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Forecasting and Optimizing Dual Media Filter Performance via Machine Learning.

Water research
Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of ...

An interpretive constrained linear model for ResNet and MgNet.

Neural networks : the official journal of the International Neural Network Society
We propose a constrained linear data-feature-mapping model as an interpretable mathematical model for image classification using a convolutional neural network (CNN). From this viewpoint, we establish detailed connections between the traditional iter...

Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network.

Sensors (Basel, Switzerland)
Oxygen uptake (V˙O2) is an important metric in any exercise test including walking and running. It can be measured using portable spirometers or metabolic analyzers. Those devices are, however, not suitable for constant use by consumers due to their ...

LGEANet: LSTM-global temporal convolution-external attention network for respiratory motion prediction.

Medical physics
PURPOSE: To develop a deep learning network that treats the three-dimensional respiratory motion signals as a whole and considers the inter-dimensional correlation between signals of different directions for accurate respiratory tumor motion predicti...

Expectile Neural Networks for Genetic Data Analysis of Complex Diseases.

IEEE/ACM transactions on computational biology and bioinformatics
The genetic etiologies of common diseases are highly complex and heterogeneous. Classic methods, such as linear regression, have successfully identified numerous variants associated with complex diseases. Nonetheless, for most diseases, the identifie...

Predictive Models of Life Satisfaction in Older People: A Machine Learning Approach.

International journal of environmental research and public health
Studies of life satisfaction in older adults have been conducted extensively through empirical research, questionnaires, and theoretical analysis, with the majority of these studies basing their analyses on simple linear relationships between variabl...

Application of Neural Network in Predicting HS from an Acid Gas Removal Unit (AGRU) with Different Compositions of Solvents.

Sensors (Basel, Switzerland)
The gas sweetening process removes hydrogen sulfide (HS) in an acid gas removal unit (AGRU) to meet the gas sales' specification, known as sweet gas. Monitoring the concentration of HS in sweet gas is crucial to avoid operational and environmental is...

Gradient Tree Boosting for Hierarchical Data.

Multivariate behavioral research
Gradient tree boosting is a powerful machine learning technique that has shown good performance in predicting a variety of outcomes. However, when applied to hierarchical (e.g., longitudinal or clustered) data, the predictive performance of gradient ...

Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation.

Journal of the Royal Society, Interface
Inferring the underlying processes that drive collective behaviour in biological and social systems is a significant statistical and computational challenge. While simulation models have been successful in qualitatively capturing many of the phenomen...

Several machine learning techniques comparison for the prediction of the uniaxial compressive strength of carbonate rocks.

Scientific reports
In engineering practices, it is critical and necessary to either measure or estimate the uniaxial compressive strength (UCS) of the rock. Measuring the UCS of rocks requires comprehensive studies in the field and in the laboratory for the rock block ...