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  • 标题:Quality Assurance for Economy Classification Based on Data Mining Techniques Full Text
  • 本地全文:下载
  • 作者:Ahmed S. El Rawas ; Hamdi A. Mahmoud
  • 期刊名称:International Journal of Data Mining & Knowledge Management Process
  • 印刷版ISSN:2231-007X
  • 电子版ISSN:2230-9608
  • 出版年度:2018
  • 卷号:8
  • 期号:1
  • 页码:1
  • DOI:10.5121/ijdkp.2018.8101
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:Researchers in the quality assurance field used traditional techniques for increasing the organizationincome and take the most suitable decisions. Today they focus and search for a new intelligent techniquesin order to enhance the quality of their decisions. This paper based on applying the most robust trend incomputer science field which is data mining in the quality assurance field. The cases study which isdiscussed in this paper based on detecting and predicting the developed and developing countries based onthe indicators. This paper uses three different artificial intelligent techniques namely; Artificial NeuralNetwork (ANN), k-Nearest Neighbor (KNN), and Fuzzy k-Nearest Neighbor (FKNN). The main target ofthis paper is to merge between the last intelligent techniques applied in the computer science with thequality assurance approaches. The experimental result shows that proposed approaches in this paperachieved the highest accuracy score than the other comparative studies as indicates in the experimentalresult section.
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