scholarly journals Predicting the Poverty Alleviation in the Province of Eastern Samar using Data Mining Techniques

2019 ◽  
Vol 8 (3) ◽  
pp. 7140-7145

Poverty has been a main concern for century in any part of the world. The abrupt increase of population in the country and the inevitable rise of the inflation rate due to the economic challenges and other factors, it is clearly manifested that poverty is a problem that needs to be addressed seriously. With the available various advanced-technology nowadays, this problem on poverty maybe reduced with the aide of Data Mining which is a part of Data Science. This paper focused on predicting the poverty alleviation using Data Mining techniques based from all available data from the Philippine Statistic’s Authority, National Economic Development Authority, and Department of Social Welfare and Development. The application of supervised learning in Data Mining specifically, NaiveBayes Algorithm, Decision Tree J48 Algorithm, and K- Nearest Neighbour Algorithm has been utilized for the prediction of poverty alleviation in the province of Eastern Samar. The results of this study unveil that among the core indicators in identifying poverty, it is the “Economic Sector” with the attribute “Income” is the most significant factor that affects poverty alleviation in the province.

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. 


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.


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