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Bayes Theorem

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Automatic Evolution of Machine-Learning-Based Quantum Dynamics with Uncertainty Analysis.

Journal of chemical theory and computation
The machine learning approaches are applied in the dynamical simulation of open quantum systems. The long short-term memory recurrent neural network (LSTM-RNN) models are used to simulate the long-time quantum dynamics, which are built based on the k...

Uncertainty Estimation Using Variational Mixture of Gaussians Capsule Network for Health Image Classification.

Computational intelligence and neuroscience
Capsule Networks have shown great promise in image recognition due to their ability to recognize the pose, texture, and deformation of objects and object parts. However, the majority of the existing capsule networks are deterministic with limited abi...

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.

International journal of environmental research and public health
Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an incr...

Multinomial Naive Bayesian Classifier Framework for Systematic Analysis of Smart IoT Devices.

Sensors (Basel, Switzerland)
Businesses need to use sentiment analysis, powered by artificial intelligence and machine learning to forecast accurately whether or not consumers are satisfied with their offerings. This paper uses a deep learning model to analyze thousands of revie...

Human reliability analysis in deep excavation projects using a fuzzy Bayesian HEART-5M integrated method: case of a residential tower in north Tehran.

International journal of occupational safety and ergonomics : JOSE
Numerous labourers lose their lives or suffer from injuries and disabilities yearly due to the lack of safety enforcement in construction projects and accidents caused by excavation collapses. The identification and ranking of human errors have alwa...

Assessing spatial connectivity effects on daily streamflow forecasting using Bayesian-based graph neural network.

The Science of the total environment
Data-driven models have been widely developed and achieved impressive results in streamflow prediction. However, the existing data-driven models mostly focus on the selection of input features and the adjustment of model structure, and less on the im...

Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data.

Scientific reports
Individual daily travel activities (e.g., work, eating) are identified with various machine learning models (e.g., Bayesian Network, Random Forest) for understanding people's frequent travel purposes. However, labor-intensive engineering work is ofte...

A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors.

Sensors (Basel, Switzerland)
For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. In this work, the time domain features and time-frequency-domain features extracted from several successive segments of current ...

Implementation of Machine Learning Mechanism for Recognising Prostate Cancer through Photoacoustic Signal.

Contrast media & molecular imaging
Biological tissues may be studied using photoacoustic (PA) spectroscopy, which can yield a wealth of physical and chemical data. However, it is really challenging to directly analyse these tissues because of a lot of data. Data mining techniques can ...

TransMorph: Transformer for unsupervised medical image registration.

Medical image analysis
In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationshi...