Medical image segmentation is a technique for detecting boundaries in a 2D or 3D image automatically or semiautomatically. The enormous range of the medical image is a considerable challenge for image segmentation. Magnetic resonance imaging (MRI) scans to aid in the detection and existence of brain tumors. This approach, however, requires exact delineation of the tumor location inside the brain scan. To solve this, an optimization algorithm will be one of the most successful techniques for distinguishing pixels of interest from the background, but its performance is reliant on the starting values of the centroids. The primary goal of this work is to segment tumor areas within brain MRI images. After converting the gray MRI image to a color image, a multiobjective modified ABC algorithm is utilized to separate the tumor from the brain. The intensity determines the RGB color generated in the image. The simulation results are assessed in terms of performance metrics such as accuracy, precision, specificity, recall, F-measure, and the time in seconds required by the system to segment the tumor from the brain. The performance of the proposed algorithm is computed with other algorithms like the single-objective ABC algorithm and multiobjective ABC algorithm. The results prove that the proposed multiobjective modified ABC algorithm is efficient in analyzing and segmenting the tumor from brain images.
The emergence of technology and communication system paved way for the development of the internet of things (IoT). The IoT system generates diversified data and the quantity of the data is also huge. The IoT systems are developed to adhere to the situation and to make intelligent decisions in a specified time. Hence, the IoT system necessitates high processing and storage environment, which makes the effective response in a short-duration. The data transmission across the mobile nodes and cloud service has made huge utilization of energy. The storage and energy consumption are considered as major issues in the IoT system whereas these issues will reflect in the performance of the IoT system. Initiation of edge computing into the IoT system permits the workload to be offloaded from the cloud providers, which is attained from the closer location of the source of data. This improves privacy, minimizes the saving time, and traffic. In this article, an Effective Energy usage and Data Compression Approach using Data Mining proposed for IoT data. The proposed approach is investigated by considering the driving behaviour and it achieves effective compression without influencing the quality of the data. The stress level of the driver is also identified with high accuracy.
In this model the induction motor speed controlis based on torque control drive is analysis the output power using Anticipating Power Impulse Technique (APIT). Induction motors are used in various applications, this is a model of a motor used in constant speed applications. Vibrations and singularities are eliminated with the sliding mode control of conventional terminals. In this method, the reference state is determined in a systematic manner so that the maximum torque per ampere and ux limiting operation and the reference torque tracking can be determined. The di culty of a controller driver is choosing a switch with the proper control scheme to achieve dynamic and steady state response speed requirements and the proposed method is completely varied from conventional model. AnAnticipating Power Impulse Technique (APIT)is the proposed systemto the induction motor drive is control it is also applied to be speed control of the motor. The system presented here analyses motor current and rotor position, which helps to obtain a suitable switching pulse diver circuit. Because of low switching loss speed control. In this system, MATLAB software is used to develop control circuit simulations. It is expected that the simulation output will reduce the torque ripple veri ed by experiments and Anticipating Power Impulse Technique(APIT), giving the system output excellent stability.
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