biomed. Eng. 59 (5) (May 2012) 1281–1290,.

[100]

C.M. Anish, B. Majhi, R. Majhi, A novel hybrid model using RBF and PSO for net asset value prediction, in: In Intelligent Systems: Concepts, Methodologies, Tools, and Applications, IGI Global, 2018, pp. 1031–1049.

[101]

E. Pisoni, M. Farina, C. Carnevale, L. Piroddi, Forecasting peak air pollution levels using NARX models, Eng. Appl. Artif. Intell. 22 (4–5) (Jun. 2009) 593–602,.

Digital Library

[102]

A.V. Mathai, A. Agarwal, V. Angampalli, S. Narayanan, E. Dhakshayani, Development of new methods for measuring forecast error, Int. J. Logist. Syst. Manag. 24 (2) (2016) 213–225,.

[103]

N.A. Mohd-Lair, J.K. Hong, M.S. Misaran, The linear regression vs. additive forecast techniques in predicting palm oil estate monthly delivery quantity,” in, Appl. Mech. Mater. 465–466 (2014) 1127–1132. https://doi.org/10.4028/www.scientific.net/AMM.465-466.1127.

[104]

A.J.D. Antoja, P.A.O. Lafamia, C.A.B. Yang, G.V. Magwili, R.V.M. Santiago, Automated short-term load forecasting using modified stochastic hour ahead proportion (SHAP) analysis, in: In 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019, Nov. 2019,.

[105]

Republic of Turkey Ministry of Health (2021): General coronavirus table. https://covid19.saglik.gov.tr/EN-69532/general-coronavirus-table.html.

[106]

M.Q. Raza, A. Khosravi, A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings, Renewable And Sustainable Energy Reviews, vol. 50, Elsevier Ltd, Jun. 18, 2015, pp. 1352–1372,.

[107]

X. Ye, Z. Yao, W. Liu, Y. Fan, Y. Xu, S. Chen, Path analysis to identify factors influencing health skills and behaviors in adolescents: a cross-sectional survey, PLoS One 9 (8) (2014).

[108]

N. George, N. K. Tyagi, and J. B. Prasad, “COVID-19 pandemic and its average recovery time in Indian states,” Clin. Epidemiol. Glob. Heal., vol. 11, Jul. 2021.

[109]

S.A. SeyedAlinaghi, et al., Predictors of the prolonged recovery period in COVID-19 patients: a cross-sectional study, Eur. J. Med. Res. 26 (1) (Dec. 2021),.

[110]

C.M. Chiesa-Estomba, et al., Patterns of smell recovery in 751 patients affected by the COVID-19 outbreak, Eur. J. Neurol. 27 (11) (Nov. 2020) 2318–2321,.

[111]

T. Zhang, et al., Detectable SARS-CoV-2 viral RNA in feces of three children during recovery period of COVID-19 pneumonia, J. Med. Virol. 92 (7) (Jul. 2020) 909–914,.

[112]

L.B. Daniels, et al., Relation of statin use prior to admission to severity and recovery among COVID-19 inpatients, Am. J. Cardiol. 136 (Dec. 2020) 149–155,.

[113]

G. Sorrosal, E. Irigoyen, C.E. Borges, C. Martin, A.M. Macarulla, A. Alonso-Vicario, Artificial neural network modelling of the bioethanol-to-olefins process on a HZSM-5 catalyst treated with alkali, Appl. Soft Comput. J. 58 (Sep. 2017) 648–656,.

[114]

I.B.R. Nogueira, et al., A quasi-virtual online analyser based on an artificial neural networks and offline measurements to predict purities of raffinate/extract in simulated moving bed processes, Appl. Soft Comput. J. 67 (Jun. 2018) 29–47,.

Digital Library

[115]

N.H. Zainuddin, et al., Improvement of time forecasting models using a novel hybridization of bootstrap and double bootstrap artificial neural networks, Appl. Soft Comput. J. 84 (Nov. 2019) 105676,.

Digital Library

[116]

H. Xiao, M. Cao, R. Peng, Artificial neural network based software fault detection and correction prediction models considering testing effort, Appl. Soft Comput. J. 94 (Sep. 2020) 106491,.

[117]

E. Zhu, Y. Ju, Z. Chen, F. Liu, X. Fang, DTOF-ANN: an artificial neural network phishing detection model based on decision tree and optimal features, Appl. Soft Comput. J. 95 (Oct. 2020) 106505,.

[118]

V.Ş. Ediger, S. Akar, ARIMA forecasting of primary energy demand by fuel in Turkey, Energy Pol. 35 (3) (Mar. 2007) 1701–1708,.

[119]

M. Milenković, L. Švadlenka, V. Melichar, N. Bojović, Z. Avramović, SARIMA modelling approach for railway passenger flow forecasting, Transport 33 (5) (Dec. 2018) 1113–1120,.

[120]

D. Kwiatkowski, P.C.B. Phillips, P. Schmidt, Y. Shin, Testing the null hypothesis of stationarity against the alternative of a unit root. How sure are we that economic time series have a unit root?, J. Econom. 54 (1–3) (Oct. 1992) 159–178,.

[121]

V.K. Sudarshan, M. Brabrand, T.M. Range, U.K. Wiil, Performance evaluation of Emergency Department patient arrivals forecasting models by including meteorological and calendar information: a comparative study, Comput. Biol. Med. 135 (Aug. 2021) 104541,.

Digital Library

[122]

T. Santosh, D. Ramesh, D. Reddy, LSTM based prediction of malaria abundances using big data, Comput. Biol. Med. 124 (Sep. 2020) 103859,.

[123]

M. Castangia, A. Aliberti, L. Bottaccioli, E. Macii, E. Patti, A compound of feature selection techniques to improve solar radiation forecasting, Expert Syst. Appl. 178 (Sep. 2021),.

[124]

J. Luo, Z. Zhang, Y. Fu, F. Rao, Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms, Results Phys 27 (Aug. 2021) 104462,.

[125]

A. Di Piazza, M.C. Di Piazza, G. La Tona, M. Luna, An artificial neural network-based forecasting model of energy-related time series for electrical grid management, Math. Comput. Simulat. 184 (Jun. 2021) 294–305,.

[126]

M. Yucesan, E. Pekel, E. Celik, M. Gul, F. Serin, Forecasting daily natural gas consumption with regression, time series and machine learning based methods, Energy Sources, Part A Recover. Util. Environ. Eff. (2021),.

[127]

D.S. Domingos, J.F.L. de Oliveira, P.S.G. de Mattos Neto, An intelligent hybridization of ARIMA with machine learning models for time series forecasting, Knowl. Base Syst. vol. 175 (2019) 72–86,.

Digital Library

[128]

J.F.L. de Oliveira, E.G. Silva, P.S.G. de Mattos Neto, A hybrid system based on dynamic selection for time series forecasting, IEEE Trans. Neural Networks Learn. Syst. (2021),.

[129]

J.F.L. de Oliveira, L.D.S. Pacífico, P.S.G. de Mattos Neto, E.F.S. Barreiros, C.M. de O. Rodrigues, A.T. de A. Filho, A hybrid optimized error correction system for time series forecasting, Appl. Soft Comput. 87 (Feb. 2020) 105970,.

Digital Library

[130]

P.S.G. de Mattos Neto, J.F.L. de Oliveira, D.S. de Oliveira Santos Júnior, H.V. Siqueira, M.H. da Nóbrega Marinho, F. Madeiro, A hybrid nonlinear combination system for monthly wind speed forecasting, IEEE Access 8 (2020) 191365–191377,.

[131]

P.S.G. de Mattos Neto, G.D.C. Cavalcanti, P.R.A. Firmino, E.G. Silva, S.R.P. Vila Nova Filho, A temporal-window framework for modelling and forecasting time series, Knowl. Base Syst. 193 (Apr. 2020) 105476,.

Digital Library

Cited By

Jintanasonti PDumrongsiri ATantiwattanakul P(2024)Retailers' Order Decision with Setup Cost using Machine LearningProceedings of the 2024 8th International Conference on Machine Learning and Soft Computing10.1145/3647750.3647781(37-44)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3647750.3647781
Pehlivanlı DAlp EKatanalp B(2024)Introducing the overall risk scoring as an early warning systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123232246:COnline publication date: 15-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123232
Rekabi SGarjan HGoodarzian FPamucar DKumar A(2024)Designing a responsive-sustainable-resilient blood supply chain network considering congestion by linear regression methodExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122976245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122976
Show More Cited By

Index Terms

Forecasting COVID-19 recovered cases with Artificial Neural Networks to enable designing an effective blood supply chain
1. Applied computing
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image Computers in Biology and Medicine

Computers in Biology and Medicine Volume 139, Issue C

Dec 2021

919 pages

ISSN:0010-4825

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

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Published: 01 December 2021

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Cited By

Jintanasonti PDumrongsiri ATantiwattanakul P(2024)Retailers' Order Decision with Setup Cost using Machine LearningProceedings of the 2024 8th International Conference on Machine Learning and Soft Computing10.1145/3647750.3647781(37-44)Online publication date: 26-Jan-2024
https://dl.acm.org/doi/10.1145/3647750.3647781
Pehlivanlı DAlp EKatanalp B(2024)Introducing the overall risk scoring as an early warning systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123232246:COnline publication date: 15-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123232
Rekabi SGarjan HGoodarzian FPamucar DKumar A(2024)Designing a responsive-sustainable-resilient blood supply chain network considering congestion by linear regression methodExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122976245:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122976
Yin YAhmadianfar IKarim FElmannai H(2024)Advanced forecasting of COVID-19 epidemicComputers in Biology and Medicine10.1016/j.compbiomed.2024.108442175:COnline publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108442
Chen LFan XMao KTolba AAlqahtani FAhmed A(2022)Study of Multidimensional and High-Precision Height Model of Youth Based on Multilayer PerceptronComputational Intelligence and Neuroscience10.1155/2022/78434552022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/7843455
Ayyıldız ETaşkın AYıldız AÖzkan C(2022)Artificial neural networks integrated mixed integer mathematical model for multi-fleet heterogeneous time-dependent cash in transit problem with time windowsNeural Computing and Applications10.1007/s00521-022-07659-734:24(21891-21909)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s00521-022-07659-7

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