A Design of 'Windows 7 Troubleshooting' Software Using Hybrid Intelligence Systems

International Journal of Engineering Research & Management Technology , Vol. 2 , Issue 2, March 2015

13 Pages Posted: 22 Aug 2015

Date Written: March 15, 2015

Abstract

This paper describes the integration of two Artificial intelligence technologies, Radial Basis Function networks with expert systems to construct a robust hybrid system. Constructing this proposed hybrid system software is to diagnose the error messages and give recommendations to repair the operating system (Windows 7) problems and troubleshoot the problems that can be repaired. The neural network has unique characteristics which it can complete the uncompleted data, the expert system can't deal with data that is incomplete, but using the neural network individually has some disadvantages which it can't gives explanations and recommendations to the problems. The expert system has the opposite characteristics of Neural Network which is the ability to explain and give recommendations by using the rules and the human expert in some conditions. Therefore, we have combined the two technologies. Software engineering process models is used for constructing the proposed software. The paper will explain the integration methods between the two technologies and which method is suitable to be used in the proposed hybrid system.

Keywords: Hybrid Intelligence System, Windows, Artificial Intelligent System

Suggested Citation

Jasim, Yaser and Thabit, Thabit, A Design of 'Windows 7 Troubleshooting' Software Using Hybrid Intelligence Systems (March 15, 2015). International Journal of Engineering Research & Management Technology , Vol. 2 , Issue 2, March 2015. Available at SSRN: https://ssrn.com/abstract=2648808

Yaser Jasim

Cihan University

Street 100M
Erbil, Kurdistan Region 0383-23
Iraq

Thabit Thabit (Contact Author)

Ninevah University ( email )

Al-Majmoaa St.
Mosul, Ninevah 41002
Iraq

Register to save articles to
your library

Register

Paper statistics

Downloads
25
Abstract Views
227
PlumX Metrics