Predicting vitamin D deficiency using optimized random forest classifier.

BACKGROUND: Vitamin D can be acquired from various dietary sources, but exposure to sunlight's ultraviolet rays can convert a natural compound called ergosterol present in the skin into vitamin D. AIM: The current study aimed to investigate vital parameters and use an optimized random forest (OptRF) classifier to understand better and predict the effect of environmental and nutritional factors of Vitamin D deficiency. METHODS: A predictive, cross-sectional, and correlational design was utilized in a study involving 350 male and female Tabuk citizens in Saudi Arabia. The Weka machine-learning tool was employed for comprehensive data analysis, with the OptRF algorithm being tailored through advanced feature selection methods and meticulous hyperparameter tuning. RESULTS: In addition to the OptRF classifier, a number of traditional machine learning techniques have been tested and compared on the dataset of vitamin D to analyze and build the predictive model for classifying vitamin D deficiency. In general, the OptRF-based predictive model can statistically describe data for determining significant features related to Vitamin D deficiency. OptRF demonstrated its ability to classify vitamin D deficiency cases with high accuracy 91.42 %. CONCLUSION: This study showed that Tabuk citizens are at high risk of vitamin D deficiency especially among females (gender predictor) with little regard to age, income, smoking, and sun exposure. In addition, exercise, less Vitamin D intake, and less intake of Calcium are also predictors of Vitamin D deficiency. Due to the link between Vitamin D Deficiency and major chronic illnesses, it is important to emphasize the importance of identifying risk factors and screening for Vitamin D Deficiency. It may be appropriate for nutritionists, nurses, and physicians to promote community awareness about strategies to improve dietary Vitamin D intake or consider recommending supplements.

Digitalization of learning in Saudi Arabia during the COVID-19 outbreak: A survey.

Following the outbreak of the novel coronavirus (COVID-19) in China in late December 2019, more than 217 countries became almost immediately infected in the resulting pandemic. Consequently, many of them decided to close their educational institutions as a way of preventing the spread of this virus. For many of them, though, the closure made them unable to deliver learning materials to students owing to their inability to provide the right technology for the purpose. To assist with the digitalizing of learning during this time, this study reviews the most common technologies used in the delivery of learning materials, with the experience of most infected countries being considered. Major challenges in online learning are discussed in this study as well. Further, Saudi Arabia was considered as a case study for the effectiveness of distance learning during the 2020 spring semester, where 300 undergraduate students were surveyed on their opinions of distance learning. The responses to the survey indicated that distance learning was effective in providing the required knowledge to the students during the outbreak of COVID-19. The findings showed that although the lack of interaction and poor internet connections were factors affecting comfortable and successful learning of physics and mathematics, 63% of students were satisfied with learning management systems, 75% of students found it easy to understand course materials, and 67% of students found it easy to understand assignments and could deal with them comfortably. The study findings can encourage educational institutions to digitalize their learning materials in the future.

Internship training in computer science: Exploring student satisfaction levels.

The requirement of employability in the job market prompted universities to conduct internship training as part of their study plans. There is a need to train students on important academic and professional skills related to the workplace with an IT component. This article describes a statistical study that measures satisfaction levels among students in the faculty of Information Technology and Computer Science in Jordan. The objective of this study is to explore factors that influence student satisfaction with regards to enrolling in an internship training program. The study was conducted to gather student perceptions, opinions, preferences and satisfaction levels related to the program. Data were collected via a mixed method survey (surveys and interviews) from student-respondents. The survey collects demographic and background information from students, including their perception of faculty performance in the training poised to prepare them for the job market. Findings from this study show that students expect internship training to improve their professional and personal skills as well as to increase their workplace-related satisfaction. It is concluded that improving the internship training is crucial among the students as it is expected to enrich their experiences, knowledge and skills in the personal and professional life. It is also expected to increase their level of confidence when it comes to exploring their future job opportunities in the Jordanian market.