1) Developing Intelligent Search Engine Based on Neural Network and Support Vector Machine
Author’s Details:1Sozan Abdulla Mahmood, 2Zhyan Abdulwahab Hassan-Faculty of Science, Computer Department,Sulaimani University, Sulaimani, Iraq

Abstract: Nowadays the internet is the first delimited source of information. Everywhere and at any time you can get your demand of information from the internet. The best tool for doing this is only by using web search engine. So most of the scientific researches today is focusing on this direct by improving the web search engine. The aim of this work is to developing the web search engine by using two techniques of supervised machine learning: Back-propagation Neural Network (BPNN) and Support Vector Machine (SVM). Using the neural network is to help the crawler more accurate select pages by searching in a specific category of data instead searching whole the search engine database and using the support vector machine with Radial Basis Function (RBF) Kernel for classifying the result of searching as relevant or irrelevant then rank the relevant pages for more accurate result pages. The performance of system is evaluated by using different values of parameters for neural network and support vector machine.
Support vector machine, Neural Network , Search Engine,ranking

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