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.

With the recent expansion of social media in the form of social networks, online portals, and microblogs, users have generated a vast number of opinions, reviews, ratings, and feedback. Businesses, governments, and individuals benefit greatly from this information. While this information is intended to be informative, a large portion of it necessitates the use of text mining and sentiment analysis models. It is a matter of concern that reviews on social media lack text context semantics. A model for sentiment classification for customer reviews based on manifold dimensions and manifold modeling is presented to fully exploit the sentiment data provided in reviews and handle the issue of the absence of text context semantics. This paper uses a deep learning framework to model review texts using two dimensions of language texts and ideogrammatic icons and three levels of documents, sentences, and words for a text context semantic analysis review that enhances the precision of the sentiment categorization process. Observations from the experiments show that the proposed model outperforms the current sentiment categorization techniques by more than 8.86%, with an average accuracy rate of 97.30%.

Stepper motors are used for many applications in industries and robotics where precise control of motor position is essential. The is paper presents the implementation of position control of a Stepper motor using LabView.Half step and Full step mode of stepper motor control is performed and tested for different position angles. The test results show that half step mode makes an angle of 0.9ºper step (400 steps), whereas full step makes 1.8°per step (200 steps). With LabVIEW, the system has friendly interface, good control effect and easy control in both the directions.

Segmentation of images plays a key role in Image Processing as it simplifies further processing by separating a broad image into many parts. White Blood Cells (WBC) microscopic images allow haematologists to predict vulnerability to several diseases. Our intension is to identify Nucleus of White Blood cells. Colour is a significant reference point for discerning segmented WBCs from microscopic images. Results reveal that in identifying the nucleus of various types of WBC: including Neutrophils, Lymphocytes, Eosinophils, Monocytes, Basophils. In this study, we will evaluate the efficiency of the Edge detection Algorithm (log & Canny) methods, k-Means Algorithm, Linear Transformed Image, Color-based technique, and will compare the results with the original image. This will allow haematologists for clear identification of WBC. Analysis of the results using Algorithms shows that Edge detection and k-means based segmentation is the most suitable approach for segmenting WBC cells.