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The effects of big data, artificial intelligence, and business intelligence on e-learning and business performance: Evidence from Jordanian telecommunication firms
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This study sought to investigate the impacts of big data, artificial intelligence (AI), and business intelligence (BI) on Firms' e-learning and business performance at Jordanian telecommunications industry. After the samples were checked, a total of 269 were collected. All of the information gathered throughout the investigation was analyzed using the PLS software. The results show a network of interconnections can improve both e-learning and corporate effectiveness. This research concluded that the integration of big data, AI, and BI has a positive impact on e-learning infrastructure development and organizational efficiency. The findings indicate that big data has a positive and direct impact on business performance, including Big Data External and Internal, Innovative Usage, Indexing, and Sources Accuracy. In addition, Artificial intelligence positively affects business performance, including Data Accuracy, Data Transparency, Data Speed, and Creative Thinking and Learning. Moreover, business intelligence has a direct and positive impact on business performance, including Data Warehouse, Data Mining, Business Process Management, and Competitive Intelligence. In addition, the findings indicate that e-learning which represents system quality, information quality, and self-efficacy has a positive relationship on enhancing business performance. Interestingly, the present findings are inconsistent with those of previous studies showing the variables of interest which have no effect on e-learning and business performance. Taken together, the findings of this study suggest that firms should begin to apply processes related with applying e-learning and developing business performance. The novelty of the present study lies in highlighting the key dimensions of big data, artificial intelligence, and business intelligence when it comes to enhancing e-learning and business performance at Jordanian telecommunications industry.

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Publication Date
Mon Jun 30 2025
Journal Name
Modern Sport
The Contribution Rate of Certain Cognitive and Visual Abilities to the Performance of Forehand and Backhand Skills in Tennis
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Publication Date
Sat Mar 02 2024
Journal Name
International Development Planning Review
EXTRAPOLATION OF THE MACHINE AND ITS EFFICIENCY IN DEVELOPING THE SKILL PERFORMANCE AND ACCURACY OF DRIBBLING IN YOUTH FOOTBALL
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Relying on modern work strategies, such as adopting scientific inductions, consolidates the information in the learner’s memory, develops the skill work of the football player, and raises the efficiency of their motor abilities. From this standpoint, the researcher, who is a teacher at the University of Baghdad, College of Physical Education and Sports Sciences, and follows most of the sports club teams in youth football, believes that there must be From extrapolations through the machine and employing it in the field to serve the skill aspect and benefit from scientific technology in development and making it a useful tool to serve the sports field in football, as the goal of the research was the efficiency of machine extrapolation in de

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection
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In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the License Plate (LP) detection is presented using canny edge detection

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Publication Date
Tue Dec 31 2024
Journal Name
Iraqi Geological Journal
Geomechanical Modeling and Artificial Neural Network Technique for Predicting Breakout Failure in Nasiriyah Oilfield
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Wellbore instability is one of the major issues observed throughout the drilling operation. Various wellbore instability issues may occur during drilling operations, including tight holes, borehole collapse, stuck pipe, and shale caving. Rock failure criteria are important in geomechanical analysis since they predict shear and tensile failures. A suitable failure criterion must match the rock failure, which a caliper log can detect to estimate the optimal mud weight. Lack of data makes certain wells' caliper logs unavailable. This makes it difficult to validate the performance of each failure criterion. This paper proposes an approach for predicting the breakout zones in the Nasiriyah oil field using an artificial neural network. It

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Publication Date
Tue Sep 01 2020
Journal Name
Baghdad Science Journal
Developing Arabic License Plate Recognition System Using Artificial Neural Network and Canny Edge Detection
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            In recent years, there has been expanding development in the vehicular part and the number of vehicles moving on the roads in all the sections of the country. Arabic vehicle number plate identification based on image processing is a dynamic area of this work; this technique is used for security purposes such as tracking of stolen cars and access control to restricted areas. The License Plate Recognition System (LPRS) exploits a digital camera to capture vehicle plate numbers is used as input to the proposed recognition system. Basically, the proposed system consists of three phases, vehicle license plate localization, character segmentation, and character recognition, the

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Publication Date
Wed Feb 01 2017
Journal Name
Journal Of Economics And Administrative Sciences
A comparison between the logistic regression model and Linear Discriminant analysis using Principal Component unemployment data for the province of Baghdad
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     The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.

     Was conducted to compare the two methods above and it became clear by comparing the  logistic regression model best of a Linear Discriminant  function written

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Oil spill classification based on satellite image using deep learning techniques
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 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

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Publication Date
Wed Feb 01 2023
Journal Name
Journal Of Engineering
An Empirical Investigation on Snort NIDS versus Supervised Machine Learning Classifiers
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With the vast usage of network services, Security became an important issue for all network types. Various techniques emerged to grant network security; among them is Network Intrusion Detection System (NIDS). Many extant NIDSs actively work against various intrusions, but there are still a number of performance issues including high false alarm rates, and numerous undetected attacks. To keep up with these attacks, some of the academic researchers turned towards machine learning (ML) techniques to create software that automatically predict intrusive and abnormal traffic, another approach is to utilize ML algorithms in enhancing Traditional NIDSs which is a more feasible solution since they are widely spread. To upgrade t

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Publication Date
Wed Jun 01 2022
Journal Name
Annals Of The Faculty Of Arts
Motivation and Second Language Acquisition: Motivational Orientations in Foreign Language Learning
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Numerous research studies have been conducted on why some learners acquire a second language more easily and quickly than others. Most of these studies have demonstrated that acquiring a second language does not depend only on learners’ cognitive ability or professional teaching strategies. The learning language process is more complicated than that. It is affected by crucial factors that are beyond the control of learners and teachers. These factors are known as sociolinguistic factors. These factors include culture, age, motivation, socio-economic status, and gender. This research paper mainly concentrates on the role of motivation in second language acquisition.

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Publication Date
Fri Apr 28 2023
Journal Name
Surgical Neurology International
Neurosurgery theater-based learning: Etiquette and preparation tips for medical students
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