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- 검색키워드
- 저자 : Nafie Faisal Mohammed (검색결과 4 건)
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Nafie, Faisal Mohammed,Hamed, Abdelmoneim Ali Mohamed International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.4
The main goal of higher education institutions is to present a high level of quality education to its students. This study uses data mining techniques to extract educational data from cumulative databases and used them to make the right decisions. This paper also aims to find the factors affecting students' academic performance in Majmaah University, KSA, during 2010 - 2017 period. The study utilized a sample of 6,158 students enrolled from two colleges, males and females. The results showed a high percentage of stumbling and dismissed between graduate and regular students where more than 62.5% failed to follow the plan. Only 2% of students scored distinction during their study of all graduated since their grade point average, secondary level, was statistically significant, where p<0.05. Dismissed percentage was higher among males. These results promoted some recommendations in which decision-makers could take them in considerations for better improvement of academic achievements: including of specialized programs to follow-up in regards to stumbling and failure. Utilization of different communication tools are needed to activate academic advisory for dismiss and dropout evaluation.
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Tracing Students Performance by Intervention of the Academic Advisor
Mohamed, Abdelmoneim Ali,Nafie, Faisal Mohammed International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.spc12
Data mining technique was used to track student's performance during years studding in college and determine the impact of GPA_SEC on the GPA student rates according to the current academic advising method used on student's status. The study utilized a sample of 5436 individuals were drawn from two colleges in Majmaah University, KSA during 2013-2018 period. The results showed that the student's completion status in terms of graduation, dropout, Stumbling or dismissed was classified according to the average grades of admission from secondary school GPA_SEC. The results show the effect of the current academic advising that most of students gain less grades comparing with GPA_SEC in addition that the higher GPA_SEC was the higher graduation, dropout and dismissed decreased when GPA_SEC was high.. Therefore, the study recommends tracking students academically to evaluate their results of each semester to find out the causes of the deficiencies and addressing them within the departments.
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Tracing Students Performance by Intervention of the Academic Advisor
Mohamed, Abdelmoneim Ali,Nafie, Faisal Mohammed International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.12
Data mining technique was used to track student's performance during years studding in college and determine the impact of GPA_SEC on the GPA student rates according to the current academic advising method used on student's status. The study utilized a sample of 5436 individuals were drawn from two colleges in Majmaah University, KSA during 2013-2018 period. The results showed that the student's completion status in terms of graduation, dropout, Stumbling or dismissed was classified according to the average grades of admission from secondary school GPA_SEC. The results show the effect of the current academic advising that most of students gain less grades comparing with GPA_SEC in addition that the higher GPA_SEC was the higher graduation, dropout and dismissed decreased when GPA_SEC was high.. Therefore, the study recommends tracking students academically to evaluate their results of each semester to find out the causes of the deficiencies and addressing them within the departments.
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Hilal Anwer Mustafa,Alzahrani Jaber S.,Alsolai Hadeel,Negm Noha,Nafie Faisal Mohammed,Motwakel Abdelwahed,Yaseen Ishfaq,Hamza Manar Ahmed 한국컴퓨터산업협회 2023 Human-centric Computing and Information Sciences Vol.13 No.-
In recent times, numerous decision-making procedures are not only based on the decision-making of choices, but also public perceptions of possible solutions. In a multi-criteria-based decision-making system, user preferences have been deeply considered. Sentiment analysis, on either side, is similar to natural language processing dedicated to the creation of methods capable of assessing evaluations and determining their intensity. The main aim of this research is to make efficient decisions using social media tweets. The proposed method uses the SentiRank method and neutrosophic set theory to make decisions and rank the reviews. Novel multi-criteria-based neutrosophic theory is used in this research for decision-making. An assembled neutral vocabulary, and the adapted VADER, are used to create Neutro-VADER, a novel version. Every evaluation of a product feature is given a positive, neutral, or negative scores of sentiment by the Neutro-VADER. A unique idea at this level is to use the positive, neutral, and negative scores on emotion to represent reality, uncertainty, and falsehood participation levels of a neutrosophic number. The testing findings support the value of sentiment data through reviews in the ranking procedure. The performance metrics used in the systems are precision, recall, and F1 measures and accuracy for evaluating the aspect detection module. The system performs better in food, service, and pricing categories, whereas the anecdotes group gives bad results. F1 and accuracy level shows better results in the proposed system by using SentiRank and the neutrosophic set theory method.
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