• English Language & Literature Teaching
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• v.11 no.2
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• pp.35-50
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• 2005

Thirty six EFL freshman students at the College of Languages and Translation, King Saud University, Riyadh, Saudi Arabia were given a dictation, a listening comprehension test and a decoding test. The purpose of the study was to find out whether EFL freshmen students' spelling ability correlates with their listening comprehension and decoding skills. Data analysis showed that the typical EFL freshman student misspelled 41.5% of the words on the dictation, gave 49.5% correct responses on the listening comprehension test, and 52% correct responses on the decoding test. The median and mean scores showed that the subjects' spelling, listening and decoding achievement is low, which implied that the subjects were having spelling, listening comprehension and decoding difficulties. The students' spelling errors and correct listening comprehension and decoding responses revealed strong correlations between spelling ability, listening comprehension and decoding skills. This means that good spelling ability in EFL is related to good listening comprehension and good decoding skills. The better the listening comprehension and decoding abilities, the fewer the spelling errors. When listening comprehension and decoding skills are poor, spelling ability is also poor. Recommendations for spelling, listening and decoding instruction are given.

• International Journal of Reliability and Applications
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• v.3 no.1
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• pp.17-23
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• 2002

The class of “new better than used of a specified age” is a large and practical class of life distributions. Its properties, applicability, and testing was discussed by Hollander, Park and Proschan (1986). Their test, while remaining the yardstick for this class, suffers from weak efficiency and weak power, especially for specified ages below the average age. Thus, it is beneficial to have an alternative testing procedure that would work better for early ages and still work well for later ages. This is exactly the subject of the current note. The test developed here is also simpler than that of Hollander, et. al. (1986).

• Journal of Power Electronics
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• v.15 no.4
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• pp.929-938
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• 2015

Maximum power point tracking (MPPT) and battery charging control are two important functions for a solar battery charger. The former improves utilization of the available solar energy, while the latter ensures a prolonged battery life. Nevertheless, complete implementation of both functions can be complex and costly, especially for low voltage application such as standalone street lamps. In this paper, the operation of a solar battery charger for standalone street light systems is investigated. Using only one voltage sensor, the solar charger is able to operate in both MPPT and constant voltage (CV) charging mode, hence providing high performance at a low cost. Using a lab prototype and a solar simulator, the operation of the charger system is demonstrated and its performance under varying irradiance is validated.

• International Journal of Reliability and Applications
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• v.2 no.2
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• pp.81-97
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• 2001

Lifetesting problems have been the subject of investigations for over three decades. Most suggested approaches are markedly different from those used in the related but wider goodness of fit problems. In the current investigation, it is demonstrated that a goodness of fit approach is possible in many lifetesting problems and that It results in simpler procedures that are asymptotically equivalent or better than standard ones. They may also have superior finite sample behavior. Several perennial classes are addressed here. The class of increasing failure rate (IFR) and the class of new better than used (NBU) are addressed first. In addition, we provide testing for a newer and practical class of new better than used in convex ordering (NBUC) due to Cao and Wang (1991). Other classes can be developed similarly and this point is illustrated with the classes of new better than used in expectation (NBUE) and harmonic new better than used in expectation (HNBUE).

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.247-253
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• 2021

The outbreak of the deadly virus COVID-19 is said to infect 17.3Cr people around the globe since 2019. This outbreak is continuously affecting a lot of new people till this day and, most of it is said to under control. However, vaccines introduced around the world can help mitigate the risk of the virus. Apart from medical professionals, prediction models are also said to combinedly help predict the risk of infection based on given datasets. This paper is based on publication of a machine learning approach using regression models to predict the output based on dataset which have indictors grouped based on active, tested, recovered and critical cases along with regions and cities covering most of it from Dubai. Hence, the active cases are tested based on the other indicators and other attributes. The coefficient of the determination (r²) is 0.96, which is considered promising. This model can be used as an frame work, among others, to predict the resources related to the dangerous outbreak.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.93-102
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• 2021

This paper aims to study the influence of environmental attitudes on the choice of non-professional investors. It highlights the role of environmental performance assurance on investment judgments. This choice is due to the motivation and importance that investors place on the disclosure of environmental information. The main purpose of the research is focused on the empirical approach justified by the use of a questionnaire addressed to 200 non-professional investors. The results show that attitudes towards the environment do not correlate with the importance that gives this category of investors to the environmental information.. Subsequently, the results prove that the disclosure of an environmental assurance report has a positive impact on investment judgments independently of their appreciation of the environmental information concerning that of financial order.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.214-222
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• 2021

Recently, the growth of e-commerce in Saudi Arabia has been exponential, bringing new remarkable challenges. A naive approach for product matching and categorization is needed to help consumers choose the right store to purchase a product. This paper presents a machine learning approach for product matching that combines deep learning techniques with standard artificial neural networks (ANNs). Existing methods focused on product matching, whereas our model compares products based on unstructured descriptions. We evaluated our electronics dataset model from three business-to-consumer (B2C) online stores by putting the match products collectively in one dataset. The performance evaluation based on k-mean classifier prediction from three real-world online stores demonstrates that the proposed algorithm outperforms the benchmarked approach by 80% on average F1-measure.

• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.85-92
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• 2021

Distance learning for students with intellectual disabilities can prove beneficial, particularly if adjusted to their educational characteristics and needs. This study seeks to identify the views of parents living in Saudi Arabia, regarding the opportunities offered by distance learning for students with intellectual disabilities, alongside their challenges during the Covid-19 pandemic. The research employed qualitative methods using semi-structured interviews with six parents of students with intellectual disabilities. The results revealed a number of both opportunities and challenges, including issues related to the family, in addition, the data highlighted difficulties related to the educational process (i.e. a lack of variety of educational methods) and technical issues related to access to the Internet and the insufficient computer skills of both teachers and students. The findings have several important implications for future practice, including the need for training workshops for parents concerning the online platform, as well as further research to determine students' perspectives of their experiences with distance learning.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.49-54
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• 2022

The objective of this study was to examine the function of the items on the Arabic version of the Social Networking Time Use Scale (SONTUS) using Differential Item Functioning (DIF) across groups of university students in SA (i.e., males and females at UQU). The findings of this study indicated that some of the items in the Arabic version of the SONTUS functioned differently across male and female students in SA. University faculty and administrators in Saudi Arabia as well as in the Arabic world can benefit from understanding students use of SNS.