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The life of living beings from cell to society in the universe is controlled by complex processes to preserve life. Understanding the gene network and discovering interactions between genes in cells is an important goal in biological systems. Modeling the gene network is one of the important issues in signal processing at the gene level. After the development of microarray technology, it was possible to model this network using time series data. The main objective of this research is to model the gene network from microarray time-series data that uses Granger causality, and to improve Granger causality and to observe the vague nature of microarray data,The linear method in Granger causality is replaced by a fuzzy method which then was applied on artificial and the real HELA data.

Robots and automation techniques are used in many industries for a long period because of the economic advantages and efficiency. Though the railway has a long history compared to other transportation systems, it still lacks wide application of modern technologies such as robots and AI. Track maintenance using robotic technologies has gained some attraction from both infrastructure managers and researchers due to safety and cost benefits. A Railway Inspection and Repair System (RIRS) has been proposed using commercially available Unmanned Ground Vehicles (UGV) and an industrial manipulator for the railway track inspection and repair tasks. The use of a specially designed trolley enables the on-track and off-track navigation capability of RIRS. The infrastructure in railway is very diversified and unique in size, shape, and remoteness compared to other industries. This research investigates the unique challenges to the operation of RIRS imposed by the railway infrastructure.