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Öğe Are Italy and Iran really suffering from COVID-19 epidemic? A controversial study(VERDUCI PUBLISHER, VIA GREGORIO VII, ROME, 186-00165, ITALY, 2020) Al-Najjar, Hazem; Al-Rousan, NadiaThe number of global COVID-19 infected cases is increased rapidly to exceed 370 thousand. COVID-19 is transmitted between humans through direct contact and touching dirty surfaces. This paper aims to find the similarity between DNA sequences of COVID-19 in different countries, and to compare these sequences with three different diseases [HIV, Hand-Foot-Mouth disease (HFMD), and Cryptococcus]. The study used pairwise distance, maximum likelihood tree. and similarity between amino acid to find the results. The results showed that different three main types of viruses namely, COVID-19 are found. The virus in both Italy and Iran is not similar to COVID-19 in China and USA. While, two viruses were spread in Wuhan (before and after December 26, 2019). Besides Cryptococcus and HFMD are found as dominant diseases with Group 1 and Group 3, respectively. Authors claim that the current virus in Italy and Iran that killed thousands of people is not COVID-19 based on the available data.Öğe Assessment of predicting hourly global solar radiation in Jordan based on Rules, Trees, Meta, Lazy and Function prediction methods(ELSEVIER, RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS, 2021) Al-Rousan, Nadia; Al-Najjar, Hazem; Alomari, OsamaNowadays, predicting solar radiation is widely increased to maximize the efficiency of solar systems globally. Meteorological data from metrological stations is used to implement the intelligent prediction systems. Unfortunately, uncertainty in the used solar variables and the selected prediction models would increase the difficulties in using intelligent models to predict solar radiation. Several studies perfectly estimated solar radiation using only time and date variables. The main objective of this study is to review different prediction methods in predicting the solar radiation of Jordan. To achieve this target, five main methods including Rules, Trees, Meta, Lazy and Function Methods are selected, and then the most important and used algorithms in each method are selected to build a prediction model. The study shows that M5Rule, Random forest, Random committee, Instance Based Learning with Parameter K and multi-layer perceptron are the best algorithms in Rules, Trees, Meta, Lazy, and Function Methods respectively. Random forest algorithm performed better than other algorithms in predicting global solar radiation. The results of the analysis found that the accuracy of prediction depends on the used category, training algorithm and variables combinations.Öğe Can international market indices estimate TASI’s movements? The ARIMA model(Multidisciplinary Digital Publishing Institute (MDPI), 2020) Assous, Hamzeh F.; Al-Rousan, Nadia; Al-Najjar, Dania; Al-Najjar, HazemThis study investigates the effectiveness of six of the key international indices in estimating Saudi financial market (TADAWUL) index (TASI) movement. To investigate the relationship between TASI and other variables, six equations were built using two independent variables of time and international index, while TASI was the dependent variable. Linear, logarithmic, quadratic, cubic, power, and exponential equations were separately used to achieve the targeted results. The results reveal that power equation is the best equation for forecasting the TASI index with a low error rate and high determination coefficient. Additionally, findings of the AutoRegressive Integrated Moving Average (ARIMA) model represent the most important variables to use in order to build a prediction model that can estimate the TASI index. The ARIMA model (with Expert Modeler) coefficients are described as ARIMA (0,1,14). The results show that the SP500, NIKKEI, CAC40, and HSI indices are the most suitable variables for estimating TASI with an R2 and RMSE equal to 0.993 and 113, respectively. This relationship can be used on the previous day to estimate the opening price of TASI based on the closing prices of international indices. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.Öğe A classifier prediction model to predict the status of Coronavirus CoVID-19 patients in South Korea(VERDUCI PUBLISHER, VIA GREGORIO VII, ROME, 186-00165, ITALY, 2020) Al-Najjar, Hazem; Al-Rousan, NadiaOBJECTIVE: Coronavirus COVID-19 further transmitted to several countries globally. The status of the infected cases can be determined basing on the treatment process along with several other factors. This research aims to build a classifier prediction model to predict the status of recovered and death coronavirus CovID-19 patients in South Korea. MATERIALS AND METHODS: Artificial neural network principle is used to classify the collected data between February 20, 2020 and March 9, 2020. The proposed classifier used different seven variables, namely, country, infection reason, sex, group, confirmation date, birth year, and region. The most effective variables on recovered and fatal cases are analyzed based on the neural network model. RESULTS: The results found that the proposed predictive classifier efficiently predicted recovered and death cases. Besides, it is found that discovering the infection reason would increase the probability to recover the patient. This indicates that the virus might be controllable based on infection reasons. In addition, the earlier discovery of the disease affords better control and a higher probability of being recovered. CONCLUSIONS: Our recommendation is to use this model to predict the status of the patients globally.Öğe Correlation analysis and MLP/CMLP for optimum variables to predict orientation and tilt angles in intelligent solar tracking systems(WILEY-HINDAWI, ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON WIT 5HE, ENGLAND, 2021) Al-Rousan, Nadia; Isa, Nor Ashidi Mat; Desa, Mohd Khairunaz MatDifferent solar tracking variables have been employed to build intelligent solar tracking systems without considering the dominant and optimum ones. Thus, several low performance intelligent solar tracking systems have been designed and implemented due to the inappropriate combination of solar tracking variables and intelligent predictors to drive the solar trackers. This research aims to investigate and evaluate the most effective and dominant variables on dualand single-axis solar trackers and to find the appropriate combination of solar variables and intelligent predictors. The optimum variables will be found by using correlation results between different variables and both orientation and tilt angles. Then, to use the selected variables to develop different intelligent solar trackers. The results revealed that month, day, and time are the most effective variables for horizontal single-axis and dual-axis solar tracking systems. Using these variables in cascade multilayer perceptron (CMLP) and multilayer perceptron (MLP) produced high performance. These predictors could predict both orientation and tilt angles efficiently. It is found that day variable is very effective to increase the performance of solar trackers although day variable is neither correlated nor significant with both orientation and tilt angles. Linear regression predicted less than 70% of the given data in most cases, whereas nonlinear models could predict the optimum orientation and tilt angles. In single-axis tracker, month, day, and time variables achieved prediction rates of 96.85% and 96.83% for three hidden layers of MLP and CMLP, respectively, whereas the MSE are 0.0025 and 0.0008, respectively. In dual-axis solar tracker, MLP and CMLP predicted 96.68% and 97.98% respectively, with MSE of 0.0007 for both.Öğe The correlation between the spread of COVID-19 infections and weather variables in 30 Chinese provinces and the impact of Chinese government mitigation plans(VERDUCI PUBLISHER, VIA GREGORIO VII, ROME, 186-00165, ITALY, 2020) Al-Rousan, Nadia; Al-Najjar, HazemOn February 1, 2020, China announced a novel coronavirus CoVID-19 outbreak to the public. CoVID-19 was classified as an epidemic by the World Health Organization (WHO). Although the disease was discovered and concentrated in Hubei Province, China, it was exported to all of the other Chinese provinces and spread globally. As of this writing, all plans have failed to contain the novel coronavirus disease, and it has continued to spread to the rest of the world. This study aimed to explore and interpret the effect of environmental and metrological variables on the spread of coronavirus disease in 30 provinces in China, as well as to investigate the impact of new China regulations and plans to mitigate further spread of infections. This article forecasts the size of the disease spreading based on time series forecasting. The growing size of CoVID-19 in China for the next 210 days is estimated by predicting the expected confirmed and recovered cases. The results revealed that weather conditions largely influence the spread of coronavirus in most of the Chinese provinces. This study has determined that increasing temperature and short-wave radiation would positively increase the number of confirmed cases, mortality rate, and recovered cases. The findings of this study agree with the results of our previous study.Öğe CoVID-19 symptoms analysis of deceased and recovered cases using Chi-square test(VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY, 2020) Al-Najjar, Dana; Al-Najjar, Hazem; Al-Rousan, NadiaThis paper aims to show the relationship between COVID-19 symptoms and patients’ status including recovered and deceased cases. The study uses different CoVID-19 patients’ information from different countries, the dataset contains 13174 patients with 730 as recovered and 34 cases as deceased. The Chisquare test is adopted with asymptotic significance level to show the strength of each symptom on recovered and deceased cases independently. The study found that the recovered cases are associated with different symptoms based on the patient history, where the deceased cases showed that high fever is not responsible for increasing the number of deceased cases. In addition, the use of symptoms will not give evidence of the patients’ status, and therefore gender, age, reason of infection and patients’ province are more dominant in determining the status of patients.Öğe Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index(WILEY-HINDAWI, ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON WIT 5HE, ENGLAND, 2022) Al-Najjar, Dania; Al-Najjar, Hazem; Al-Rousan, Nadia; Assous, Hamzeh F.The air quality index (AQI) can be described using different pollutant indices. Many investigators study the effect of stock prices and air quality in the field to show if there is a relationship between changing the stock market and the concentration of various pollutants. This study aims to find a relationship between Saudi Tadawul All Share Index (TASI) and multiple pollutants, including particulate matter (PM10), ozone (O-3), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and AQI. Based on tree models, the relationship is created using linear regression and two prediction models, Chi-square Automatic Interaction Detection (CHAID), and CR-Tree. In order to achieve the target of this research, the TASI dataset relates to six pollutants using time; afterward, the new dataset is divided into three parts-test, validate, and train-after eliminating the outlier data. In order to test the performance of two prediction models, R-2 and various error functions are used. The linear regression model results found that PM10, NO2, CO, month, day, and year are significant, whereas O-3, SO2, and AQI indices are insignificant. The test dataset showed that R-2 scores are 0.995 and 0.986 for CR-Tree and CHAID, respectively, with RMSE values of 387 and 227 for CR-Tree and CHAID, respectively. The prediction models showed that the CHAID model performed better than CR-Tree because it used only three indices, namely, PM10, AQI, and O-3, with year and month. The results indicated an effect between changing TASI and the three pollutants, PM10, AQI, and O-3.Öğe Efficient single and dual axis solar tracking system controllers based on adaptive neural fuzzy inference system(King Saud University, 2020) Al-Rousan, Nadia; Mat Isa, Nor Ashidi; Mat Desa, Mohd KhairunazArtificial Intelligence is widely used in solar applications. Adaptive Neural Fuzzy Inference System (ANFIS) principle is one of the intelligent techniques that is sufficient to be used in control systems. This paper proposes two new efficient intelligent solar tracking control systems based on ANFIS principle. The aim of this paper is to design and implement efficient single and dual-axis solar tracking control systems that can increase the performance of solar trackers, predict the trajectory of the sun across the sky accurately, and minimize the error, therefore, maximize the energy output of solar tracking systems. Experimental data are used to train and test the proposed solar tracking controllers by using month, day and time as input variables to predict the optimum positions for solar tracking systems (tilt/orientation angles). The proposed ANFIS models have been evaluated to find its capability and robustness in tracking the optimum angles that gain the maximum solar radiation. It is found that the proposed controllers are optimum to control solar tracking systems with high prediction rate and the low error rate. Besides, the selected variables along with the selected architecture could successfully predict the optimum tilt and orientation angles. The proposed models provide superior results with five membership functions, and it could obtain high performance for both single-axis and dual-axis solar tracking systems. © 2020 The AuthorsÖğe Evaluation of the prediction of CoVID-19 recovered and unrecovered cases using symptoms and patient's meta data based on support vector machine, neural network, CHAID and QUEST Models(Verduci Publisher, 2021) Al-Najjar, D.; Al-Najjar, H.; Al-Rousan, NadiaOBJECTIVE: This paper aims to develop four prediction models for recovered and unrecovered cases using descriptive data of patients and symptoms of CoVID-19 patients. The developed prediction models aim to extract the important variables in predicting recovered cases by using the binary values for recovered cases. MATERIALS AND METHODS: The data were collected from different countries all over the world. The input of the prediction model contains 28 symptoms and four variables of the patient's information. Symptoms of COVID-19 include a high fever, low fever, sore throat, cough, and so on, where patient metadata includes Province, county, sex, and age. The dataset contains 1254 patients with 664 recovered cases. To develop prediction models, four models are used including neural network, support vector machine, CHAID, and QUEST models. To develop prediction models, the dataset is divided into train and test datasets with splitting ratios equal to 70%, and 30%, respectively. RESULTS: The results showed that the neural network model is the most effective model in developing COVID-19 prediction with the highest performance metrics using train and test datasets. The results found that recovered cases are associated with the place of the patients mainly, province of the patient. Besides the results showed that high fever is not strongly associated with recovered cases, where cough and low fever are strongly associated with recovered cases. In addition, the country, sex, and age of the patients have higher importance than other patient's symptoms in COVID-19 development. CONCLUSIONS: The results revealed that the prediction models of the recovered COVID-19 cases can be effectively predicted using patient characteristics and symptoms, besides the neural network model is the most effective model to create a COVID -19 prediction model. Finally, the research provides empirical evidence that recovered cases of COVID-19 are closely related to patients' provinces.Öğe Impact of COVID-19 pandemic virus on G8 countries' financial indices based on artificial neural network(EMERALD GROUP PUBLISHING LTD, HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND, 2021) Al-Najjar, Hazem; Al-Rousan, Nadia; Al-Najjar, Dania; Assous, Hamzeh F.; Al-Najjar, DanaPurpose – The COVID-19 pandemic virus has affected the largest economies around the world, especially Group 8 and Group 20. The increasing numbers of confirmed and deceased cases of the COVID-19 pandemic worldwide are causing instability in stock indices every day. These changes resulted in the G8 suffering major losses due to the spread of the pandemic. This paper aims to study the impact of COVID-19 events using country lockdown announcement on the most important stock indices in G8 by using seven lockdown variables. To find the impact of the COVID-19 virus on G8, a correlation analysis and an artificial neural network model are adopted. Design/methodology/approach – In this study, a Pearson correlation is used to study the strength of lockdown variables on international indices, where neural network is used to build a prediction model that can estimate the movement of stock markets independently. The neural network used two performance metrics including R2 and mean square error (MSE). Findings – The results of stock indices prediction showed that R2 values of all G8 are between 0.979 and 0.990, where MSE values are between 54 and 604. The results showed that the COVID-19 events had a strong negative impact on stock movement, with the lowest point on the March of all G8 indices. Besides, the US lockdown and interest rate changes are the most affected by the G8 stock trading, followed by Germany, France and the UK. Originality/value – The study has used artificial intelligent neural network to study the impact of US lockdown, decrease the interest rate in the USA and the announce of lockdown in different G8 countries.Öğe Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems(WILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJ, 2021) Al-Rousan, Nadia; Isa, Nor Ashidi Mat; Desa, Mohammad Khairunaz Mat; Al-Najjar, HazemIntelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. Several low performance intelligent solar tracking systems have been designed and implemented. Multilayer perceptron (MLP) is one of the common controllers that used to drive solar tracking systems. However, when the input data are complex for neural network, neural network would not well explain the relationship between these data. Thus, it performed worse than when the input data are simple. Using a premapping of relationship between samples of data as input to neural network along with the original input data could probably a strong guide to help neural network to reach the desired goal and predict the output variables faster and more accurate. It is found that using the output of logistic regression as input to neural network would faster the process of finding the predicted output by neural network. Thus, this study aims to propose new efficient and low complexity single and dual axis solar tracking systems by integrating supervised logistic regression (LR) and supervised MLP or cascade multilayer perceptron (CMLP). LR models are trained by using one of unsupervised clustering techniques (k?means, fuzzy c?means, and hierarchical clustering algorithms). The proposed models were used to predict both tilt and orientation angles by two different data sets (month, day, and time variables data set) and (month, day, time, Isc, Voc, and power radiation variables data sets). The results revealed that the proposed MLP/CMLP?LR systems are able to increase the prediction rate and decrease the mean square error rate as compared to conventional models in both single and dual axis solar tracking systems. The new developed intelligent systems achieved less number of overall connections, less number of neurons, and less time complexity. The finding suggests that the proposed intelligent solar tracking systems has a great potential to be applied for real?world applications (i.e., solar heating systems, solar lightening systems, factories, and solar powered ventilation.Öğe Is visiting Qom spread CoVID-19 epidemic in the Middle East?(VERDUCI PUBLISHER, VIA GREGORIO VII, ROME 186-00165, ITALY, 2020) Al-Rousan, Nadia; Al-Najjar, HazemThe CoVID-19 epidemic started in Wuhan, China and spread to 217 other countries around the world through direct contact with patients, goods transfer, animal transport, and touching unclean surfaces. In the Middle East, the first confirmed case in both Iran and UAE originated from China. A series of infections since those confirmed cases started in the Middle East originated from Qom, Iran, and other Shi’ite holy places. Thereafter, CoVID-19 has been transmitted to other countries in the Middle East. This report aims to trace all of the confirmed cases in the Middle East until March 6, 2020 and their further spread. This report proves that further transmission of CoVID-19 to the Middle East was because of human mobility, besides engaging in different Jewish and Shi’ite religious rites. This report suggests avoiding several religious rites, closing the borders of infected countries, and supporting the infected countries to prevent further transmission.Öğe Long-Term General Index Prediction Based on Feature Selection and Search Methods: Amman Stock Exchange Market(RUSSIAN ACAD SCIENCES, URAL BRANCH, INST ECONOMICS, UL MOSKOVSKAYA 29, EKATERINBURG 620014, RUSSIA, 2022) Al-Najjar, Dana; Al-Najjar, Hazem; Al-Rousan, NadiaStock markets are an essential backbone for the economy worldwide; their indices provide all interested parties with indicators regarding the performance of firms listed in the financial market due to tracking the daily transactions. This study aims to investigate factors that affect the stock exchange directly so that it simplifies building a prediction model for the exchange index in Jordan’s financial market. The study hypothesis assumes that some sub-sectors are most influential in creating the stock market prediction model. Therefore, this study applies four feature selection methods on 23 sub-sectors and Amman Stock Exchange Index (ASEI100) for the period 2008–2018. The top 10 attributes from each selection method are combined, and the frequency table is used to find the highly trusted attributes. Moreover, linear regression with ordinary least square regression is used to test the validity of the top factors that frequently occurred in the four methods and their effect on ASEI. The results found that there are six main sub-sectors directly affecting the general index in Jordan: Health Care Services, Mining and Extraction Industries, Textiles, Leather and Clothing, Real Estate, Financial Services and Transportation. These sectors can be utilised to predict the movements of the Amman Stock Exchange Index in Jordan. Also, the linear regression model output showed a statistically significant relationship between the six sub-sectors (independent variables) and ASEI (dependent variable). Investors can use this paper’s findings to signal the most important sectors in Jordan. Thus, it helps in taking investment decisions.Öğe Machine Learning to Develop Credit Card Customer Churn Prediction(MDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND, 2022) Al-Najjar, Dana; Al-Rousan, Nadia; Al-Najjar, HazemThe credit card customer churn rate is the percentage of a bank’s customers that stop using that bank’s services. Hence, developing a prediction model to predict the expected status for the customers will generate an early alert for banks to change the service for that customer or to offer them new services. This paper aims to develop credit card customer churn prediction by using a feature-selection method and five machine learning models. To select the independent variables, three models were used, including selection of all independent variables, two-step clustering and k-nearest neighbor, and feature selection. In addition, five machine learning prediction models were selected, including the Bayesian network, the C5 tree, the chi-square automatic interaction detection (CHAID) tree, the classification and regression (CR) tree, and a neural network. The analysis showed that all the machine learning models could predict the credit card customer churn model. In addition, the results showed that the C5 tree machine learning model performed the best in comparison with the three developed models. The results indicated that the top three variables needed in the development of the C5 tree customer churn prediction model were the total transaction count, the total revolving balance on the credit card, and the change in the transaction count. Finally, the results revealed that merging the multi-categorical variables into one variable improved the performance of the prediction models.Öğe Optimizing the performance of MLP and SVR predictors based on logical oring and experimental ranking equation(TAYLOR & FRANCIS LTD, 2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND, 2021) Al-Rousan, Nadia; Al-Najjar, HazemImproving conventional prediction systems is widely used to optimize the learning process, achieve higher performance, and avoid overfitting. This paper’s purpose is to propose a new predictor for solar tracking systems applications based on oring operator and ranking equation with a conventional predictor including Multi-Layer Perceptron (MLP) and Support Vector Machine Regression (SVR). The point of using oring and ranking equation is to create a new variable that stores the information of combined attributes. This process aims to increase the accuracy of predictors and increase the efficiency of intelligent solar tracking systems. The experiments used 6 different datasets for solar tracking systems. The results revealed that the proposed predictors performed better than conventional predictors. Using the proposed predictors has improved both Root Mean Square Error (RMSE) and Coefficient of Determination (R2 ). The developed MLP models showed lower RMSE and higher R2 compared to conventional MLP models. The improvement ranges for using MLP are from 1.0013 to 1.4614 degrees for RMSE, and from 1.0019 to 1.4984 times for R2 , while the improvement ranges using SVM are from 1.001 to 1.988 degrees for RMSE and from 1.000 to 2.385 times for R2.Öğe Ramadan effect and indices movement estimation: a case study from eight Arab countries(Emerald Group Publishing Ltd, 2023) Al-Najjar, Dania; Assous, Hamzeh F.; Al-Najjar, Hazem; Al-Rousan, NadiaPurpose This study aims to investigate the Ramadan effect anomaly on the stock markets' indices and estimate the movement of these indices in the light of the phenomenon. Design/methodology/approach Stock market indices are used as financial indicators to show the Ramadan effect. To validate this effect, eight Arab countries, which comprises Jordan, Saudi Arabia, Oman, Qatar, United Arab Emirates, Bahrain, Kuwait and Egypt, are adopted. A linear regression with R-2, error, F-value and p-value is considered to analyze and understand the effect of Ramadan on the aforementioned Arab countries. Findings Results found that Ramadan has a strong effect on estimating and predicting the performance of stock market indices in all studied Arab countries, except Kuwait. Results found that the majority of the Ramadan effect occurred after the second 10 days of Ramadan, where the direction of stock indices is opposite of Ramadan variables in all aforementioned cases. Originality/value This study is considered as an enrichment of the existing literature review with regard to the Ramadan effect. The study presents a new methodology that can be followed to improve the predictions of stock market indices by using a weight least square method with linear regression. This study presents the most affected periods of time that could decrease or increase the stock prices. Finally, the study proves the capability of the weight least square method in building a predictive model that takes the date into consideration in predicting stock market indices.
