In the digital age, image forgery is a significant concern due to advanced editing tools. This study addresses the need for reliable forgery detection, focusing on copy-move, splicing, and retouching techniques. Using...
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In this paper, we propose a novel ensemble of Convolutional Neural Network-Long Short-Term Memory with an Extra Tree Classifier for automatic feature engineering in the spatiotemporal domain and classification of diff...
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The management of plastic waste is a pressing global issue that demands effective solutions to mitigate it's negative impacts on the environment, health and economies. This work proposes the development of a web b...
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In this article, a face recognition via thermal imaging: a comparative study of traditional and CNN-based approaches is proposed. The methodology comprises two distinct components: traditional face recognition and CNN...
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In today's world, Artificial Intelligence (AI) and its usage concerning human tasks have become an integral part of our daily lives. Humans depend upon AI to provide faster and more efficient solutions. In the wor...
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In this paper, we define a new problem, called the almost order-preserving matching (aOPM) problem, which is a combined variant of order-preserving matching (OPM) and the longest almost increasing subsequence (LaIS). ...
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This paper investigates the impact of different earthing methods on overvoltage within modular multilevel Voltage Source Converter (MMC VSC) based Direct Current (DC) transmission and distribution grids, particularly ...
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Technological advancements can have powerful impact on the economic growth of agriculture in India. Areca nut farming is one such terrain of agriculture where investment in technology and automation can elevate the pr...
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In this paper, we propose a time-dependent multi-objective trip planning using ant colony optimization. Especially, the proposed method deals with time-dependent POI factors by utilizing past-trip records with time st...
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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
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