It is well known that the 4-equation formulation of the two-fluid model is ill-posed. As a result, it is impossible to differentiate between the errors originating from uncertainty in the empirical closure models and ...
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Fetal heart rate monitoring is very important to check the status of the fetal heart during pregnancy. Cardiotocography is the most extensively used technique for the surveillance of the fetal heart rate signal and ut...
Fetal heart rate monitoring is very important to check the status of the fetal heart during pregnancy. Cardiotocography is the most extensively used technique for the surveillance of the fetal heart rate signal and uterine activity of the mother. It helps in monitoring the health of the fetus in pregnant women at high risk. The various Machine Learning classifiers play a vital role in the classification of the CTG data. The CTG data is the publicly available data with 2126 instances and 21 features which consists of 8 continuous and 13 discrete values. It consists of 1655 normal, 295 suspect, and 176 pathology classes which represents a clear imbalance in the dataset. In this paper, we proposed a novel ensemble method of sampling technique combined with a Random Forest classifier for the classification of the CTG data. There are two sampling techniques such as the oversampling technique to increase the samples in the minority class and the undersampling technique to decrease the samples in the majority class. Here we combined both the techniques such as the SMOTE (Synthetic Minority Oversampling Technique) from the oversampling technique and ENN (Edited Nearest Neighbor) from the undersampling technique and implemented it in the Random Forest classifier for classification. As there is an imbalance in the data, the sampling techniques help to balance the data. This proposed technique achieves the best accuracy of 93% in the classification of the normal, suspect, and pathology data. It also provides better results in terms of other metrics such as sensitivity and specificity. This method is also compared with other competent classifiers and is found to be an effective method for the classification of the CTG data.
Statistical mechanics can provide a versatile theoretical framework for investigating the collective dynamics of weakly nonlinear waves-settings that can be utterly complex to describe otherwise. In optics, composite ...
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We introduce a new framework to study the dynamics of open quantum systems with linearly coupled Gaussian baths. Our approach replaces the continuous bath with an auxiliary discrete set of pseudomodes with dissipative...
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We introduce a new framework to study the dynamics of open quantum systems with linearly coupled Gaussian baths. Our approach replaces the continuous bath with an auxiliary discrete set of pseudomodes with dissipative dynamics, but we further relax the complete positivity requirement in the Lindblad master equation and formulate a quasi-Lindblad pseudomode theory. We show that this quasi-Lindblad pseudomode formulation directly leads to a representation of the bath correlation function in terms of a complex weighted sum of complex exponentials, an expansion that is known to be rapidly convergent in practice and thus leads to a compact set of pseudomodes. The pseudomode representation is not unique and can differ by a gauge choice. When the global dynamics can be simulated exactly, the system dynamics is unique and independent of the specific pseudomode representation. However, the gauge choice may affect the stability of the global dynamics, and we provide an analysis of why and when the global dynamics can retain stability despite losing positivity. We showcase the performance of this formulation across various spectral densities in both bosonic and fermionic problems, finding significant improvements over conventional pseudomode formulations.
In this article, we consider an energy-critical complex Ginzburg-Landau equation in the exterior of a smooth compact strictly convex obstacle. We prove the global well-posedness of energy-critical complex Ginzburg-Lan...
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The degree of the Grassmannian with respect to the Plücker embedding is well-known. However, the Plücker embedding, while ubiquitous in pure mathematics, is almost never used in appliedmathematics. In appli...
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This paper presents a new data assimilation (DA) scheme based on a sequential Markov Chain Monte Carlo (SMCMC) DA technique [36] which is provably convergent and has been recently used for filtering, particularly for ...
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Federated learning (FL) has found many important applications in smart-phone-APP based machine learning applications. Although many algorithms have been studied for FL, to the best of our knowledge, algorithms for FL ...
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In this paper, we explore the potential of deep learning techniques in the field of ultra-fast laser processing. More specifically, we trained convolutional neural networks on an in-house dataset with the aim of predi...
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In today's world where technology and mathematics are progressing hand in hand there are so many things to be considered and thought of when it comes to network security. Cryptography plays a prominent and an impo...
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