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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. [ABSTRACT FROM AUTHOR]

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 light‐weight 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. [ABSTRACT FROM AUTHOR]

Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a disease of wasting with airflow limitation, associated with a variety of systemic manifestations such as reduced Bone Mineral Density (BMD). There is a paucity of Indian studies on the effects of COPD on BMD. Aim: This study was conducted to estimate the prevalence of osteopenia and osteoporosis in COPD patients and the correlation between bone density and severity of COPD classified according to GOLD Global initiative for chronic Obstructive Lung Disease guidelines (GOLD). Materials and Methods: A prospective study of 60 patients diagnosed to have COPD, was conducted in the outpatient department of Respiratory Medicine, at a tertiary care hospital in Southern India, between September 2012 and September 2013. BMD was measured using ultrasound bone densitometer (ACHILLES GE HEALTH CARE). Patients with a T-score between -1 and -2.5 were considered to be osteopenic while patients with a T score less than -2.5 were considered to be osteoporotic (WHO criteria). Results: Overall, 40 (67%) patients had an abnormal bone mineral density. A total of 21 (35%) patients were osteoporotic while 19 (33%) were osteopenic. BMD levels correlated with severity of obstruction (p<0.001), smoking status (p=0.02), age (p=0.05) and number of pack years (p=0.001). Conclusion: Patients with COPD are at an increased risk for lower BMD and osteoporotic fractures and the risk appears to increase with disease severity. Further studies are required to assess whether routine BMD measurements in COPD patients is beneficial to diagnose osteoporosis and reduce morbidity. [ABSTRACT FROM AUTHOR]