The current state of the radial distribution system deteriorates daily. Loads are rapidly increasing in practice. As a result, maintaining the power quality of distribution systems is becoming increasingly challenging...
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The current state of the radial distribution system deteriorates daily. Loads are rapidly increasing in practice. As a result, maintaining the power quality of distribution systems is becoming increasingly challenging. The radial distribution scheme relies heavily on DG allocation and size derivation. Losses are visible in the distribution system, which might impact the power deviation quality. As a result, researchers have created many optimization strategies. The modifiedantlionoptimization (MALO) technique is suggested for determining the DG position and size of the radial distribution standard network. The loss sensitivity factor (LSF) is used to find the candidate bus. Many studies have used this strategy to identify local candidate bus search events. However, the MALO technique allows for a wide range of candidate search processes and determines the placement and size of needed buses. The method is applied to the system, which chooses the majority of potential buses for DG installation via loss sensitivity factors. The planned MALO is then utilized to determine DG positions and sizes based on the selected buses. The proposed technique is assessed on two IEEE radial distribution systems and contrasted with the outcomes of other recently established and efficient techniques. The results reveal that the power loss reduction increases to approximately 86 %, which leads to an increase in the economy. This paper provides the results, which confirm the utility of the MALO in optimizing the voltage profile in the distribution system under various loading conditions. This method is applicable worldwide because the loads might be changeable. Furthermore, superiority can be evaluated via the Wilcoxon test.
Thyroid is one of the most common diseases affecting millions of individuals across the world. According to the findings from numerous studies and surveys on thyroid disease, it is estimated that about 42 million peop...
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ISBN:
(纸本)9789811514517;9789811514500
Thyroid is one of the most common diseases affecting millions of individuals across the world. According to the findings from numerous studies and surveys on thyroid disease, it is estimated that about 42 million people in India and around 20 million people in America are suffering from some form of thyroid diseases, and women make up the majority of thyroid patients among them. It is caused due to the under (Hypothyroidism) or over (Hyperthyroidism) functionality of thyroid gland, which is responsible for maintaining the metabolism of the body, and it is imperative to diagnose its effects as early as possible so that a possible cure or treatment can be performed at the earliest. This paper aims to propose a modified ant lion optimization algorithm (MALO) for improving the diagnostic accuracy of thyroid disease. The proposed MALO is employed as a feature selection method to identify the most significant set of attributes from a large pool of available attributes to improve the classification accuracy and to reduce the computational time. Feature selection is one of the most significant aspects of machine learning which is used to remove the insignificant features from a given dataset to improve the accuracy of machine learning classifiers. Three different classifiers, namely Random Forest, k-Nearest Neighbor (kNN) and Decision Tree, are used for diagnosing the thyroid disease. The experimental results indicate that MALO eliminates 71.5% insignificant features out of the total number of features. The best accuracy achieved on the reduced set of features is 95.94% with Random Forest Classifier. Also, a notable accuracy of 95.66% and 92.51% has been achieved by Decision Tree classifier and k-Nearest Neighbor classifier, respectively. Additionally, MALO has been compared with other optimized variants of evolutionary algorithms to show the effectiveness and superiority of the proposed algorithm. Hence, the experimental results indicate that the MALO significantly outperf
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