The analysis of human movement has attracted the attention of many scholars of various disciplines today. The purpose of such systems is to perceive human behavior from a sequence of video images. They monitor the population to find common properties among pedestrians on the scene. In video surveillance, the main purpose of detecting specific or malicious events is to help security personnel. Different methods have been used to detect human behavior from images. This paper has used an efficient computational algorithm for detecting anomalies in video images based on the combined approach of the differential evolution algorithm and cellular neural network. In this method, the input image's gray-level image is first generated. Because it may be possible to identify several large areas in the image after the threshold, the largest white area is selected as the target area. The images are then used to remove noise, smooth the image, and fade the morphology. The results showed that the proposed method has higher speed and accuracy than other methods. The advantage of the algorithm is that it has a runtime of three seconds on a home computer, and the average sensitivity criterion is 98.6% (97.2%).
Task scheduling poses a major challenge for cloud computing environments. Task scheduling ensures cost-effective task execution and improved resource utilization. It is classified as a NP-hard problem due to its nondeterministic polynomial time nature. This characteristic motivates researchers to employ meta-heuristic algorithms. The number of cloud users and computing capabilities is leading to increased concerns about energy consumption in cloud data centers. In order to leverage cloud resources in the most energy-efficient manner while delivering real-time services to users, a viable cloud task scheduling solution is necessary. This study proposes a new deadline-aware task scheduling algorithm for cloud environments based on the Firefly Optimization Algorithm (FOA). The suggested scheduling algorithm achieves a higher level of efficiency in multiple parameters, including execution time, waiting time, resource utilization, the percentage of missed tasks, power consumption, and makespan. According to simulation results, the proposed algorithm is more effective and superior to the CSO algorithm under HP2CN and NASA workload archives.
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