This approach can handle video scenes containing moving background, illumination variation, and also include into the background model shadow cast by moving objects.Shobha, GKumar, N. SatishInternational Association of Engineering and Technology (IAET)...
By connecting Otto to a Bayesian optimizer, the machine-learning model directed the experimental execution on the basis of measurement feedback in real time to optimize the electrochemical window of aqueous sodium electrolyte in the design space of mixtures of NaNO3, NaClO4, Na2SO4, and NaBr and...
Every machine learning application has to consider the aspects of overfitting and underfitting. The reason for underfitting usually lies either in the model, which lacks the ability to express the complexity of the data, or in the features, which do not adequately describe the data. This inevitabl...
In recent years, machine learning (ML) techniques have emerged as powerful tools for forecasting renewable energy (RE) generation, enabling improved planning... M Mohabbati - Springer, Cham 被引量: 0发表: 2024年 A two-stage model for stock price prediction based on variational mode decomposition...
Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics Article20 September 2019 Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification ...
a quote from the research from the China University of Petroleum, "Feature importance indicates that a distance to fault, a distance to basement, minimum principal stress, cumulative fluid injection, initial formation pressure, and the number of fracturing stages are among significant model predictors...
The innovation of machine learning In a remarkable paper published in 1998, Gassner et al. demonstrated for the case of Al3+ions in water that ‘the advantages of a neural network type potential function as a model-independent and “semiautomatic” potential function outweigh the disadvantages in...
Machine learning and Deep Learning techniques may be also exploited to model the behavior of a number of MT components and structural parts. Interesting applications are related to the prediction of the process forces on the workpiece and the computation of coefficients to define the stability of th...
A higher R-value or (R2) value indicates more accurate model performance. Two other metrics are commonly used to assess the predictive error of machine learning models: Root mean square error (RMSE)R=∑n−1N(xnˆ−xn)2Nand standard error of estimate (SEE)R=∑n−1N(xnˆ−xn)...
ObjectivesThe purpose of this study was to develop a machine-learning model based on routine, quantitative, and easily measured variables to predict the presence and extent of subclinical atherosclerosis (SA) in young, asymptomatic individuals. The risk of having SA estimated by this model could be...