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In the present communication we report a novel fuzzy based algorithm developed for transformer designing. The Fuzzy Logic Transformer Design Algorithm (FLTDA) incorporates the experience of human transformer designers and builders in terms of minimum of fuzzy rules. It easily accomodates the linguistic design concepts and linguistic values of transformer specifications. The FLTDA allows to use assumptions, approximations, estimations and guess-figures for specifications in the beginning of design-route and adjusts the parameters in iterations yielding optimum design results.As a first attempt towards the development of FLTDA only preliminary results have been worked out in trial designs. Comparison between conventional design method and fuzzy based method is made by working out the typical design problems.

Abstract: Now days, Big data applications are having most of the importance and space in industry and research area. Surveillance videos are a major contribution to unstructured big data. The main objective of this paper is to give brief about video analysis using deep learning techniques in order to detect suspicious activities. Our main focus is on applications of deep learning techniques in detection the count, no of involved persons and the activity going on in a crowd considering all conditions [9]. This video analysis helps us to achieve security. Security can be defined in different terms like identification of theft, detecting violence etc. Suspicious Human Activity Detection is simply the process of detection of unusual (abnormal)l human activities . For this we need to convert the video into frames and processing these frames helps us to analyze the persons and their activities. There are two modules in this system first one Object Detection Module and Second one is Activity Detection Module .Object detection module detects whether the object is present or not. After detecting the object the next module is going to check whether the activity is suspicious or not.