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Title page for etd-0202109-095013


URN etd-0202109-095013 Statistics This thesis had been viewed 1344 times. Download 14 times.
Author Wen-chin Cheng
Author's Email Address gary.jin@msa.hinet.net
Department Computer Science and Enginerring
Year 2008 Semester 1
Degree Master Type of Document Master's Thesis
Language zh-TW.Big5 Chinese Page Count 60
Title The Study of Using Artificial Neural Network to Inspect The Insurance of Insured Unit
Keyword
  • insurance
  • genetic algorithm
  • back-propagation neural network
  • Artificial intelligence
  • Artificial intelligence
  • back-propagation neural network
  • genetic algorithm
  • insurance
  • Abstract National Health Insurance that social insurance system that shared dangerously that one kind helps each other by oneself. For reach purpose this, must all people in accordance with participate in insurance to stipulate, pay insurance premium, in order to enjoy proper medical care. So how implement the whole people to receive and go through formalities of insuring by force, and take precautions against and stop the illegal thing happening, and expect the whole people health insurance managed continuously forever to set up system to National Health Insurance diligent goal.
    This research is mainly to use back-propagation neural network and genetic algorithm technology. From the insurance in the unusual materials, build and predict that declares the unusually categorized model in the insurance. Help to audit personnel and grasp the insurance to declare unusual of insured unit. Must carry out and explain the convoluted operation procedure covered to insure the unit to cause by the fact that audit personnel to judge by accident while reducing. The experimental result shows, utilize the methods proposed to promote the correct distinguish rate up to 94.24% higher than generally audits the correct rate that personnel differentiate 84.63% .
    Advisor Committee
  • Shang-lin Hsieh - advisor
  • Yo-ping Huang - advisor
  • Maw-sheng Horng - co-chair
  • Files indicate in-campus access only
    Date of Defense 2009-01-10 Date of Submission 2009-02-02


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