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Now-a-days Global economy depends on the supply of energy and proper use of it. Energy is very compelling and critical issues all over the world. But the price of energy especially oil is increasing day by day. It is an obvious duty for all government throughout the world that estimation of cost of Oil for future development. The main purpose of this research is to develop a dynamic future and instant oil price prediction model for Business organization, Ministry of Finance, Ministry of Economic, Oil Company, Think Tank of the Government, Prime-Minister, World Bank Policy Maker, International Monetary Fund (IMF) etc. In this work, we first apply chi square test to separate factors such as demand of Oil and Gas, over population, Increasing rate Industry, completion of Development and etc. We then make a automate comparison of the production and export rate of the Oil and Gas in various countries among Middle East and Europe. The main purpose of applying it is feature selection to data. Degree of freedom is used to P-value (Probability value) for best predicators of dependent variable. After being separation of factors we have had examined the desired outcome using Bayesβ Networks (BN). The BN helps to determine the actual result based on our input factors. We should bear in mind that our activities for this work are dynamic and our system can inspect dynamically irrespective of any volume of dataset.
Mr.Linkon Chowdhury, Md.Sarwar kamal, Sonia Farhana Nimmy. 1970. "Artificial System to Compare Energy Status in the Context of Europe and Middle East". Global Journal of Computer Science and Technology GJCST Volume 12 (GJCST Volume 12 Issue 8).
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
v1.2
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Total Score: 143
Country: Bangladesh
Subject: Global Journal of Computer Science and Technology
Authors: Mr.Linkon Chowdhury, Md.Sarwar kamal, Sonia Farhana Nimmy (PhD/Dr. count: 0)
View Count (all-time): 340
Total Views (Real + Logic): 4114
Total Downloads (simulated): 180
Publish Date: 2012 05, Tue
Monthly Totals (Real + Logic):
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