Considering task dependencies, the balancing of the Internet of Health Things (IoHT) scheduling is considered important to reduce the make span rate. In this paper, we developed a smart model approach for the best task schedule of Hybrid Moth Flame Optimization (HMFO) for cloud computing integrated in the IoHT environment over e-healthcare systems. The HMFO guarantees uniform resource assignment and enhanced quality of services (QoS). The model is trained with the Google cluster dataset such that it learns the instances of how a job is scheduled in cloud and the trained HMFO model is used to schedule the jobs in real time. The simulation is conducted on a CloudSim environment to test the scheduling efficacy of the model in hybrid cloud environment. The parameters used by this method for the performance assessment include the use of resources, response time, and energy utilization. In terms of response time, average run time, and lower costs, the hybrid HMFO approach has offered increased response rate with reduced cost and run time than other methods.

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Surface roughness of specimens is an important area of research since it influences the performance of machined parts. Meanwhile, employing a vision system to judge the roughness of the machined surface of specimens via captured images acquired from the specimen is an innovative and extensively used method. In this investigation, a vision system is used to capture the SEM images of the machined surface. The two-dimensional images of the machined surface of the Nimonic263 alloy are used to approximate the profile of the surface of specimens in finish turning. Surface roughness was detected in simulated images of specimens in a variety of machining conditions using the imaging technology. In this research work, the surface texture is extracted using a technique that combines 2D surface images and wavelet transform approach. The 2D wavelet transform has the capability to disintegrate a machined surface image into multiresolution depiction for several surface characteristics and can be utilized for surface evaluation. The difference in the histogram frequency of an illuminated region of interest (ROI) from turned surface images was analyzed to aid in the evaluation of surface roughness with an average prediction error of less than 3.2%.

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