Neural Network Modelling, Simulation and Prediction of Innovation Growth in United Arab Emirates (UAE)

Abstract

Neural network exhibits superior ability in mapping and finding input-output patterns from a priori data set. Neural network is a common data analytics tool for extracting knowledge and forecasting. In this paper, a General Regression Neural Network or GRNN is used in modeling and forecasting Global Innovation Output. Global Innovation Index (GII) provides a detailed analysis of underlying factors influencing year-on-year changes in rankings; identifies the strengths and weaknesses of each country's profile; computes and provides the national indicators based on the certain input and output indicators. This paper particularly focuses on Innovation Output of United Arab Emirates or UAE. General Regression Neural Networks is proposed using GII data for 125 countries for training and then optimized to compute the improvement on the innovation output indicator of UAE as a test case by individually and collectively using following inputs from Singapore: Regulatory environment, Tertiary education, Information and Communication Technologies, Market sophistication in Investment and Knowledge absorption. It is observed that the output indicator improves from a value of 29.6 to about 42 when Knowledge absorption input is considered individually whereas the output increases to over 45 when all five indicators are simultaneously improved.