Identifying and analyzing the most important factors in universities scientific output using neural network

Abstract

For achieving to ideal research system is necessary to have policy and plan. The main aims of this research are knowledge extracted from the existing data gathered by the country research system. This Knowledge is prerequisite for science and technology policy. Population of the present descriptive research includes Universities of the Ministry of Science, Research & Technology. Data is extracted from database of monitoring of science and technology system. Classification algorithms Multilayer Preceptor neural network and neural network-based radius are used for data analysis. This research results showed that among the 15 indicators studied in this research, full-time faculty members, the number of theses, research funding and number of scholarships have more importance than other indicators. Among the four factors studied, the human resource factor is in the first place and has most importance in the scholarly productions. The education factor, financial factor and Structural factor is in the second to fourth rate.

Keywords

• Scientific output
• University
• Neural network
• science policy
• scientometrics.