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Video surveillance has undergone numerous changes in the past few years and several pieces of research have been carried out in this field. Object tracking is the significant task in such systems, and hence it is essential to review the standard approaches dealing with object detection, classification and tracking. This work proposes a novel classification technique for a detected object of a moving scene from a video dataset. Initially the dataset has been processed and prepared for data training based on neural networks. The data has been classified using the proposed enhanced deep belief based multilayered convolutional neural network (EBMCNN). The major focus is on deep learning applications involved in estimating the count, total persons involved and the activities in a crowd where all criteria are taken into consideration, thereby achieving security through video analysis. Identifying theft and the detection of violence are some security measures where video is converted to frames which are then processed to analyse the individuals along with their activities. The classification is performed through comparative analysis of a real‐time dataset. Experimental results show the accuracy 97%, precision 93.8%, recall 87.7% and F‐1 score 87.5%.

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Wireless Sensor Networking (WSN) is among the most recent technologies with uses ranging from medicine to the military. Nevertheless, WSNs are impervious to numerous types of cyber-attacks that could compromise the performance of the entire network, which could lead to fatal problems such as a routing attacks, denial-of-service attack, probe, etc. Key management protocols, secure routing, and authentication protocols cannot offer WSN protections for such kinds of attacks. The intrusion detection scheme is the way to solve the issue. This paper proposes an Enhanced simulated annealing based support vector machine algorithm for intrusion detection. Traditional features selection algorithm simulating annealing takes much time to run. So, to avoid this problem, we have introduced Enhanced simulated annealing. From the performance results, it can be seen that our proposed feature selection method provides better performance results than the existing method.

The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising R -squared and p value, with a right skewed distribution normal probability plot. The impact of lockdown in people’s lives in different sectors were also discussed in this paper.