Traffic forecasting in Morocco using artificial neural networks

Slimani, Nadia; Slimani, Ilham; Sbiti, Nawal; Amghar, Mustapha

doi:10.1016/j.procs.2019.04.064

Cited by 28 publications

(15 citation statements)

References 6 publications

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“…is the sum from the summation function, is the value of , is the weight of the neuron , is the number of neurons in the input layer, b is the inclination value, has the value of 1 to . [15].According to the prior research, it can be concluded that the Multi-Layer Perceptron Neural Networks have been applied in various fields, for example, disease diagnosis [2,3,8,12], education [13], and traffic management [14,15]. The results of the application of the Multi-Layer Perceptron Neural Networks in different areas led to the development of models established on this technique, which could be further used for effective data classification or data estimation.…”

Section: The Multi-layer Perceptron Neural Networkmentioning

confidence: 99%

The Model Development for Forecasting the NS1Ag Antigen Examination Results of Dengue Fever Patients Using the Multi-Layer Perceptron Neural Network

2020

IJATCSE

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The objective of the article is to develop a model for forecasting the NS1Ag antigen examination results of dengue fever patients by using the Multi-Layer Perceptron Neural Network. The data used for developing the model was the results of the antigen type NS1Ag tests collected from a certain hospital's information system. The data dated from October 2018 -March2020 with the number of 1,916 records. The data featured with 11 attributes.The Resample and SMOTE techniques were applied to adjust the imbalance ofthe data by increasing the size of data from 100% to 200%. The effectivenessevaluation results of the model using the percentage split of 70:30 revealedthat the imbalance-adjusted model using the SMOTE method with the size of200% and the Multi-Layer Perceptron Neural Network technique provided theaccuracy of 93.54%. This provided higher accuracy compared to the modelusing the Multi-Layer Perceptron Neural Network solely. The results of thestudy show that the model could be used for forecasting the NS1Ag antigenexamination results of dengue fever patients. It can be applied as a tool tosupport doctors and nurses to diagnose the dengue fever accurately and filterpatient transfers to the laboratory appropriately.

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Section: The Multi-layer Perceptron Neural Networkmentioning

confidence: 99%

The Model Development for Forecasting the NS1Ag Antigen Examination Results of Dengue Fever Patients Using the Multi-Layer Perceptron Neural Network

2020

IJATCSE

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“…Due to the ability of ANN to model non-linear time series problems it has be found useful in a wide range of applications such as in energy systems ( Panapakidis and Dagoumas, 2016 ; Wang et al, 2016 ), finance ( Chen and Du, 2009 ), traffic ( Slimani et al, 2019 ) and even in waste management ( Oliveira et al, 2019 ; Solano et al, 2019 ). Its flexible computational framework allows the users to vary its topology such as numbers of layers and neurons in the layers and this has made it suitable for many time series prediction applications ( Çavu, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for predicting the physical composition of municipal solid waste: An assessment of the impact of seasonal variation

et al. 2021

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Sustainable planning of waste management is contingent on reliable data on waste characteristics and their variation across the seasons owing to the consequential environmental impact of such variation. Traditional waste characterization techniques in most developing countries are time-consuming and expensive; hence the need to address the issue from a modelling approach arises. In modelling the complexity within the system, a paradigm shift from the classical models to the intelligent models has been observed. The application of artificial intelligence models in waste management is gaining traction; however its application in predicting the physical composition of waste is still lacking. This study aims at investigating the optimal combinations of network architecture, training algorithm and activation functions that accurately predict the fraction of physical waste streams from meteorological parameters using artificial neural networks. The city of Johannesburg was used as a case study. Maximum temperature, minimum temperature, wind speed and humidity were used as input variables to predict the percentage composition of organic, paper, plastics and textile waste streams. Several sub-models were stimulated with combination of nine training algorithms and four activation functions in each single hidden layer topology with a range of 1–15 neurons. Performance metrics used to evaluate the accuracy of the system are, root mean square error, mean absolute deviation, mean absolute percentage error and correlation coefficient (R). Optimal architectures in the order of input layer-number of neurons in the hidden layer-output layer for predicting organic, paper, plastics and textile waste were 4-10-1, 4-14-1, 4-5-1 and 4-8-1 with R-values of 0.916, 0.862, 0.834 and 0.826, respectively at the testing phase. The result of the study verifies that waste composition prediction can be done in a single hidden-layer satisfactorily.

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“…In a study on predicting the electricity demand in Thailand, the ANN model showed a more significant prediction [13]. By using the Multi-layer perceptron architecture ANN model machine learning approach, the traffic flow in Morocco country was predicted [14] with accuracy. Another study [15] predicted river runoff using ANN with accuracy.…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

Selvakumar¹,

Satpathi²,

Kumar³

et al. 2021

ujaf

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The main objective of this research paper is to build an appropriate mathematical model that helps in forecasting third party claim amount for different categories of vehicles based on the chosen characteristics of the data. In actuarial research, predicting the insurance claim amount for different vehicle categories is a challenging task, and minimal empirical research studies were done to forecast the claims. In the present study, the annual time series historical data were collected for a period of 34 years. We had built the machine learning predictive models to modeling the claim amount with different categories of vehicles effectively. In this context, we exhibited the feasibility of using a statistical machine learning approach such as Linear regression Model, the Exponential Smoothing Model, autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and hybrid ARIMA-ANN models to predict the various categories of vehicles claim amount. The data were analyzed, compared, and the empirical analysis showed that Artificial Neural Network is a better predictive model among the other time series models based on performance evaluation metrics RMSE and MAPE with lesser variance. Therefore, the machine learning approach for forecasting third party claim amounts will help the Insurance Companies in India to provide a better predictive model, which ensures better claims settlement and management for different categories of vehicles.

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Traffic forecasting in Morocco using artificial neural networks

Cited by 28 publications

References 6 publications

The Model Development for Forecasting the NS1Ag Antigen Examination Results of Dengue Fever Patients Using the Multi-Layer Perceptron Neural Network

The Model Development for Forecasting the NS1Ag Antigen Examination Results of Dengue Fever Patients Using the Multi-Layer Perceptron Neural Network

Application of artificial neural networks for predicting the physical composition of municipal solid waste: An assessment of the impact of seasonal variation

Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

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