AIMC Topic: Probability

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

Computational intelligence and neuroscience Jun 23, 2022
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...
Intelligence Machine Learning Neural Networks, Computer Algorithms Probability
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Remaining Useful Life Prediction Method for Bearings Based on LSTM with Uncertainty Quantification.

Sensors (Basel, Switzerland) Jun 16, 2022
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...
Probability Uncertainty Neural Networks, Computer
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Maximum entropy models provide functional connectivity estimates in neural networks.

Scientific reports Jun 10, 2022
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...
Brain Models, Neurological Neural Networks, Computer Nerve Net Entropy Probability Neurons
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Security Evaluation of Financial and Insurance and Ruin Probability Analysis Integrating Deep Learning Models.

Computational intelligence and neuroscience Jun 8, 2022
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...
Probability Bankruptcy Insurance Deep Learning
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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 Jun 2, 2022
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...
Computer Simulation Neural Networks, Computer Normal Distribution Probability
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Nonparametric estimation of the causal effect of a stochastic threshold-based intervention.

Biometrics May 26, 2022
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...
Humans Machine Learning Bias Probability Models, Statistical Causality
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Analysis and Prevention and Control System of Domino Accident Risk Data in Chemical Parks Based on Topological Neural Network.

Computational intelligence and neuroscience May 26, 2022
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...
Probability Reproducibility of Results Accidents Algorithms Neural Networks, Computer
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A New Wavelet-Based Privatization Mechanism for Probability Distributions.

Sensors (Basel, Switzerland) May 14, 2022
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...
Privatization Machine Learning Artificial Intelligence Probability Algorithms
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Continuous-time probabilistic models for longitudinal electronic health records.

Journal of biomedical informatics May 7, 2022
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...
Probability Algorithms Models, Statistical Electronic Health Records Humans Machine Learning
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Robots as models of evolving systems.

Proceedings of the National Academy of Sciences of the United States of America Mar 17, 2022
Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, an...
Robotics Mutation Population Dynamics Animals Models, Genetic Probability Selection, Genetic Mammals
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