The Brain tumor is considered an unusual growth of cells in the nervous system that restricts the normal functionality of the brain. However, is generated in the skull and pressures the brain which affects the health ...
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The Brain tumor is considered an unusual growth of cells in the nervous system that restricts the normal functionality of the brain. However, is generated in the skull and pressures the brain which affects the health of a person. So it is essential to detect and classify the brain tumor at an early stage before reaching the severity level. Meanwhile, brain tumor detection is performed based on MRI images which are considered an effective diagnosis system. But the detection and classification using MRI images is obtained as a complex task and cannot show the difference between normal and abnormal cells. So to overcome this issue the Crossover smellagent Optimized Multilayer Perception (CSA-MLP) is proposed to perform the exact detection and classification of tumor cells from MRI images. The images are collected from three datasets namely MR, Brain MRI, and Brain tumor datasets and they are preprocessed to remove the unwanted noise. After preprocessing the features of the images are extracted to perform the classification process. Moreover, the Convolutional Neural Network (CNN) classifier is used to classify healthy and unhealthy brain cells. The Multi-Layer Perceptron (MLP) is employed for the classification category that minimized the errors and enhanced the performance of the proposed method. The MLP is integrated with the CSA optimizationalgorithm to improve classification accuracy. The experimentation results revealed that the proposed method achieved a better accuracy of about 98.56% which enhanced the effectiveness compared to existing methods.
Energy efficiency of cold storage systems has remained a challenge to industries over the years. Despite several attempts made by experts to surmount the challenge, there remains a huge potential for improvement. Ther...
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ISBN:
(纸本)9781665479783
Energy efficiency of cold storage systems has remained a challenge to industries over the years. Despite several attempts made by experts to surmount the challenge, there remains a huge potential for improvement. Therefore, a smellagentoptimization (SAO) algorithm is proposed to tune a supervisory model predictive control (MPC) scheme to achieve reduction in high cooling load caused by high ambient temperatures. By this reduction, the compressor is saved from operating at saturated state, a state that is highly inefficient and detrimental to product quality. Simulation results show that the SAO-based supervisory model predictive controller achieved better performance than the traditional regulatory model predictive controller by reducing the cooling load by 55.5%. The SAO-based supervisory MPC also performed better in keeping the product temperature below 5 degrees C while the regulatory MPC response settled at 6.4 degrees C, a temperature that is detrimental to food safety and quality. The results imply that SAO-based supervisory MPC achieved a significant improvement in energy efficiency while preserving the product within the safe temperature range recommended by the food authority. All simulations are performed using MATLAB 2020a.
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