The revolution of Cloud Computing increases the opportunities to provide realistic and most sophisticated evaluation modules that reduce the management time and cost of network performance evaluation and failure prediction. In our research, This paper presents a cloud-based software system that utilizing the Amazon Elastic MapReducer (EMR) ensemble clustered instances for evaluating the collected network measurements to quantifies network performance and predicate its degradation in the long run. The extracted outcomes illustrate the efficiency of the proposed system.

When a page fault occurs, the operating system has to choose a page to remove from memory to make room for the page that has to be brought in. The page replacement is done by swapping the required pages from backup storage to main memory and vice-versa. When a new page needs memory for allocation, page replacement algorithms decide which pages to swap out of the memory. In this paper, a comparison and analysis of five replacement algorithms: First in First out (FIFO), Least Recently Used (LRU), Clock, Most Recently Used algorithm (MRU), and Optimal Page Replacement (OPT) are made.

Recently, Kalman filter(KF)-based algorithms of tracking had demonstrated to be effective, however, their efficiency is limited by fixed feature selections and the possibility of model drift. In the presented research, we offer a new adaptive feature selection-based tracking approach that maintains the KF’s excellent discriminating power. Depending on scores of confidence regarding features in every one of frames, the suggested approach might select (automatically)either SIFT feature or the colour feature for the tracking. With a use of KF, a response map related to the SIFT features and color features are retrieved first. The color features that distinguish the luminance from the color are extracted using the Lab color space. Second, the average peak-to-correlation energy is used for the determination of the confidence region and the target's possible location. Finally, a total of 3 criteria have been utilized in order to choose the appropriate feature for present frame in order to execute adaptive tracking. On OTB benchmark datasets, the experimental findings show that the suggested tracker performs better in comparison with other state-of-art techniques.

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Human crowd analysis has common utilizations from the urban engineering and traffic management to law enforcement. They all need a crowd for first being detected, and is the issue that has been dealt with in the present study. Considering an image, the algorithm that has been proposed in this paper performs a segmentation of that image to crowd and non-crowd areas. The fundamental concept is capturing two main characteristics of the crowd: (a) on a narrower scale, its main elements have to appear like humans (only weakly so, as a result of the low resolution, dressing variations, occlusion, and so on), whereas (b) on the wider scale, the crowd intrinsically includes elements of the redundant appearance. The proposed approach makes use of that through the utilization of underlying statistical framework which has been based on the quantized features of the SURF. The two previously mentioned characteristics of the crowds have been obtained through the resultant statistical model responses' feature vector, which describe the level of crowd-like appearances around the location of an image with the increase of the spatial level around it.