The impact of College English Test (CET) on graduates' salaries using data mining techniques

Author(s):  
Dong Chen ◽  
Atae Rezaei Aghdam ◽  
Mostafa Kamalpour ◽  
Alex Tze Hiang Sim
2019 ◽  
Author(s):  
Abo Taleb T. Al-Hameedi ◽  
Husam H. Alkinani ◽  
Shari Dunn-Norman ◽  
Ralph E. Flori ◽  
Mortadha T. Alsaba ◽  
...  

2019 ◽  
Author(s):  
Abo Taleb T. Al-Hameedi ◽  
Husam H. Alkinani ◽  
Shari Dunn-Norman ◽  
Ralph E. Flori ◽  
Mortadha T. Alsaba ◽  
...  

Leadership ◽  
2018 ◽  
Author(s):  
Waseem Ahmad ◽  
Muhammad Akhtaruzamman ◽  
Uswa Zahra ◽  
Chandan Ohri ◽  
Binu Ramakrishnan

2018 ◽  
Vol 5 (1) ◽  
pp. 45-50
Author(s):  
Md Ashaduzzaman ◽  
Shihabuzzaman ◽  
Md Hasanur Rahman Sagor ◽  
Md Mizanur Rahman ◽  
Ahmed Iqbal Pritom

With the improvement of information technology, presently educational institutions generally store and compile a huge volume of students’ data. This huge volume of data can be analyzed using different data mining techniques and extract hidden relation between students’ result with other academic attributes. The main objective of this paper is to evaluate the impact of different academic attributes on the students’ final result using data mining techniques. We used different data mining techniques to analyze students data collected from Green University of Bangladesh. We applied three well-known classification algorithms namely Decision Tree, Naïve Bayes, and SVM to develop a prediction model that can suggest probable grade by analyzing parameters like the midterm, attendance, assignment, presentation, class test, final, and CT marks. Our goal is to find out the key factors playing as a catalyst for getting good or bad CGPA. Through this research, the university authority will get the knowledge about key factors playing significant role in students’ result that will help them to take proper decisions to improve students’ grade that in turns will reduce students’ dropout. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 5(1), Dec 2018 P 45-50


Author(s):  
Sujata Mulik

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 


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