Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

Hamadneh, Nawaf N.

doi:10.4018/ijsir.2020070102

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…In this study, we used several types of accuracy criteria: error functions. There are another statistical criteria that can also use such as the Akaike information criterion (AIC, AICs), and the Bayesian information criterion (BIC) [31][32][33]. The MAPE criterion is one the error function that we have used, where the function is given as…”

Section: Accuracy Criteriamentioning

confidence: 99%

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

Alshammari¹,

Ismail²,

Hamadneh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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In this study, we proposed a new model to improve the accuracy of forecasting the stock market volatility pattern. The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange (Tadawul). The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations. The proposed forecasting model combines the best maximum overlapping discrete wavelet transform (MODWT) function (Bl14) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. The results show the model's ability to analyze stock market data, highlight important events that contain the most volatile data, and improve forecast accuracy. The results were compared from a number of mathematical models, which are the non-linear spectral model, autoregressive integrated moving average (ARIMA) model and EGARCH model. The performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error (MAPE), Mean absolute scaled error (MASE) and Root means squared error (RMSE).

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Section: Accuracy Criteriamentioning

confidence: 99%

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

Alshammari¹,

Ismail²,

Hamadneh³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…MLPNN is a feed-forward ANN with three layers (input layer, hidden layers, and output layer), as shown in Figure 2 [39][40][41][42]. In this study, we used a single hidden layer that contains ten hidden neurons and the hidden activation function is the sigmoid function, as determined by:…”

Section: Annmentioning

confidence: 99%

Using Artificial Intelligence to Predict Students’ Academic Performance in Blended Learning

et al. 2022

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University electronic learning (e-learning) has witnessed phenomenal growth, especially in 2020, due to the COVID-19 pandemic. This type of education is significant because it ensures that all students receive the required learning. The statistical evaluations are limited in providing good predictions of the university’s e-learning quality. That is forcing many universities to go to online and blended learning environments. This paper presents an approach of statistical analysis to identify the most common factors that affect the students’ performance and then use artificial neural networks (ANNs) to predict students’ performance within the blended learning environment of Saudi Electronic University (SEU). Accordingly, this dissertation generated a dataset from SEU’s Blackboard learning management system. The student’s performance can be tested using a set of factors: the studying (face-to-face or virtual), percentage of attending live lectures, midterm exam scores, and percentage of solved assessments. The results showed that the four factors are responsible for academic performance. After that, we proposed a new ANN model to predict the students’ performance depending on the four factors. Firefly Algorithm (FFA) was used for training the ANNs. The proposed model’s performance will be evaluated through different statistical tests, such as error functions, statistical hypothesis tests, and ANOVA tests.

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“…Multilayer perceptron neural network (MLPNN) is a feed-forward neural network with three types of layers (input layer, hidden layers, and output layer), as shown in Fig. 1 [25,26]. In this study, we have used one hidden layer with ten hidden neurons, and the hidden activation function (sigmoid function), that is defined in the following equation.…”

Section: Structurementioning

confidence: 99%

Artificial Neural Networks for Prediction of Covid-19 in Saudi Arabia

Hamadneh¹,

Khan²,

Ashraf³

et al. 2021

Computers, Materials &Amp; Continua

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In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation-Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN-PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.

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Dead Sea Water Levels Analysis Using Artificial Neural Networks and Firefly Algorithm

Cited by 7 publications

References 22 publications

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

Using Artificial Intelligence to Predict Students’ Academic Performance in Blended Learning

Artificial Neural Networks for Prediction of Covid-19 in Saudi Arabia

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