Water serves as the primary source for living organisms, but the availability of drinkable water on Earth is limited, mainly found in icebergs and the sea. Determining the pH of water is crucial for various applicatio...
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In this work, a novel operator-based differential evolution (DE) algorithm has been proposed. The proposed approach has been inspired by the internal adaption (environment) of the search space. Therefore, maintaining ...
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Monitoring the environment and managing water bodies are crucial for preserving ecosystems and ensuring sustainable resource utilization. This study aims to propose a robust approach for segmenting water bodies by com...
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Fault prediction is the process of using data analysis and machine learning models to anticipate potential defects or faults in the software system. Using only the base machine learning models for software fault predi...
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Fault prediction is the process of using data analysis and machine learning models to anticipate potential defects or faults in the software system. Using only the base machine learning models for software fault prediction leads to limited performance, difficulty in handling non-linear relationships and imbalanced data, inadequate feature representation, and limited complexity handling. Hence, in order to overcome these challenges, this paper proposes a new technique for the selection of classifiers that forms a heterogeneous ensemble. The main goal is to remove or trim out the classifiers that show poor performance compared to the other base classifiers, which can result into a more effective ensemble and can produce better results. The algorithm proposed in this paper finds a set of classifiers that can perform better than using all the classifiers. The challenge that was faced was how to identify the poor-performing classifiers. This challenge is dealt with by performing an experiment using different threshold values to choose the trimmed set of classifiers. For evaluation of the proposed model, 8 different benchmark software fault datasets were used, which are taken from PROMISE and the Apache repository, and AUC is used as the performance measure. The results obtained after the experimental analysis demonstrate the effectiveness of our algorithm compared to the traditional approaches, which used all the base classifiers. There is a significant increase in the AUC values for 6 datasets out of 8, while using the average of probabilities and majority voting, it was seen that there is improvement in 7 out of 8 datasets used. The best-performing dataset by using the average of probabilities is ARC, where the AUC values increase from 0.6505 to 0.694, and while using majority voting, the best-performing dataset is XALAN, where the AUC values increase from 0.5455 to 0.679. From this, it can be seen that the proposed ensemble approach achieved higher AUC values for the
The present scene text extraction methods generally include two independent steps of text detection and text recognition, which is not beneficial to the interaction of feature information and may result in the accumul...
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This paper presents a high gain, compact size and dual band rectangular patch antenna for 5G applications. To enhance the gain of antenna, an equilateral triangle slots on the upper rectangular patch are constructed. ...
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Cervical cancer is a serious health issue and a leading reason of cancer-related deaths among women, particularly in less economically developed countries (LEDCs) where there is limited availability of screening. Earl...
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In the contemporary landscape, consumer purchasing decisions heavily rely on customer reviews available on various e-commerce platforms. However, the process of acquiring information for a product involves reading num...
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In modern time, the field of robotics is growing fast. A robot is a machine that can do physical jobs, with people overseeing and guiding it. Many robots have been developed to perform hazardous tasks that people cann...
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Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank str...
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