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  • 1
    Online Resource
    Online Resource
    Impact of COVID-19 pandemic virus on G8 countries’ financial indices based on artificial neural network
    Emerald ; 2021
    In:  Journal of Chinese Economic and Foreign Trade Studies Vol. 14, No. 1 ( 2021-03-16), p. 89-103
    Details
    In: Journal of Chinese Economic and Foreign Trade Studies, Emerald, Vol. 14, No. 1 ( 2021-03-16), p. 89-103
    Abstract: 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 R 2 and mean square error (MSE). Findings The results of stock indices prediction showed that R 2 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.
    Type of Medium: Online Resource
    ISSN: 1754-4408 , 1754-4408
    URL: Article
    DOI: 10.1108/JCEFTS-06-2020-0025
    Language: English
    Publisher: Emerald
    Publication Date: 2021
    detail.hit.zdb_id: 2421758-X
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  • 2
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    Developing Machine Learning Techniques to Investigate the Impact of Air Quality Indices on Tadawul Exchange Index
    Hindawi Limited ; 2022
    In:  Complexity Vol. 2022 ( 2022-10-6), p. 1-12
    Details
    In: Complexity, Hindawi Limited, Vol. 2022 ( 2022-10-6), p. 1-12
    Abstract: 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 (O3), 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, R2 and various error functions are used. The linear regression model results found that PM10, NO2, CO, month, day, and year are significant, whereas O3, SO2, and AQI indices are insignificant. The test dataset showed that R2 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 O3, with year and month. The results indicated an effect between changing TASI and the three pollutants, PM10, AQI, and O3.
    Type of Medium: Online Resource
    ISSN: 1099-0526 , 1076-2787
    URL: Article
    DOI: 10.1155/2022/4079524
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2004607-8
    SSG: 11
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  • 3
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    Can International Market Indices Estimate TASI’s Movements? The ARIMA Model
    Elsevier BV ; 2020
    In:  Journal of Open Innovation: Technology, Market, and Complexity Vol. 6, No. 2 ( 2020-06), p. 27-
    Details
    In: Journal of Open Innovation: Technology, Market, and Complexity, Elsevier BV, Vol. 6, No. 2 ( 2020-06), p. 27-
    Type of Medium: Online Resource
    ISSN: 2199-8531
    URL: Article
    DOI: 10.3390/joitmc6020027
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 2832108-X
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  • 4
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    Developing a Sustainable Machine Learning Model to Predict Crop Yield in the Gulf Countries
    MDPI AG ; 2023
    In:  Sustainability Vol. 15, No. 12 ( 2023-06-11), p. 9392-
    Details
    In: Sustainability, MDPI AG, Vol. 15, No. 12 ( 2023-06-11), p. 9392-
    Abstract: Crop yield prediction is one of the most challenging tasks in agriculture. It is considered to play an important role and be an essential step in decision-making processes. The goal of crop prediction is to establish food availability for the coming years, using different input variables associated with the crop yield domain. This paper aims to predict the yield of five of the Gulf countries’ crops: wheat, dates, watermelon, potatoes, and maize (corn). Five independent variables were used to develop a prediction model, namely year, rainfall, pesticide, temperature changes, and nitrogen (N) fertilizer; all these variables are calculated by year. Moreover, this research relied on one of the most widely used machine learning models in the field of crop yield prediction, which is the neural network model. The neural network model is used because it can predict complex relationships between independent and dependent variables. To evaluate the performance of the prediction models, different statistical evaluation metrics are adopted, including mean square error (MSE), root-mean-square error (RMSE), mean bias error (MBE), Pearson’s correlation coefficient, and the determination coefficient. The results showed that all Gulf countries are affected mainly by four independent variables: year, temperature changes, pesticides, and nitrogen (N) per year. Moreover, the average of the best crop yield prediction results for the Gulf countries showed that the RMSE and R2 are 0.114 and 0.93, respectively. This provides initial evidence regarding the capability of the neural network model in crop yield prediction.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su15129392
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2518383-7
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  • 5
    Online Resource
    Online Resource
    Ramadan effect and indices movement estimation: a case study from eight Arab countries
    Emerald ; 2023
    In:  Journal of Islamic Marketing Vol. 14, No. 8 ( 2023-07-14), p. 1989-2008
    Details
    In: Journal of Islamic Marketing, Emerald, Vol. 14, No. 8 ( 2023-07-14), p. 1989-2008
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1759-0833 , 1759-0833
    URL: Article
    DOI: 10.1108/JIMA-01-2022-0008
    Language: English
    Publisher: Emerald
    Publication Date: 2023
    detail.hit.zdb_id: 2553045-8
    SSG: 3,2
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  • 6
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    Assessing the Impact of Syrian Refugee Influx on the Jordanian Stock Exchange Market
    MDPI AG ; 2023
    In:  Risks Vol. 11, No. 7 ( 2023-06-21), p. 114-
    Details
    In: Risks, MDPI AG, Vol. 11, No. 7 ( 2023-06-21), p. 114-
    Abstract: The past decade has witnessed significant turmoil and political conflicts in several Middle Eastern countries, such as Egypt, Syria, and Libya, called the Arab Spring. These revolutions did not only affect the countries mentioned previously; their neighboring countries were also directly affected. This study explores the impact of the Syrian refugee influx on the stock exchange market of one of its neighboring countries, namely Jordan. The Syrian civil war represents a recent catastrophic event that has resulted in over three million refugees migrating to various countries worldwide. The main objective of this paper is to examine the effect of the Syrian war on Jordan’s stock exchange market. The study utilizes the stock exchange indices as indicators of the performance of the exchange market, including Financials, Services, Industries, and General indices as dependent variables, and seven dummy variables are defined as representatives of the main events occurring in the Syrian civil war during the period 2011–2018 as independent variables. Multiple statistical analysis techniques, including correlation coefficients, error functions, and stepwise regression, are employed to analyze the selected variables. The findings reveal an inverse influence of the Syrian war on Jordan’s stock market. These findings can potentially enhance the development of prediction models for stock indices in Jordan and other countries by incorporating relevant variables.
    Type of Medium: Online Resource
    ISSN: 2227-9091
    URL: Article
    DOI: 10.3390/risks11070114
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704357-5
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  • 7
    Online Resource
    Online Resource
    Machine Learning to Develop Credit Card Customer Churn Prediction
    MDPI AG ; 2022
    In:  Journal of Theoretical and Applied Electronic Commerce Research Vol. 17, No. 4 ( 2022-11-16), p. 1529-1542
    Details
    In: Journal of Theoretical and Applied Electronic Commerce Research, MDPI AG, Vol. 17, No. 4 ( 2022-11-16), p. 1529-1542
    Abstract: The 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.
    Type of Medium: Online Resource
    ISSN: 0718-1876
    URL: Article
    DOI: 10.3390/jtaer17040077
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2266832-9
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  • 8
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    The impact of Russo-Ukrainian war, COVID-19, and oil prices on global food security
    Elsevier BV ; 2024
    In:  Heliyon Vol. 10, No. 8 ( 2024-04), p. e29279-
    Details
    In: Heliyon, Elsevier BV, Vol. 10, No. 8 ( 2024-04), p. e29279-
    Type of Medium: Online Resource
    ISSN: 2405-8440
    URL: Article
    DOI: 10.1016/j.heliyon.2024.e29279
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2024
    detail.hit.zdb_id: 2835763-2
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  • 9
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    Pooled analysis of WHO Surgical Safety Checklist use and mortality after emergency laparotomy
    Oxford University Press (OUP) ; 2019
    In:  British Journal of Surgery Vol. 106, No. 2 ( 2019-01-08), p. e103-e112
    Details
    In: British Journal of Surgery, Oxford University Press (OUP), Vol. 106, No. 2 ( 2019-01-08), p. e103-e112
    Abstract: The World Health Organization (WHO) Surgical Safety Checklist has fostered safe practice for 10 years, yet its place in emergency surgery has not been assessed on a global scale. The aim of this study was to evaluate reported checklist use in emergency settings and examine the relationship with perioperative mortality in patients who had emergency laparotomy. Methods In two multinational cohort studies, adults undergoing emergency laparotomy were compared with those having elective gastrointestinal surgery. Relationships between reported checklist use and mortality were determined using multivariable logistic regression and bootstrapped simulation. Results Of 12 296 patients included from 76 countries, 4843 underwent emergency laparotomy. After adjusting for patient and disease factors, checklist use before emergency laparotomy was more common in countries with a high Human Development Index (HDI) (2455 of 2741, 89·6 per cent) compared with that in countries with a middle (753 of 1242, 60·6 per cent; odds ratio (OR) 0·17, 95 per cent c.i. 0·14 to 0·21, P & lt; 0·001) or low (363 of 860, 42·2 per cent; OR 0·08, 0·07 to 0·10, P & lt; 0·001) HDI. Checklist use was less common in elective surgery than for emergency laparotomy in high-HDI countries (risk difference −9·4 (95 per cent c.i. −11·9 to −6·9) per cent; P & lt; 0·001), but the relationship was reversed in low-HDI countries (+12·1 (+7·0 to +17·3) per cent; P & lt; 0·001). In multivariable models, checklist use was associated with a lower 30-day perioperative mortality (OR 0·60, 0·50 to 0·73; P & lt; 0·001). The greatest absolute benefit was seen for emergency surgery in low- and middle-HDI countries. Conclusion Checklist use in emergency laparotomy was associated with a significantly lower perioperative mortality rate. Checklist use in low-HDI countries was half that in high-HDI countries.
    Type of Medium: Online Resource
    ISSN: 0007-1323 , 1365-2168
    URL: Article
    DOI: 10.1002/bjs.11051
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 2006309-X
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  • 10
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    Exploring the cost-effectiveness of high versus low perioperative fraction of inspired oxygen in the prevention of surgical site infections among abdominal surgery patients in three low- and middle-income countries
    Elsevier BV ; 2023
    In:  BJA Open Vol. 7 ( 2023-09), p. 100207-
    Details
    In: BJA Open, Elsevier BV, Vol. 7 ( 2023-09), p. 100207-
    Type of Medium: Online Resource
    ISSN: 2772-6096
    URL: Article
    DOI: 10.1016/j.bjao.2023.100207
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 3133899-9
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