Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

Author(s):  
Mustafa S. Abd ◽  
Suhad Faisal Behadili

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

Author(s):  
Syaidatus Syahira Ahmad Tarmizi ◽  
Sofianita Mutalib ◽  
Nurzeatul Hamimah Abdul Hamid ◽  
Shuzlina Abdul-Rahman ◽  
Ariff Md Ab Malik

2019 ◽  
Vol 125 ◽  
pp. 21002
Author(s):  
Mochamad Idris ◽  
Mustafid ◽  
Jatmiko Endro Suseno

Higher education has an important role to develop human resources in the economic growth and development of the country. One of specific way of evaluating and analyze data in education is to use data mining techniques. C4.5 algorithm as one of the data mining techniques that have good performance is very relevant used for data analysis tools. In this research using data on the performance of lecturers in college, there are 100 records with a 6 variable that affects individual factors in the productivity of lecturers including age, employment, attendance, certification, position, Education, and additional duties. In the end of the mining result, the forward chaining method is used to extract the rules that are generated by C4.5 algorithm. The input premises are examined by forwarding chaining to generate the prediction result.


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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