International Journal of Mathematical Sciences and Computing (IJMSC)
IJMSC Vol. 7, No. 2, 8 Jun. 2021
Cover page and Table of Contents: PDF (size: 927KB)
Machine Learning Based on Kernel Function Controlled Gaussian Process Regression Method for In-depth Extrapolative Analysis of Covid-19 Daily Cases Drift Rates
Full Text (PDF, 927KB), PP.14-23
Views: 0 Downloads: 0
Author(s)
Joseph Isabona
1,*
Divine O. Ojuh
2
Index Terms
Covid-19 pandemic, short term, Non-stationary data, kernel, Machine learning, Gaussian process regression
Abstract
Precise extrapolative mining and analysis of relevant dataset during or after any disease outbreak can assist the government, stake holders and relevant agencies in the health sector to make important decisions with respect to the disease outbreak control and management. While prior works has concentrated on non-stationary long term data, this work focuses on a short term non-stationary and relatively noisy data. Particularly, a distinctive nonparametric machine learning method based kernel-controlled probabilistic Gaussian process regression model has been proposed and employed to model and analyze Covid-19 pandemic data acquired over a period of approximately six weeks. To accomplish the aim, the MATLAB 2018a computational and machine learning environment was engaged to develop and perform the Gaussian process extrapolative analysis. The results displayed high scalability and optimal performance over the commonly used machine learning methods such as the Neural networks, Neural-Fuzzy networks, Random forest, Regression tree, Support Vector machines, K-nearest neighbor and Discriminant linear regression models. These results offer a solid foundation for conducting research on reliable prognostic estimations and analysis of contagious disease emergence intensity and spread.
Cite This Paper
Joseph Isabona, Divine O. Ojuh," Machine Learning Based on Kernel Function Controlled Gaussian Process Regression Method for In-depth Extrapolative Analysis of Covid-19 Daily Cases Drift Rates ", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.7, No.2, pp. 14-23, 2021. DOI: 10.5815/ijmsc.2021.02.02
Reference
[1]"First Case of CORONA Virus Disease Confirmed in Nigeria". Nigeria Centre for Disease Control. 28 February 2020. https://ncdc.gov.ng/news/227/first-case-of-corona-virus-disease-confirmed-in-nigeria, Retrieved 22 May, 2020.
[2]Maclean, Ruth; Dahir, Abdi Latif (28 February 2020). "Nigeria Responds to First Coronavirus Case in Sub-Saharan Africa". The New York Times. Retrieved 22 May, 2020.
[3]Isabona, J and Konyeha. C.C (2013) ‘‘Urban Area Path loss Propagation Prediction and Optimisation Using Hata Model at 800MHz’’, IOSR Journal of Applied Physics (IOSR-JAP), Vol. 3, Issue 4, pp.8-18.
[4]Ebhota, V.C, Isabona, J, and Srivastava, V.M. Environment‑Adaptation Based Hybrid Neural Network Predictor for Signal Propagation Loss Prediction in Cluttered and Open Urban Microcells, Wireless Personal Communications, vol.104:935-948, 2019. DOI 10.1007/s11277-018-6061-2 pp. 1-16, 2019
[5]Liu, L., Luan, R. S., Yin, F., Zhu, X. P. & Lu, Q. Predicting the incidence of hand, foot and mouth disease in Sichuan province, China using the ARIMA model. Epidemiology and Infection 144, 144–151, https://doi.org/10.1017/s0950268815001144 (2016).
[6]Benvenuto, D; Giovanetti, M; Vassallo, L; Angeletti, S and Ciccozzi, M. Application of the ARIMA model on the COVID-2019 epidemic dataset, Data in Brief, Vol. 29, pp.1-4, 2020, https://doi.org/10.1016/j.dib.2020.105340.
[7]Akhtar, M; Kraemer, M U. G. and Gardner, L. M. A dynamic neural network model for predicting risk of Zika in real time, BMC Medicine, pp. 1-16; 2017. https://doi.org/10.1186/s12916-019-1389-3
[8]Kuo P-J, Wu S-C, Chien P-C, Chang S-S, Rau C-S, Tai H-L, et al. Artificial neural network approach to predict surgical site infection after free-flap reconstruction in patients receiving surgery for head and neck cancer. Oncotarget 2018;9:13768e82. https://doi.org/10.18632/oncotarget.24468.
[9]Habibi Z, Ertiaei A, Nikdad MS, Mirmohseni AS, Afarideh M, Heidari V, et al. Predicting ventriculoperitoneal shunt infection in children with hydrocephalus using artificial neural network. Childs Nerv Syst 2016;32:2143e51.https://doi.org/10.1007/s00381-016-3248-2.
[10]Nidhi Arora, Jatinderkumar R. Saini,"Estimation and Approximation Using Neuro-Fuzzy Systems", International Journal of Intelligent Systems and Applications (IJISA), Vol.8, No.6, pp.9-18, 2016. DOI: 10.5815/ijisa.2016.06.02.
[11]Koushal Kumar, Gour Sundar Mitra Thakur, "Advanced Applications of Neural Networks and Artificial Intelligence: A Review", International Journal of Information Technology and Computer Science (IJITCS), vol.4, no.6, pp.57-68, 2012. DOI: 10.5815/ijitcs.2012.06.08
[12]Ryusuke Hata, M. A. H. Akhand, Md. Monirul Islam, Kazuyuki Murase, "Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.5, pp.1-13, 2018. DOI: 10.5815/ijisa.2018.05.01
[13]Obanijesu Opeyemi, Emuoyibofarhe O. Justice, "Development of Neuro-fuzzy System for Early Prediction of Heart Attack", International Journal of Information Technology and Computer Science (IJITCS), vol.4, no.9, pp.22-28, 2012. DOI: 10.5815/ijitcs.2012.09.03
[14]K Srinivasa Rao, G. Lavanya Devi, N. Ramesh, "Air Quality Prediction in Visakhapatnam with LSTM based Recurrent Neural Networks", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.2, pp.18-24, 2019. DOI: 10.5815/ijisa.2019.02.03.
[15]Meghna Sharma, Manjeet Singh Tomer, " Predictive Analysis of RFID Supply Chain Path Using Long Short Term Memory (LSTM): Recurrent Neural Networks", International Journal of Wireless and Microwave Technologies (IJWMT), Vol.8, No.4, pp. 66-77, 2018.DOI: 10.5815/ijwmt.2018.04.05
[16]Isabona, J. and Osaigbovo, I. A. ‘‘Investigating Predictive Capabilities of RBFNN, MLPNN and GRNN Models for LTE Cellular Network Radio Signal Power Datasets’’, FUOYE Journal of Engineering and Technology, Vol. 4(1), pp. 155-159, 2019
[17]Ebhota, V.C, Isabona, J, and Srivastava, V.M. ‘‘Investigation and Comparison of Generalization Ability of Multi-Layer Perceptron and Radial Basis Function Artificial Neural Networks for Signal Power Loss Prediction’’, International Journal on Communications Antenna and Propagation, Vol. 9 (1), pp. 43-54, 2019.
[18]Ebhota, V.C, Isabona, J, and Srivastava, V.M. ‘‘Effect of Learning Rate on GRNN and MLP for the Prediction of Signal Power Loss in Microcell Sub-Urban Environment’’, International Journal on Communications Antenna and Propagation, Vol. 9 (1), pp. 36-45, 2019.
[19]Obahiagbon, K and Isabona, J. ‘‘Generalized Regression Neural Network: an Alternative Approach for Reliable Prognostic Analysis of Spatial Signal Power Loss in Cellular Broadband Networks’’, International Journal of Advanced Research in Physical Science, vol. 5(10): 35-42, 2018.
[20]Isabona, J, and Srivastava, V.M. ‘‘A Neural Network based Model for Signal Coverage Propagation Loss Prediction in Urban Radio Signal Propagation Loss in Urban Microcells, International Journal of Applied Engineering Research Vol. 11, No 22, pp. 11002-11008, 2016.
[21]Zhou, L. et al. A hybrid model for predicting the prevalence of schistosomiasis in humans of Qianjiang City, China. PloS One 9, e104875, https://doi.org/10.1371/journal.pone.0104875 (2014).
[22]Wang,Y; Xu,C; Zhang, S; Yang, L; Wang,Z; Zhu, Y and Yuan, J. Development and evaluation of a deep learning approach for modeling seasonality and trends in hand-foot-mouth disease incidence in mainland China. Sci Rep 9, 8046 (2019). https://doi.org/10.1038/s41598-019-44469-9
[23]Saqib M, Sha Y, Wang MD. Early prediction of sepsis in EMR records using traditional ML techniques and deep learning LSTM networks. Conf Proc IEEE Eng Med Biol Soc 2018;2018:4038e41. https://doi.org/10.1109/ EMBC.2018.8513254.
[24]Kam HJ, Kim HY. Learning representations for the early detection of sepsis with deep neural networks. Computing in Biological Medicine 2017;89:248e55.https:// doi.org/10.1016/j.compbiomed.2017.08.015.
[25]Ebhota, V.C, Isabona, J and Srivastava V.M., ‘‘Base line knowledge on propagation modelling and prediction techniques in wireless communication networks’’, Journal of Engineering and Applied Sciences (JEAS), Vol. 13 (4), pp. 235-240, 2018. https://doi.org/10.15866/irecap.v9i1.15329
[26]Isabona, J. (2020), Wavelet Generalized Regression Neural Network Approach for Robust Field Strength Prediction in Open and Shadow urban Microcells, Wireless Personal Communications, Vol. 114 (3), pp.3635–3653
[27]Isabona, J, and Srivastava, V. M. "Hybrid neural network approach for Predicting Signal Propagation Loss in Urban Microcells," 2016 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Agra, India, 2016, pp. 1-5.
[28]Ebhota, V.C., Isabona, J, and Srivastava, V.M. ‘‘Improved Adaptive Signal Power loss Prediction using Combined Vector Statistics based Smoothing and Neural Network approach’’, Progress in Electromagnetic Research C, Vol. 82, 155–169, 2018.
[29]Goyala, R; Chandraa, P and Singh, Y, Suitability of KNN Regression in the Development of Interaction Based Software Fault Prediction Models, IERI Procedia 6 (2014) 15 – 21.
[30]Nigeria Centre for Disease Control (NDDC), https://ncdc.gov.ng/ Accessed 17th May, 2020