• International Journal of Computer Science & Network Security
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• 제23권11호
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• pp.8-20
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• 2023

The current study aims to determine digital transformation (organizational, technical, and human resources) requirements at Saudi universities from Umm Al-Qura University faculty members' perspectives. The researcher used a quantitative approach based on the descriptive analytical design. To answer the questions of the study, the researcher used the questionnaire as a data collection tool. The questionnaire was sent electronically to faculty members working in colleges and institutes affiliated with Umm Al-Qura University in Makkah Al-Mukarramah, Saudi Arabia. The questionnaire consisted of the three dimensions of digital transformation: organizational; technical; and human resources requirements. The results showed that requirements related to human resources came first with an average of 2.25 then the organizational requirements with an average of 1.95, and in the last, technical requirements came with an average of 1.64. In addition, some suggestions were given by the participants (faculty members) related to the mechanism that could contribute to implementing digital transformation at Saudi universities. Likewise, at the end of the study, the researcher has given some suggestions related to the implementation of digital transformation requirements at Saudi universities.

• International Journal of Computer Science & Network Security
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• 제24권7호
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• pp.207-213
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• 2024

This study investigated the perceived usability of the Blackboard learning management system (LMS) amongst students at Umm Al-Qura University. A quantitative approach was employed to explore the potential relationship between Blackboard usability and social media platform usage. Additionally, the study aimed to identify other factors influencing perceived usability. Data were collected through a three-section questionnaire distributed electronically to a sample of students (n=544). The findings, based on System Usability Scale (SUS) scores, revealed that the overall perceived usability of Blackboard resided near the midpoint of the scale, indicating an "acceptable" level. A potential negative correlation emerged between social media usage time and perceived Blackboard usability. Students who reported lower social media usage exhibited higher SUS scores. Training on Blackboard usage demonstrably exerted a positive influence on perceived usability. Gender was not identified as a statistically significant factor. An analysis of student support methods revealed that seeking help from a friend was the most prevalent approach, followed by search engines, university technical support, and social media platforms. The findings suggest that implementing strategies to improve Blackboard usability at Umm Al-Qura University could be achieved through readily accessible training materials and the exploration of alternative support channels.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.55-64
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• 2021

In the last few years, the massive development in wireless networks, high internet speeds and improvement in car manufacturing has shifted research focus to Vehicular Ad-HOC Networks (VANETs). Consequently, many related frameworks are explored, and it is found that security is the primary issue for VANETs. Despite that, a small number of research studies have taken into consideration the identification of performance standards and parameters. In this paper, VANET security frameworks are explored, studied and analysed which resulted in the identification of a list of performance evaluation parameters. These parameters are defined and categorized based on the nature of parameter (security or general context). These parameters are identified to be used by future researchers to evaluate their proposed VANET security frameworks. The implementation paradigms of security frameworks are also identified, which revealed that almost all research studies used simulation for implementation and testing. The simulators used in the simulation processes are also analysed. The results of this study showed that most of the surveyed studies used NS-2 simulator with a percentage of 54.4%. The type of scenario (urban, highway, rural) is also evaluated and it is found that 50% studies used highway urban scenario in simulation.

• International Journal of Computer Science & Network Security
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• 제23권6호
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• pp.202-206
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• 2023

Today, social media has become a vital tool. The world communicates and reaches the news and each other's opinions through social media accounts. Recently, considerable research has been done on analyzing social media due to its rich data content. At the same time, since the beginning of the COVID-19 pandemic, which has afflicted so many around the world, the search for a vaccine has been intense. There have been many studies analyzing people's feelings during a crisis. This study aims to understand people's opinions about available Coronavirus vaccines through a learning model that was developed for this purpose. The dataset was collected using Twitter's streaming Application Programming Interface (API) , then combined with another dataset that had already been collected. The final dataset was cleaned, then analyzed using Python. Polarity and subjectivity functions were used to obtain the results. The results showed that most people had positive opinions toward vaccines in general and toward the Pfizer one. Our study should help governments and decision-makers dispel people's fears and discover new symptoms linked to those listed by the World Health Organization.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• International Journal of Computer Science & Network Security
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• 제22권3호
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• pp.319-325
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• 2022

By using the Blackboard (BB) system in the education sector, the educational process for both academics and students is facilitated. Two data resources were used to evaluate the use of the BB system by students of Umm Al-Qura University: statistical reports issued by the university and an online questionnaire. A total of 989 students from all colleges and different programmes provided by the university responded to the questionnaire survey. According to our findings, most students did not use the BB before the pandemic. Therefore, the sudden conversion to the BB system required intensive training courses. After the data analysis, the relationship between the use of the BB system before the pandemic and the problems students faced during the lockdown was revealed. The most critical issues raised by the respondents were: (1) "The voice of the lecturer went on and off during BB collaborate class", (2) "internet connection of the lecturer went on and off during BB collaborate class" and (3) "High possibility of IT problems during exams".

• Journal of electromagnetic engineering and science
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• 제15권4호
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• pp.206-212
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• 2015

This paper introduces new wideband antenna-filter-antenna (AFA) uniform arrays that can be utilized as frequency selective surfaces (FSS) with low loss and sharp roll-off response, which are highly desirable characteristics for millimeter wave applications. The design adopts a simple 3-layer single polarization structure consisting of two patch antennas and a resonator. Both simulations and measurements are used to characterize the performances of the proposed design. Overall results show 18.5% 10-dB bandwidth. For the targeted band the insertion loss is less than 0.2 dB. Possible applications include quasi-optical amps, grid mixers and radomes for aircraft radar antennas.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.324-330
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• 2021

Agriculture is one of the essential needs of human life on planet Earth. It is the source of food and earnings for many individuals around the world. The economy of many countries is associated with the agriculture sector. Lots of diseases exist that attack various fruits and crops. Apple Tree Leaves also suffer different types of pathological conditions that affect their production. These pathological conditions include apple scab, cedar apple rust, or multiple diseases, etc. In this paper, an automatic detection framework based on deep learning is investigated for apple leaves disease classification. Different pre-trained models, VGG16, ResNetV2, InceptionV3, and MobileNetV2, are considered for transfer learning. A combination of parameters like learning rate, batch size, and optimizer is analyzed, and the best combination of ResNetV2 with Adam optimizer provided the best classification accuracy of 94%.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.165-174
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• 2021

This study aimed to ascertain whether electronic courses at the deanship of electronic learning and distance education at Umm Al-Qura University meet the quality standards developed by the Quality Matters (QM) organization. This endeavor adopted a mixed method of an explanatory sequential research design for an in-depth understanding of the topic under scrutiny. The sample of the study consisted of ten courses designed at the deanship and reviewed using an evaluation form. The results showed that the courses in focus did not meet the criteria of QM. Based on this finding, a semi-structured interview was designed to collect relevant data from the syllabus designers at the deanship. The interviews yielded information on the difficulties the course designers faced when designing QM-criteria-based courses. The results obtained from the interviews showed that the designers experienced administrative, technical, and faculty-member-related challenges that, when producing online courses, intercepted their way to achieving the QM standards. The study closed with some recommendations, the most important of which is a call for re-developing online courses in alignment with the well-recognized QM standards.