Intelligent Predictive Analysis Towards Different Applications

Abstract

Data prediction is a branch of the advanced analytics domain newlinethat is utilized for future event prediction. It investigates the historical newlineand the present data for making predictions regarding the future through newlinethe utilization of the approaches from artificial intelligence, machine newlinelearning, data mining, and statistics. Hence, predictive analytics is newlineimportant for handling high sensitive data. It exists as a challenging and newlinecomplex task in the dynamic and competitive business environment. The newlinemajor idea of this research work is to predict the data in distinct ways on newlinethe basis of three phases. The first phase uses the ANN-based newlineprecipitation prediction model. Here, the arithmetic, structure, and newlinebehavior of the BPN are described. The feed forward BPN is used in newlinedifferent applications like face apprehension, economic and weather newlineforetelling, character acceptance and etc. The second phase uses the newlineSVM classifier for the operation dataset to find the accurate tree based newlineon the span fashion based on the age group. It considers the support of newlinethe vertical associations in the prognosis databases to solve the re-doing newlineof the measurement. The third phase uses a predictive data mining newlinemodel for distinct applications using the optimized RNN with Fuzzy newlineclassifier. Here, the data prediction model is composed of four steps newlinesuch as, data acquisition, feature extraction, data normalization, and newlineprediction . The data acquisition collects the data from the UCI newlineix newlinerepository such as Bike Sharing Dataset, Carbon Nanotubes, Concrete newlineCompressive Strength, Electrical Grid Stability Simulated Data, and newlineSkill craft-1 Master Table . The feature extraction extracts the first newlineorder as well as the second order statistics. The data normalization newlinearranges the data within the specific limit. The normalized features are newlinegiven to the prediction step, in which the hidden neuron count of RNN newlineand the membership limit of Fuzzy regression model are optimized by newlinethe proposed WS-CSO. The performance is proved by comparing it with newlinevarious optimization