• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.28-34
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• 2021

Internet Service Providers (ISPs) constantly endeavor to resolve network congestion, in order to provide fast and cheap services to the customers. This study suggests two models based on Markov chain, using three and four access attempts to complete the call. It involves a comparative study of four models to check the relationship between Internet Access sharing traffic, and the possibility of network jamming. The first model is a Markov chain, based on call-by-call attempt, whereas the second is based on two attempts. Models III&IV suggested by the authors are based on the assumption of three and four attempts. The assessment reveals that sometimes by increasing the number of attempts for the same operator, the chances for the customers to complete the call, is also increased due to blocking probabilities. Three and four attempts express the actual relationship between traffic sharing and blocking probability based on Markov using MATLAB tools with initial probability values. The study reflects shouting results compared to I&II models using one and two attempts. The success ratio of the first model is 84.5%, and that of the second is 90.6% to complete the call, whereas models using three and four attempts have 94.95% and 95.12% respectively to complete the call.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.98-112
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• 2021

This study aimed at measuring students' interaction in distance education through the electronic platform among intermediate school students, by identifying the level of students' interaction in distance education and differences between them, as well as its impact on their motivation to learn. To achieve the aim of the study, two scales were designed for this purpose and were applied to a sample consisting of (268) individuals. The results showed that the level of students' interaction through the e-learning platform was at a high level. The results also showed that there was no statistically significant difference between the mean scores of males and females in the scale of students' interaction through the e-learning platform. There was no statistically significant difference between them in their motivation for distance learning via the online platform. There were also no statistically significant differences related to the grade variable in the level of interaction through the electronic platform and in the motivation to learn, while there was a positive statistically significant effect of interaction through the electronic platform on students' motivation to learn.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.35-43
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• 2024

The development and integration of Enterprise Resource Planning (ERP) systems have consistently attracted attention from software engineering researchers. Many studies have examined the factors that influence successful ERP integration, while others have focused on introducing integration models that address issues and challenges that affect the successful integration of ERP. However, it is crucial to recognize that the key player in successful integration is the individual involved. This paper aims to investigate how individuals based on departmental attachments and experiences have viewed the factors that affected the success of ERP integration. A case study was conducted at one large organization namely Umm Al Qura University, Saudi Arabia. Five departments were involved namely: Financial management, purchasing management, warehouse management, human resources management, and the Deanship of Information Technology. The results of 78 participants were collected and analyzed. Furthermore, it was different how individuals from different departments involved in the ERP integration viewed the factors that affected the success of integration. In addition, it was noticed that individuals with different experiences have various views on the factors. Moreover, it was evident that departmental attachments and individual experience might play a role in the successful integration of ERP.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.343-351
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• 2022

A brain tumor forms when some tissue becomes old or damaged but does not die when it must, preventing new tissue from being born. Manually finding such masses in the brain by analyzing MRI images is challenging and time-consuming for experts. In this study, our main objective is to detect the brain's tumorous part, allowing rapid diagnosis to treat the primary disease instantly. With image processing techniques and deep learning prediction algorithms, our research makes a system capable of finding a tumor in MRI images of a brain automatically and accurately. Our tumor segmentation adopts the U-Net deep learning segmentation on the standard MICCAI BRATS 2018 dataset, which has MRI images with different modalities. The proposed approach was evaluated and achieved Dice Coefficients of 0.9795, 0.9855, 0.9793, and 0.9950 across several test datasets. These results show that the proposed system achieves excellent segmentation of tumors in MRIs using deep learning techniques such as the U-Net algorithm.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.25-31
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• 2023

Fog computing diversifies cloud computing by using edge devices to provide computing, data storage, communication, management, and control services. As it has a decentralised infrastructure that is capable of amalgamating with cloud computing as well as providing real-time data analysis, it is an emerging method of using multidisciplinary domains for a variety of applications; such as the IoT, Big Data, and smart cities. This present study provides an overview of the security and privacy concerns of fog computing. It also examines its fundamentals and architecture as well as the current trends, challenges, and potential methods of overcoming issues in fog computing.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.207-212
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• 2021

Clustering Unlabeled Spatial-datasets to convert them to Labeled Spatial-datasets is a challenging task specially for geographical information systems. In this research study we investigated the NYC Taxi Limousine Commission dataset and discover that all of the spatial-temporal trajectory are unlabeled Spatial-datasets, which is in this case it is not suitable for any data mining tasks, such as classification and regression. Therefore, it is necessary to convert unlabeled Spatial-datasets into labeled Spatial-datasets. In this research study we are going to use the Clustering Technique to do this task for all the Trajectory datasets. A key difficulty for applying machine learning classification algorithms for many applications is that they require a lot of labeled datasets. Labeling a Big-data in many cases is a costly process. In this paper, we show the effectiveness of utilizing a Clustering Technique for labeling spatial data that leads to a high-accuracy classifier.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.45-54
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• 2022

In this study, a multi-objective robust job-shop scheduling (JSS) model was developed. The model considered multi-jobs and multi-machines. The model also considered uncertain processing times for all tasks. Each job was assigned a specific due date and a tardiness penalty to be paid if the job was not delivered on time. If any job was completed early, holding expenses would be assigned. In addition, the model added idling penalties to accommodate the idling of machines while waiting for jobs. The problem assigned was to determine the optimal start times for each task that would minimize the expected penalties. A numerical problem was solved to minimize both the makespan and the total penalties, and a comparison was made between the results. Analysis of the results produced a prescription for optimizing penalties that is important to be accounted for in conjunction with uncertainties in the job-shop scheduling problem (JSSP).

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.338-347
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• 2022

The current study aimed to identify the reality of the transitional services provided to people with intellectual disabilities from the parent's point of view. The results indicated an average level, with an arithmetic mean (3.66) of the reality of transitional services provided to students with intellectual disabilities through the response of the study participants to the questionnaire consisting of (20) items. The dimension (social and societal skills) ranked first with an arithmetic average (4.03) with a high degree, through the response of the participants in the study to the items of the dimension consisting of (10) items. It was followed by the dimension (self-determination skills) with an arithmetic average of (3.29) to a medium degree, through the response of the participants in the study to the items of the dimension consisting of (10) items. The researchers recommend the necessity of joint planning by all relevant authorities, to solve the legal, societal, technical, and administrative problems and challenges that impede the provision of transitional services for students with intellectual disabilities.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.152-163
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• 2023

This research aimed at investigating Islamic Studies teachers' attitudes towards utilizing virtual learning environment in distance teaching among primary stage pupils. It also aimed at determining the statistical differences among variables due to sex, educational qualification, number of years of experience, and training sessions. This research adopted the descriptive approach. The sample consisted of male and female primary teachers of Islamic Studies (N=250) in governmental schools in Taif. The questionnaire was used as a main research tool. It included (20) items. Results showed that Islamic Studies teachers' attitudes towards utilizing virtual learning environment in distance teaching among primary stage pupils were ranked to a medium degree. There was a statistically significant difference among primary Islamic Studies teachers' attitudes due to sex variable. It was recommended to adopt more training sessions and seminars for adopting the idea of utilizing virtual learning environments among Islamic Studies teachers at boys' and girls' school in Mecca through emphasizing its significance and benefits in Teaching.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.281-291
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• 2021

The Islamic jurisprudential legal system represents an essential component of the Islamic religion, that governs many aspects of Muslims' daily lives. This creates many questions that require interpretations by qualified specialists, or Muftis according to the main sources of legislation in Islam. The Islamic jurisprudence is usually classified into branches, according to which the questions can be categorized and classified. Such categorization has many applications in automated question-answering systems, and in manual systems in routing the questions to a specialized Mufti to answer specific topics. In this work we tackle the problem of automatic categorisation of Islamic jurisprudential legal questions using deep learning techniques. In this paper, we build a hierarchical deep learning model that first extracts the question text features at two levels: word and sentence representation, followed by a text classifier that acts upon the question representation. To evaluate our model, we build and release the largest publicly available dataset of Islamic questions and answers, along with their topics, for 52 topic categories. We evaluate different state-of-the art deep learning models, both for word and sentence embeddings, comparing recurrent and transformer-based techniques, and performing extensive ablation studies to show the effect of each model choice. Our hierarchical model is based on pre-trained models, taking advantage of the recent advancement of transfer learning techniques, focused on Arabic language.