Many applications in the real world include optimizing specific targets, such as cost minimization, energy conservation, climate, and maximizing production, efficiency, and sustainability. The optimization problem is strongly non-linear with multifunctional landscapes under several dynamic, non-linear constraints in some instances. It is challenging to address those issues. Also, with the increasing strength of modern computers, simplistic brute force methods are still inefficient and unwanted. Practical algorithms are also vital for these implementations whenever possible. Cloud computing has become an essential and popular emerging computing environment that supports on-demand services and provides internet-based services. Cloud computing allows a range of services and tools to be easily accessed from anywhere in the world. Since cloud computing has global access to its services, there will always be threats and challenges facing its servers and services, such as; task scheduling, security, energy efficiency, network load, and other challenges. In the research area, many algorithms have been addressed to solve these problems. This paper investigates relevant analysis and surveys on the above topics, threats, and outlooks. This paper offers an overview of nature-inspired algorithms, their applications, and valuation, emphasizing cloud computing problems. Many problems in science and engineering can be viewed as optimization problems with complex non-linear constraints. Highly nonlinear solutions typically need advanced optimization algorithms, and conventional algorithms can have difficulty addressing these issues. Because of its simplicity and usefulness, nature-inspired algorithms are currently being used. There are nevertheless some significant concerns with computing and swarming intelligence influenced by evolution.
The purpose of this study was the comparison of two methods used to calculate the Size Specific Dose Estimate (SSDE) from CT images. The ratio of the SSDE for the two methods for different scan regions was evaluated. The first method used the patient size to find the effective diameter (Deff) and the SSDE while the second method used the patient attenuation water-equivalent diameter (Dw). In this study, the SSDE was calculated using both methods for a retrospective analysis of 10 abdominal, 15 thoracic, and 6 head CT examinations of human adults. The CT scans were segmented automatically to find the body contours. Subsequently, the segmentation results were used to calculate the effective diameter and the effective water equivalent diameter to find the CTDI to SSDE conversion factors for every slice according to the AAPM reports 204 and 220. The total SSDE for a scan is calculated as the average value of all slices. The results of this study show that the ratio Deff/Dw is about 0.98 ±0.06 for abdominal scans, 0.92±0.05 for thorax scans and 1.23 ±0.35 for head scans. From these results, we can conclude that the ratios of Deff and Dw are reasonably constant for chest and abdomen examinations and could be used to determine Dw from Deff. However, due to larger variations of the amount of bone in head CT, in these examinations the difference between Deff and Dw can be more than 50%. Therefore, we recommend to use the water equivalent diameter Dw in reporting the SSDE.
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