An adaptive neuro-fuzzy interface system model for traffic classification and noise prediction

Sharma, Asheesh; Vijay, Ritesh; Bodhe, G. L.; Malik, Latesh

doi:10.1007/s00500-016-2444-z

Cited by 28 publications

(9 citation statements)

References 16 publications

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“…Thereafter, the final single results were obtained by taking average of the k results from the folds. The advantage of using k-fold validation is that the entire observations are utilized for both training and validation (Sharma et al 2018;Nourani et al 2019b). Figure 3 shows the k-fold cross validation applied, whilst Table 2 illustrates the cumulative daily-confirmed COVID-19 cases of the study countries and the number of observations used for training and validation.…”

Section: Model Validationmentioning

confidence: 99%

Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa

Ibrahim¹,

Tulay²,

Abdullahi

2022

Environ Sci Pollut Res

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Coronavirus disease 2019 (COVID-19) has produced a global pandemic, which has devastating effects on health, economy and social interactions. Despite the less contraction and spread of COVID-19 in Africa compared to some other continents in the world, Africa remains amongst the most vulnerable regions due to less technology and unequipped or poor health system. Recent happenings showed that COVID-19 may stay for years owing to the discoveries of new variants (such as Omicron) and new wave of infections in several countries. Therefore, accurate prediction of new cases is vital to make informed decisions and in evaluating the measures that should be implemented. Studies on COVID-19 prediction are limited in Africa despite the risks and dangers that the virus possessed. Hence, this study was performed to predict daily COVID-19 cases in 10 African countries spread across the north, south, east, west and central Africa considering countries with few and large number of daily COVID-19 cases. Machine learning (ML) models due to their nonlinearity and accurate prediction capabilities were employed for this purpose, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and conventional multiple linear regression (MLR) models. As any other natural process, the COVID-19 pandemic may contain both linear and nonlinear aspects. In such circumstances, neither nonlinear (ML) nor linear (MLR) models could be sufficient; hence, combining both ML and MLR models may produce better accuracy. Consequently, to improve the prediction efficiency of the ML models, novel ensemble approaches including ANN-E and SVM-E were employed. The advantage of using ensemble approaches is that they provide collective benefits of all the standalone models, thereby reducing their weaknesses and enhancing their prediction capabilities. The obtained results showed that ANFIS led to better prediction performance with MAD = 0.0106, MSE = 0.0003, RMSE = 0.0185 and R 2 = 0.9059 in the validation step. The results of the proposed ensemble approaches demonstrated very high improvements in predicting the COVID-19 pandemic in Africa with MAD = 0.0073, MSE = 0.0002, RMSE = 0.0155 and R 2 = 0.9616. The ANN-E improved the standalone models performance in the validation step up to 10%, 14%, 42%, 6%, 83%, 11%, 7%, 5%, 7% and 31% for Morocco, Sudan, Namibia, South Africa, Uganda, Rwanda, Nigeria, Senegal, Gabon and Cameroon, respectively. This study results offer a solid foundation in the application of ensemble approaches for predicting COVID-19 pandemic across all regions and countries in the world.

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Section: Model Validationmentioning

confidence: 99%

Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa

Ibrahim¹,

Tulay²,

Abdullahi

2022

Environ Sci Pollut Res

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“…1. Details about the nodes and training options of ANFIS can be found elsewhere (Jang 1993;Sugeno and Kang 1988;Thomas et al 2016;Sharma et al 2018;Sihag et al 2019;Sari et al 2019;Kamgar et al 2020). In the training phase of ANFIS models, the grid partitioning (ANFIS-GP) and subtractive clustering (ANFIS-SC) methods were utilized and the results were compared.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Estimation of ultimate bearing capacity of driven piles in c-φ soil using MLP-GWO and ANFIS-GWO models: a comparative study

et al. 2020

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A new forecast method is proposed in order to improve the estimation accuracy of the ultimate bearing capacity (UBC) of single driven piles. The performance of the adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) models have been boosted using gray wolf optimization (GWO). To train the models, data were used from 100 driven piles in c-u soil in different locations. The input parameters were pile area (m 2 ), pile length (m), flap number, average cohesion (kN/m 2 ) and friction angle (°), average soil specific weight (kN/m 2 ) and average pile-soil friction angle (°), and the output was UBC. The results showed that both the MLP and ANFIS methods had good abilities to predict the UBC of the piles; however, the MLP-GWO model with a topology of 7 9 10 9 10 9 1 provided better results. The performance indices of this model were a RMSE of 1.86 kN and R 2 of 0.991 for the test data. The validity of the MLP-GWO model was tested using a new experimental dataset, and the difference between actual and estimated UBC was very low (about 2%) and confirmed the high accuracy of the model. It was observed that the theoretical methods underestimated the UBC significantly, while the proposed model estimated the UBC with high accuracy. Sensitivity analysis confirmed that the pile area and length were the most effective factors for estimation of UBC.Keywords Piles Á ANFIS Á Gray wolf optimization Á Ultimate bearing capacity Á Sandy soil Á Clay soil Communicated by V. Loia.

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“…Another area of application of neural-fuzzy interface systems is for prediction and evaluation of the noise of traffic flows of vehicles in various urban environments based on the traffic flow, vehicle speed and honking [17].…”

Section: ____________________________________________________________mentioning

confidence: 99%

Identification of Sine, Squire, Triangle and Sawtooth Waveforms with Uniform White and Inverse F Noises by Adaptive Neuro - Fuzzy Interface System

Georgiev¹,

Balabanova²,

Kogias³

et al. 2018

JESTR

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In this paper the structures of Adaptive Neuro-fuzzy interface system (ANFIS) are studied for noise identification. The system's structures are analyzed for different types of membership functions applied for input variables with root mean square errors variation. Hybrid algorithm and back propagation algorithm are applied. The input data are obtained through system simulation based on LabVIEW system design platform and development environment. The choice of ANFIS structure is based on the training results and minimum RMSE for identification of the signals with uniform white and inverse F Noises. Therefore, "gbellmf" membership function for input data variables is chosen. The accuracy classification is obtained at 100 %.

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An adaptive neuro-fuzzy interface system model for traffic classification and noise prediction

Cited by 28 publications

References 16 publications

Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa

Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa

Estimation of ultimate bearing capacity of driven piles in c-φ soil using MLP-GWO and ANFIS-GWO models: a comparative study

Identification of Sine, Squire, Triangle and Sawtooth Waveforms with Uniform White and Inverse F Noises by Adaptive Neuro - Fuzzy Interface System

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