scholarly journals Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

2020 ◽  
Vol 9 (3) ◽  
pp. 674 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Hong Fan ◽  
Mohamed Abd El Aziz
Keyword(s):  
Relative Error ◽  
Computing Time ◽  
World Health ◽  
Coefficient Of Determination ◽  
Flower Pollination Algorithm ◽  
Percentage Error ◽  
Anfis Model ◽  
Local Optima ◽  
Inference System ◽  
Proposed Model

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

scholarly journals A hybrid model based on mBA-ANFIS for COVID 19 confirmed cases prediction and forecast

2020 ◽  
Author(s):  
Sohail Saif ◽  
Priya Das ◽  
Suparna Biswas
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Bees Algorithm ◽  
Percentage Error ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Hybrid Forecasting

Abstract In India, the first confirmed case of novel corona virus (COVID-19) was discovered on 30 January, 2020. The number of confirmed cases is increasing day by day and it crossed 21,53,010 on 09 August, 2020. In this paper a hybrid forecasting model has been proposed to determine the number of confirmed cases for upcoming 10 days based on the earlier confirmed cases found in India. The proposed modelis based on adaptive neuro-fuzzy inference system (ANFIS) and mutation based Bees Algorithm (mBA). ThemetaheuristicBees Algorithm (BA) has been modified applying 4 types of mutation and Mutation based Bees Algorithm (mBA) is applied to enhance the performance of ANFIS by optimizing its parameters. Proposed mBA-ANFIS model has been assessed using COVID-19 outbreak dataset for India and USAand the number of confirmed cases in next 10 days in Indiahas been forecasted. Proposed mBA-ANFIS model has been compared to standard ANFIS model as well as other hybrid models such as GA-ANFIS, DE-ANFIS, HS-ANFIS, TLBO-ANFIS, FF-ANFIS, PSO-ANFIS and BA-ANFIS. All these models have been implemented using Matlab 2015 with 10 iterations each. Experimental results showthat the proposed model has achieved better performance in terms of Root Mean squared error (RMSE), Mean Absolute Percentage Error (MAPE), Mean absolute error (MAE) and Normalized Root Mean Square Error (NRMSE).It has obtained RMSE of 1280.24, MAE of 685.68, MAPE of 6.24 and NRMSE of 0.000673 for India Data.Similarly, for USA the values are 4468.72, 3082.07, 6.1, 0.000952 for RMSE, MAE, MAPE, NRMSE respectively.

scholarly journals Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020

2020 ◽  
Vol 17 (11) ◽  
pp. 4076 ◽  
Author(s):  
Abdallah Alsayed ◽  
Hayder Sadir ◽  
Raja Kamil ◽  
Hasan Sari
Keyword(s):  
Infection Rate ◽  
Fuzzy Inference ◽  
Basic Reproductive Number ◽  
Coefficient Of Determination ◽  
Reproductive Number ◽  
Percentage Error ◽  
Health Providers ◽  
Anfis Model ◽  
Inference System ◽  
The Government

The coronavirus COVID-19 has recently started to spread rapidly in Malaysia. The number of total infected cases has increased to 3662 on 05 April 2020, leading to the country being placed under lockdown. As the main public concern is whether the current situation will continue for the next few months, this study aims to predict the epidemic peak using the Susceptible–Exposed–Infectious–Recovered (SEIR) model, with incorporation of the mortality cases. The infection rate was estimated using the Genetic Algorithm (GA), while the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to provide short-time forecasting of the number of infected cases. The results show that the estimated infection rate is 0.228 ± 0.013, while the basic reproductive number is 2.28 ± 0.13. The epidemic peak of COVID-19 in Malaysia could be reached on 26 July 2020, with an uncertain period of 30 days (12 July–11 August). Possible interventions by the government to reduce the infection rate by 25% over two or three months would delay the epidemic peak by 30 and 46 days, respectively. The forecasting results using the ANFIS model show a low Normalized Root Mean Square Error (NRMSE) of 0.041; a low Mean Absolute Percentage Error (MAPE) of 2.45%; and a high coefficient of determination (R2) of 0.9964. The results also show that an intervention has a great effect on delaying the epidemic peak and a longer intervention period would reduce the epidemic size at the peak. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

scholarly journals A Wavelet Based Solar Radiation Prediction in Nigeria Using Adaptive Neuro-Fuzzy Approach

2018 ◽  
Vol 12 (3) ◽  
pp. 907 ◽  
Author(s):  
Sani Salisu ◽  
Mohd Wazir Mustafa ◽  
Mamunu Mustapha
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Hybrid Approach ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

<p><span>In this study, a hybrid approach combining an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Wavelet Transform (WT) is examined for solar radiation prediction in Nigeria. Meteorological data obtained from NIMET Nigeria comprising of </span><span lang="EN-MY">monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours were used as inputs to the model and monthly mean solar radiation was used as the model output. The data used was divided into two for training and testing, with 70% used during the training phase and 30% during the testing phase. The hybrid model performance is assessed using three statistical evaluators, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of determination </span><span lang="EN-SG">(R<sup>2</sup>). According to the results obtained, a very accurate prediction was achieved by the WT- ANFIS model by improving the value of (R<sup>2</sup>) by at least 14% and RMSE by at least 78% when compared with other existing models. And a MAPE of 2% is recorded using the proposed approach. The obtained results prove the developed WT-ANFIS model as an efficient tool for solar radiation prediction.</span></p>

scholarly journals Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Radiation Data ◽  
Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

scholarly journals Marine Predators Algorithm for Forecasting Confirmed Cases of COVID-19 in Italy, USA, Iran and Korea

2020 ◽  
Vol 17 (10) ◽  
pp. 3520 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Hong Fan ◽  
Laith Abualigah ◽  
Mohamed Abd Elaziz
Keyword(s):  
Mean Squared Error ◽  
Fuzzy Inference ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Inference System ◽  
Infected People ◽  
Marine Predators ◽  
Forecasting Performance ◽  
The Usa

The current pandemic of the new coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), or COVID-19, has received wide attention by scholars and researchers. The vast increase in infected people is a significant challenge for each country and the international community in general. The prediction and forecasting of the number of infected people (so-called confirmed cases) is a critical issue that helps in understanding the fast spread of COVID-19. Therefore, in this article, we present an improved version of the ANFIS (adaptive neuro-fuzzy inference system) model to forecast the number of infected people in four countries, Italy, Iran, Korea, and the USA. The improved version of ANFIS is based on a new nature-inspired optimizer, called the marine predators algorithm (MPA). The MPA is utilized to optimize the ANFIS parameters, enhancing its forecasting performance. Official datasets of the four countries are used to evaluate the proposed MPA-ANFIS. Moreover, we compare MPA-ANFIS to several previous methods to evaluate its forecasting performance. Overall, the outcomes show that MPA-ANFIS outperforms all compared methods in almost all performance measures, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), and Coefficient of Determination( R 2 ). For instance, according to the results of the testing set, the R 2 of the proposed model is 96.48%, 98.59%, 98.74%, and 95.95% for Korea, Italy, Iran, and the USA, respectively. More so, the MAE is 60.31, 3951.94, 217.27, and 12,979, for Korea, Italy, Iran, and the USA, respectively.

scholarly journals Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature

Materials ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 1983
Author(s):  
Mahmood Ahmad ◽  
Ji-Lei Hu ◽  
Feezan Ahmad ◽  
Xiao-Wei Tang ◽  
Maaz Amjad ◽  
...  
Keyword(s):  
Compressive Strength ◽  
High Temperature ◽  
Supervised Learning ◽  
Mean Squared Error ◽  
Cement Content ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Learning Methods ◽  
Inference System ◽  
Compressive Strength Of Concrete

Supervised learning algorithms are a recent trend for the prediction of mechanical properties of concrete. This paper presents AdaBoost, random forest (RF), and decision tree (DT) models for predicting the compressive strength of concrete at high temperature, based on the experimental data of 207 tests. The cement content, water, fine and coarse aggregates, silica fume, nano silica, fly ash, super plasticizer, and temperature were used as inputs for the models’ development. The performance of the AdaBoost, RF, and DT models are assessed using statistical indices, including the coefficient of determination (R2), root mean squared error-observations standard deviation ratio (RSR), mean absolute percentage error, and relative root mean square error. The applications of the above-mentioned approach for predicting the compressive strength of concrete at high temperature are compared with each other, and also to the artificial neural network and adaptive neuro-fuzzy inference system models described in the literature, to demonstrate the suitability of using the supervised learning methods for modeling to predict the compressive strength at high temperature. The results indicated a strong correlation between experimental and predicted values, with R2 above 0.9 and RSR lower than 0.5 during the learning and testing phases for the AdaBoost model. Moreover, the cement content in the mix was revealed as the most sensitive parameter by sensitivity analysis.

Investigating Entry Capacity Models of Roundabouts under Heterogeneous Traffic Conditions

2018 ◽  
Vol 2672 (15) ◽  
pp. 35-43 ◽  
Author(s):  
Ashish Kumar Patnaik ◽  
Ankit Raj Ranjan ◽  
Prasanta Kumar Bhuyan
Keyword(s):  
Fuzzy Inference ◽  
Point Of View ◽  
Heterogeneous Traffic ◽  
Coefficient Of Determination ◽  
Efficiency Coefficient ◽  
Anfis Model ◽  
Inference System ◽  
Critical Gap ◽  
Traffic Conditions ◽  
Traffic Volumes

The primary objectives of this study are to develop two roundabout entry capacity models using a regression-based multiple non-linear regression model (MNLR) and artificial intelligence (AI)-based ANFIS (adaptive neuro-fuzzy inference system) model under heterogeneous traffic conditions. ANFIS is the latest technique in the field of AI that integrates both neural networks and fuzzy logic principles in a single framework. Required data have been collected from 27 roundabouts in eight states of India. To assess the significance of these models and select the best model among them, modified rank index is applied in this study. The coefficient of determination ( R2) and Nash–Sutcliffe model efficiency coefficient ‘ E’ values are found to be 0.92, 0.91 and 0.98, 0.98 for the MNLR and ANFIS model, respectively. The ANFIS model is found to be the best model in this study. However, from a practical point of view, the MNLR model is recommended for determining roundabout entry capacity under heterogeneous traffic conditions. Sensitivity analysis reports that critical gap is the prime variable and shares 18.43% for the development of roundabout entry capacity. As compared with the Girabase formula (France), Brilon wu formula (Germany), and HCM 2010 models, the proposed MNLR model is quite reliable under low to medium ranges of traffic volumes.

scholarly journals Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Tien-Thinh Le ◽  
Hieu Chi Phan
Keyword(s):  
Composite Structures ◽  
Ultimate Load ◽  
Coefficient Of Determination ◽  
Gradient Descent Method ◽  
Structural Members ◽  
Load Prediction ◽  
Anfis Model ◽  
Inference System ◽  
Average Performance ◽  
Output Response

The ultimate compressive load of concrete-filled steel tubular (CFST) structural members is recognized as one of the most important engineering parameters for the design of such composite structures. Therefore, this paper deals with the prediction of ultimate load of rectangular CFST structural members using the adaptive neurofuzzy inference system (ANFIS) surrogate model. To this end, compression test data on CFST members were extracted from the available literature, including: (i) the mechanical properties of the constituent materials (i.e., steel’s yield strength and concrete’s compressive strength) and (ii) the geometric parameters (i.e., column length, width and height of cross section, and steel tube thickness). The ultimate load is the output response of the problem. The ANFIS model was trained using a hybrid of the least-squares and backpropagation gradient descent method. Quality assessment criteria such as coefficient of determination (R2), root mean square error (RMSE), and slope of linear regression were used for error measurements. A 11-fold cross-validation technique was employed to evaluate the performance of the model. Results showed that for the training process, the average performance was as follows: R2, RMSE, and slope were 0.9861, 89.83 kN, and 0.9861, respectively. For the validating process, the average performance was as follows: R2, RMSE, and slope were 0.9637, 140.242 kN, and 0.9806, respectively. Therefore, the ANFIS model may be considered valid because it performs well in predicting ultimate load using the validated data. Moreover, partial dependence (PD) analysis was employed to interpret the “black-box” ANFIS model. It is observed that PD enabled us to locally track the influence of each input variable on the output response. Besides reliable prediction of ultimate load, ANFIS can also provide maps of ultimate load. Finally, the ANFIS model developed in this study was compared with other works in the literature, showing that the ANFIS model could improve the accuracy of ultimate load prediction, in comparison to previously published results.

Prediction of tensile modulus of PA-6 nanocomposites using adaptive neuro-fuzzy inference system learned by the shuffled frog leaping algorithm

e-Polymers ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 187-198 ◽  
Author(s):  
Maryam Shahriari-kahkeshi ◽  
Mehdi Moghri
Keyword(s):  
Process Parameters ◽  
Fuzzy Inference ◽  
Tensile Modulus ◽  
Generalization Capability ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Shuffled Frog Leaping ◽  
The Relationship

AbstractIn this work, PA-6 nanocomposites containing different amounts of nanoclay (NC) were prepared using a corotating twin-screw extruder. In practice, it is hard task to identify the relationship between the extrusion process parameters and the tensile modulus of PA-6 nanocomposites by performing several experiments. One approach to map the relationship between the process parameters and the tensile modulus of PA-6 nanocomposites is the use of a non-linear system identification tool called the adaptive-neuro fuzzy inference system (ANFIS). In this study, to achieve high modeling accuracy and generalization capability, an efficient shuffled frog leaping (SFL) algorithm is proposed to learn all the parameters of the network. A multi-input single-output (MISO) ANFIS model is constructed and learned to predict the tensile modulus of PA-6 nanocomposites. The ANFIS model is constructed, trained and tested based on a collection of experimental data sets. Acceptable agreement has been observed between the experimental results and the predicted results by the proposed model. The statistical quality of the proposed model is significant due to its good correlation coefficient R2 values >0.98 between predicted values and experimental ones during the training and testing phase. Also, comparison results indicate the superior performance of the proposed scheme over the conventional reported methods due to its high approximation accuracy and good generalization capability.

scholarly journals Study for Predicting Land Surface Temperature (LST) Using Landsat Data: A Comparison of Four Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Elhadi K. Mustafa ◽  
Yungang Co ◽  
Guoxiang Liu ◽  
Mosbeh R. Kaloop ◽  
Ashraf A. Beshr ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Soft Computing ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Multivariate Adaptive Regression Splines ◽  
Coefficient Of Determination ◽  
Anfis Model ◽  
Inference System ◽  
Beijing Area ◽  
Soft Computing Techniques

The soft computing models used for predicting land surface temperature (LST) changes are very useful to evaluate and forecast the rapidly changing climate of the world. In this study, four soft computing techniques, namely, multivariate adaptive regression splines (MARS), wavelet neural network (WNN), adaptive neurofuzzy inference system (ANFIS), and dynamic evolving neurofuzzy inference system (DENFIS), are applied and compared to find the best model that can be used to predict the LST changes of Beijing area. The topographic change is considered in this study to accurately predict LST; furthermore, Landsat 4/5 TM and Landsat 8OLI_TIRS images for four years (1995, 2004, 2010, and 2015) are used to study the LST changes of the research area. The four models are assessed using statistical analysis, coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) in the training and testing stages, and MARS is used to estimate the important variables that should be considered in the design models. The results show that the LST for the studied area increases by 0.28°C/year due to the urban changes in the study area. In addition, the topographic changes and previously recorded temperature changes have a significant influence on the LST prediction of the study area. Moreover, the results of the models show that the MARS, ANFIS, and DENFIS models can be used to predict the LST of the study area. The ANFIS model showed the highest performances in the training (R2 = 0.99, RMSE = 0.78°C, MAE = 0.55°C) and testing (R2 = 0.99, RMSE = 0.36°C, MAE = 0.16°C) stages; therefore, the ANFIS model can be used to predict the LST changes in the Beijing area. The predicted LST shows that the change in climate and urban area will affect the LST changes of the Beijing area in the future.

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