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Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks.

IEEE transactions on neural networks and learning systems
Imbalanced class distribution is an inherent problem in many real-world classification tasks where the minority class is the class of interest. Many conventional statistical and machine learning classification algorithms are subject to frequency bias...

An Intelligent Fusion Model with Portfolio Selection and Machine Learning for Stock Market Prediction.

Computational intelligence and neuroscience
Developing reliable equity market models allows investors to make more informed decisions. A trading model can reduce the risks associated with investment and allow traders to choose the best-paying stocks. However, stock market analysis is complicat...

Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification.

Sensors (Basel, Switzerland)
To reduce the economic losses caused by bearing failures and prevent safety accidents, it is necessary to develop an effective method to predict the remaining useful life (RUL) of the rolling bearing. However, the degradation inside the bearing is di...

Maximum entropy models provide functional connectivity estimates in neural networks.

Scientific reports
Tools to estimate brain connectivity offer the potential to enhance our understanding of brain functioning. The behavior of neuronal networks, including functional connectivity and induced connectivity changes by external stimuli, can be studied usin...

Security Evaluation of Financial and Insurance and Ruin Probability Analysis Integrating Deep Learning Models.

Computational intelligence and neuroscience
To ensure safe development of the financial and insurance industry and promote the continuous growth of the social economy, the theory and its role of deep learning are firstly analyzed. Secondly, the security of financial and insurance and bankruptc...

Approximation properties of Gaussian-binary restricted Boltzmann machines and Gaussian-binary deep belief networks.

Neural networks : the official journal of the International Neural Network Society
Despite the successful use of Gaussian-binary restricted Boltzmann machines (GB-RBMs) and Gaussian-binary deep belief networks (GB-DBNs), little is known about their theoretical approximation capabilities to represent distributions of continuous rand...

Nonparametric estimation of the causal effect of a stochastic threshold-based intervention.

Biometrics
Identifying a biomarker or treatment-dose threshold that marks a specified level of risk is an important problem, especially in clinical trials. In view of this goal, we consider a covariate-adjusted threshold-based interventional estimand, which hap...

Analysis and Prevention and Control System of Domino Accident Risk Data in Chemical Parks Based on Topological Neural Network.

Computational intelligence and neuroscience
A topologically based neural network algorithm is used to conduct an in-depth study and analysis of domino accident risk data in chemical parks, and this is used to construct a prevention and control system applied to the safety prediction of chemica...

A New Wavelet-Based Privatization Mechanism for Probability Distributions.

Sensors (Basel, Switzerland)
In this paper, we propose a new privatization mechanism based on a naive theory of a perturbation on a probability using wavelets, such as a noise perturbs the signal of a digital image sensor. Wavelets are employed to extract information from a wide...

Continuous-time probabilistic models for longitudinal electronic health records.

Journal of biomedical informatics
Analysis of longitudinal Electronic Health Record (EHR) data is an important goal for precision medicine. Difficulty in applying Machine Learning (ML) methods, either predictive or unsupervised, stems in part from the heterogeneity and irregular samp...