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Special mathematical functions are an integral part of Fractional Calculus, one of them is the Airy function. But it’s a gruelling task for the processor as well as system that is constructed around the function when it comes to evaluating the special mathematical functions on an ordinary Central Processing Unit (CPU). The Parallel processing capabilities of a Graphics processing Unit (GPU) hence is used. In this paper GPU is used to get a speedup in time required, with respect to CPU time for evaluating the Airy function on its real domain. The objective of this paper is to provide a platform for computing the special functions which will accelerate the time required for obtaining the result and thus comparing the performance of numerical solution of Airy function using CPU and GPU.

The research presents a method for assessing public acceptability of items based on their brand by analysing the facial expression of a consumer who intends to purchase the product at a supermarket. In such circumstances, face expression detection is crucial in product evaluation. Emotions are conveyed through facial expressions. Sentimental analysis is a type of natural language processing that may be used for a variety of purposes. As a result, several techniques to classifying human emotional states have been proposed. The extraction of feature points via a cascade classifier is used to identify facial expressions, which minimizes the time complexity. The owner can view the feedback of the the reviewed product. This product ranking will assist the business owner in increasing product sales while also ensuring that the top products are available for the customers.

Since the CCTV cameras been introduced in this world, society has started to depend heavily on the usage of this technology for the high security purposes in most of the public and private areas. It is convenient to use these CCTV footages in courts as evidence and has been beneficial many times. But these footages are given priority and checked later when the incident has already taken place and that too after some period of time and not in real-time of happening. The screening of the multiple CCTV footages on a single monitor is done with very less efficiency as the ratio of number of CCTV footages to that of number of surveillance staff is very high. Also, the human unreliable supervision due to many reasons like tiredness from physical or mental effort, worker boredom, or discontinuous observation make the surveillance more inefficient. To address the issue and automatically detect the violent scenes using surveillance cameras and Embedded GPU in real-time we have developed this project for the benefit of our society. As the alert is generated in real-time, the security can take action immediately to prevent any further damage or mishappening in the crowd. Our primary objective is to automatically differentiate between violent activities and non-violent activities through CCTV surveillance cameras and automatically display the security alert on the screen as soon as any violent activity is captured and thus ensuring the safety of our society.

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