• Nutrition Research and Practice
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• v.10 no.4
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• pp.411-417
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• 2016

BACKGROUND/OBJECTIVES: Metabolic syndrome (MetS) is a set of interrelated metabolic risk factors that increase the risk of cardiovascular morbidity and mortality. Studies regarding the specificity and sensitivity of serum levels of leptin and uric acid as predictors of MetS are limited. The aim of this study was to evaluate the serum levels of leptin and uric acid in terms of their specificity and sensitivity as predictors of MetS in the studied Jordanian group. SUBJECTS/METHODS: In this cross sectional study, 630 adult subjects (308 men and 322 women) were recruited from the King Hussein Medical Center (Amman, Jordan). The diagnosis of MetS was made according to the 2005 International Diabetes Federation criteria. Receiver operating characteristic curves were used to determine the efficacy of serum levels of leptin and uric acid as predictors of MetS in the studied Jordanian group. RESULTS: Study results showed that for identification of subjects with MetS risk, area under the curve (AUC) for leptin was 0.721 and 0.683 in men and women, respectively. Serum uric acid levels in men showed no significant association with any MetS risk factors and no significant AUC, while uric acid AUC was 0.706 in women. CONCLUSION: Serum leptin levels can be useful biomarkers for evaluation of the risk of MetS independent of baseline obesity in both men and women. On the other hand, serum uric acid levels predicted the risk of MetS only in women.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.103-114
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• 2022

The Internet of Things (IoT) is one of the fastest technologies that are used in various applications and fields. The concept of IoT will not only be limited to the fields of scientific and technical life but will also gradually spread to become an essential part of our daily life and routine. Before, IoT was a complex term unknown to many, but soon it will become something common. IoT is a natural and indispensable routine in which smart devices and sensors are connected wirelessly or wired over the Internet to exchange and process data. With all the benefits and advantages offered by the IoT, it does not face many security and privacy challenges because the current traditional security protocols are not suitable for IoT technologies. In this paper, we presented a comprehensive survey of the latest studies from 2018 to 2021 related to the security of the IoT and the use of machine learning (ML) and deep learning and their applications in addressing security and privacy in the IoT. A description was initially presented, followed by a comprehensive overview of the IoT and its applications and the basic important safety requirements of confidentiality, integrity, and availability and its application in the IoT. Then we reviewed the attacks and challenges facing the IoT. We also focused on ML and its applications in addressing the security problem on the IoT.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.305-311
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• 2022

This research aimed to provide a profound description of the practices of social media users in the Kingdom of Saudi Arabia (KSA), specifically the users of Facebook^® (FB) and Snapchat^® (SC), the reasons for these practices, decisions made, and the people involved. Such research would be of significant help to designers and policymakers of social media applications in understanding user practices when using social media applications and the reasons for such practices in the KSA. This better comprehension would be of significant help in improving current applications and creating new ones. According to the data analysis, there was a clear preference for SC over FB in the KSA. Most participants with SC accounts were described as very active users, accessing their accounts at least once a day compared to FB users. The users were led by this high preference for SC to create new words derived from the name of the application and use them in daily life. We showed our experience of carrying out a study in which the main objective was to collect factual empirical data from participants about their daily usage of social media applications while considering the unique cultural settings in the KSA. Mixed quantitative and qualitative methods were used to triangulate the data, increasing its trustworthiness and validity. Multiple perspectives were obtained using various data collection methods. Therefore, conclusions would not be confounded with limitations of any particular methodology or with conditions of any collection rounds. This research would constitute a valuable guide for researchers intending to use methods with male and female informants from different cultures, preparing them for potential challenges and suggesting possible solutions.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.222-234
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• 2022

Background: Within the framework of the new Saudi Vision 2030, the education system is keen on developing Early Childhood (EC) curricula to meet the needs of children from diverse cultural and linguistic backgrounds, in addition to preparing teachers to be the main driving forces in this field. To achieve these strategic goals, the professional development of teachers has taken the lead in terms of their continuous professional achievements. Purpose: The recent study tended to explore the promotion of Cultural and Linguistic Competence (CLC) of teachers in preschool institutions in different sectors in the Kingdom of Saudi Arabia (KSA) include public, private and international. Method: In the current study, (n=300) of preschool female teachers, who had experience teaching children from diverse language and cultural backgrounds, participated voluntarily by filling out the exploratory questionnaire. It was designed on a five-point Likert scale. The credibility of the scale and the validity of the questionnaire were ascertained, and the content for which it was designed verified in terms of the purposes of the current investigation. Results: The results revealed that preschool female teachers in the private preschool settings have a higher level of CLC compared to those who were teaching in public and international preschools in KSA. In the private sector, preschool female teachers showed create abilities to provide culturally responsive environments for diverse students, applying various communication styles, and showing proper attitudes and values toward diversity. Implication: The current study provided key implications for policy makers regarding the promotion of CLC for all teachers, particularly preschool in government settings in KSA. It contributed to revealing the cultural awareness of preschool teachers' values and attitudes toward diversity.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.335-343
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• 2021

The study aimed to elucidate how to design a learning environment on the premise of micro-learning (ML) and investigate its impact on website designing skills and achievement motivation among secondary school students. Adopting the experimental approach, data were collected through an achievement test, a product evaluation form, and a test to gauge motivation for achievement. The sample was divided into two experimental groups. Results revealed statistically significant differences at 0.05≥α between the mean scores of the two groups that experienced ML, irrespective of the two modes of presenting the video in the pre-test and post-test, as for the test of websites design skills, product evaluation form, and achievement motivation test. Besides, there have been statistically significant differences at 0.05≥α between the mean scores of the first experimental group that had exposure to ML using the split-video presentation style and the scores of the second experimental group that underwent ML using continuous video presentation style in the post cognitive test of website design and management skills in favor of the group that had segmented-video-presentation ML. Another salient finding is the nonexistence of significant differences at 0.05≥α between the mean scores of the first experimental group that underwent segmented-video-presentation ML and the grades of the second experimental group that received ML with continuous video presentation style in the post-application of the product scorecard of websites designing skills and the motivation test. In light of these salient findings, the study recommended using ML in teaching computer courses at different educational stages in Saudi Arabia, training computer and information technology teachers to harness ML in their teaching and using ML in designing courses at all levels of education.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.151-160
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• 2023

The Hadith is the second source of Islamic jurisprudence after Qur'an. Both sources are indispensable for muslims to practice Islam. All Ahadith are collected and are written. But most books of Hadith contain Ahadith that can be weak or rejected. So, quite a long time, scholars of Hadith have defined laws, rules and principles of Hadith to know the correct Hadith (Sahih) from the fair (Hassen) and weak (Dhaif). Unfortunately, the application of these rules, laws and principles is done manually by the specialists or students until now. The work presented in this paper is part of the automatic treatment of Hadith, and more specifically, it aims to automatically process the chain of narrators (Hadith Isnad) to find its different components and affect for each component its own tag using a statistical method: the Hidden Markov Models (HMM). This method is a power abstraction for times series data and a robust tool for representing probability distributions over sequences of observations. In this paper, we describe an important tool in the Hadith isnad processing: A chunker with HMM. The role of this tool is to decompose the chain of narrators (Isnad) and determine the tag of each part of Isnad (POI). First, we have compiled a tagset containing 13 tags. Then, we have used these tags to manually conceive a corpus of 100 chains of narrators from "Sahih Alboukhari" and we have extracted a lexicon from this corpus. This lexicon is a set of XML documents based on HPSG features and it contains the information of 134 narrators. After that, we have designed and implemented an analyzer based on HMM that permit to assign for each part of Isnad its proper tag and for each narrator its features. The system was tested on 2661 not duplicated Isnad from "Sahih Alboukhari". The obtained result achieved F-scores of 93%.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.345-353
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• 2021

Cloud computing represent a new era of computing that's forms through the combination of service-oriented architecture (SOA), Internet and grid computing with virtualization technology. Virtualization is a concept through which every cloud is enable to provide on-demand services to the users. Most IT service provider adopt cloud based services for their users to meet the high demand of computation, as it is most flexible, reliable and scalable technology. Energy based performance tradeoff become the main challenge in cloud computing, as its acceptance and popularity increases day by day. Cloud data centers required a huge amount of power supply to the virtualization of servers for maintain on- demand high computing. High power demand increase the energy cost of service providers as well as it also harm the environment through the emission of CO₂. An optimization of cloud computing based on energy-performance tradeoff is required to obtain the balance between energy saving and QoS (quality of services) policies of cloud. A study about power usage of resources in cloud data centers based on workload assign to them, says that an idle server consume near about 50% of its peak utilization power [1]. Therefore, more number of underutilized servers in any cloud data center is responsible to reduce the energy performance tradeoff. To handle this issue, a lots of research proposed as energy efficient algorithms for minimize the consumption of energy and also maintain the SLA (service level agreement) at a satisfactory level. VM (virtual machine) consolidation is one such technique that ensured about the balance of energy based SLA. In the scope of this paper, we explore reinforcement with fuzzy logic (RFL) for VM consolidation to achieve energy based SLA. In this proposed RFL based active VM consolidation, the primary objective is to manage physical server (PS) nodes in order to avoid over-utilized and under-utilized, and to optimize the placement of VMs. A dynamic threshold (based on RFL) is proposed for over-utilized PS detection. For over-utilized PS, a VM selection policy based on fuzzy logic is proposed, which selects VM for migration to maintain the balance of SLA. Additionally, it incorporate VM placement policy through categorization of non-overutilized servers as- balanced, under-utilized and critical. CloudSim toolkit is used to simulate the proposed work on real-world work load traces of CoMon Project define by PlanetLab. Simulation results shows that the proposed policies is most energy efficient compared to others in terms of reduction in both electricity usage and SLA violation.