In today's world, large group migration of applications to the fog computing is registered in the information technology world. The main issue in fog computing is providing enhanced quality of service (QoS). QoS management consists of various method used for allocating fog-user applications in the virtual environment and selecting suitable method for allocating virtual resources to physical resource. The resources allocation in effective manner in the fog environment is also a major problem in fog computing; it occurs when the infrastructure is build using lightweight computing devices. In this article, the allocation of task and placement of virtual machine problems is explained in the single fog computing environment. The experiment is done and the result shows that the proposed framework improves QoS in fog environment.
In recent years, finding the optimal solution for image segmentation has become more important in many applications. The whale optimization algorithm (WOA) is a metaheuristic optimization technique that has the advantage of achieving the global optimal solution while also being simple to implement and solving many real-time problems. If the complexity of the problem increases, the WOA may stick to local optima rather than global optima. This could be an issue in obtaining a better optimal solution. For this reason, this paper recommends a hybrid algorithm that is based on a mixture of the WOA and gray wolf optimization (GWO) for segmenting the brain sub regions, such as the gray matter (GM), white matter (WM), ventricle, corpus callosum (CC), and hippocampus (HC). This hybrid mixture consists of two steps, i.e., the WOA and GWO. The proposed method helps in diagnosing Alzheimer’s disease (AD) by segmenting the brain sub regions (SRs) by using a hybrid of the WOA and GWO (H-WOA-GWO, which is represented as HWGO). The segmented region was validated with different measures, and it shows better accuracy results of 92%. Following segmentation, the deep learning classifier was utilized to categorize normal and AD images. The combination of WOA and GWO yields an accuracy of 90%. As a result, it was discovered that the suggested method is a highly successful technique for identifying the ideal solution, and it is paired with a deep learning algorithm for classification.
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A 40 year old male patient, farmer by occupation, presented with complaints of abdominal pain on and off for the past 20 days, increased for the past 2 days over the left lumbar region, colicky pain, non-radiating, associated with burning micturition, no complaints of nausea, vomiting, constipation, loose stools, fever, loss of weight, loss of appetite, shortness of breath, cough with expectoration or chest pain. No co-morbidities, no history of smoking or alcoholism. 1 Systemic Examination Rs-bilateral normal vesicular breath sounds, no added sounds, CVS-S1S2 heard, no murmurs, per abdomen-mild lumbar tenderness present over the left side, CNS-no focal neurological deficit.
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