The advancement of Artificial Intelligence (AI) models heavily relies on large high-quality datasets. However, in advanced manufacturing, collecting such data is time-consuming and labor-intensive for a single enterpr...
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Due to the singular nonlinear Hall term, the non-resistive electron magnetohydrodynamics (MHD) is not known to be locally well-posed in general. In this paper we consider the 221D electron MHD with either horizontal o...
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Consistency models, which were proposed to mitigate the high computational overhead during the sampling phase of diffusion models, facilitate single-step sampling while attaining the state-of-the-art empirical perform...
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The presence of confounding by high-dimensional variables complicates the estimation of the average effect of a point treatment. On the one hand, it necessitates the use of variable selection strategies or more genera...
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According to the U.S. Energy Information Administration, 60 percent of the world's electricity is generated from fossil fuels, 18 percent from nuclear power, and only 21 percent from green energy resources. These ...
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Advancements in wearable electronics and portable sensors have enabled long-term and real-time health monitoring. Among these advancements, wearable ultrasound devices have garnered attention due to their portability,...
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Diabetes mellitus is a long-term condition characterized by *** could lead to plenty of *** to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of e...
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Diabetes mellitus is a long-term condition characterized by *** could lead to plenty of *** to rising morbidity in recent years,the world’s diabetic patients will exceed 642 million by 2040,implying that one out of every ten persons will be *** is no doubt that this startling figure requires immediate attention from industry and academia to promote innovation and growth in diabetes risk prediction to save individuals’*** to its rapid development,deep learning(DL)was used to predict numerous ***,DLmethods still suffer from their limited prediction performance due to the hyperparameters selection and parameters ***,the selection of hyper-parameters is critical in improving classification *** study presents Convolutional Neural Network(CNN)that has achieved remarkable results in many medical domains where the Bayesian optimization algorithm(BOA)has been employed for hyperparameters selection and parameters *** issues have been investigated and solved during the experiment to enhance the *** first is the dataset class imbalance,which is solved using Synthetic Minority Oversampling Technique(SMOTE)*** second issue is the model’s poor performance,which has been solved using the Bayesian optimization *** findings indicate that the Bayesian based-CNN model superbases all the state-of-the-art models in the literature with an accuracy of 89.36%,F1-score of 0.88.6,andMatthews Correlation Coefficient(MCC)of 0.88.6.
The electron magnetohydrodynamics (MHD) contains a highly nonlinear Hall term with an interesting structure. Exploring the Hall nonlinear structure, we investigate possible phenomena of finite time blow up for the ele...
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This paper proposes procedures for testing the equality hypothesis and the proportionality hypothesis involving a large number of q covariance matrices of dimension p × p. Under a limiting scheme where p, q and t...
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Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes over...
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Every application in a smart city environment like the smart grid,health monitoring, security, and surveillance generates non-stationary datastreams. Due to such nature, the statistical properties of data changes overtime, leading to class imbalance and concept drift issues. Both these issuescause model performance degradation. Most of the current work has beenfocused on developing an ensemble strategy by training a new classifier on thelatest data to resolve the issue. These techniques suffer while training the newclassifier if the data is imbalanced. Also, the class imbalance ratio may changegreatly from one input stream to another, making the problem more *** existing solutions proposed for addressing the combined issue of classimbalance and concept drift are lacking in understating of correlation of oneproblem with the other. This work studies the association between conceptdrift and class imbalance ratio and then demonstrates how changes in classimbalance ratio along with concept drift affect the classifier’s *** analyzed the effect of both the issues on minority and majority classesindividually. To do this, we conducted experiments on benchmark datasetsusing state-of-the-art classifiers especially designed for data stream ***, recall, F1 score, and geometric mean were used to measure theperformance. Our findings show that when both class imbalance and conceptdrift problems occur together the performance can decrease up to 15%. Ourresults also show that the increase in the imbalance ratio can cause a 10% to15% decrease in the precision scores of both minority and majority *** study findings may help in designing intelligent and adaptive solutionsthat can cope with the challenges of non-stationary data streams like conceptdrift and class imbalance.
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