AIMC Topic: Probability

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Intelligent Reading of English Text Based on the Generative Model Constraint Label Fusion.

Computational intelligence and neuroscience
The intelligent reading of English text is affected by complex environmental factors, which will result in low reading accuracy and poor reader experience. Based on the artificial intelligence model, this study constructs the artificial intelligence ...

Volumetric macromolecule identification in cryo-electron tomograms using capsule networks.

BMC bioinformatics
BACKGROUND: Despite recent advances in cellular cryo-electron tomography (CET), developing automated tools for macromolecule identification in submolecular resolution remains challenging due to the lack of annotated data and high structural complexit...

PassTCN-PPLL: A Password Guessing Model Based on Probability Label Learning and Temporal Convolutional Neural Network.

Sensors (Basel, Switzerland)
The frequent incidents of password leakage have increased people's attention and research on password security. Password guessing is an essential part of password cracking and password security research. The progression of deep learning technology pr...

Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception.

Sensors (Basel, Switzerland)
In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attain...

Pan-Logical Probabilistic Algorithms Based on Convolutional Neural Networks.

Computational intelligence and neuroscience
A brand-new kind of flexible logic system called universal logic aims to address a variety of uncertain problems. In this study, the role of convolutional neural networks in assessing probabilistic pan-logic algorithms is investigated. A generic logi...

Spiking Neural Network Regularization With Fixed and Adaptive Drop-Keep Probabilities.

IEEE transactions on neural networks and learning systems
Dropout and DropConnect are two techniques to facilitate the regularization of neural network models, having achieved the state-of-the-art results in several benchmarks. In this paper, to improve the generalization capability of spiking neural networ...

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...