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)...
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...
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...
Explore the latest news and expert commentary on Machine Learning & AI, brought to you by the editors of InformationWeek
A machine learning model in predicting hemodynamically significant coronary artery disease: A prospective cohort study Background:Coronary artery disease (CAD) costs healthcare billions of dollars annually and is the leading cause of death despite available noninvasive diag... Y Liu,H Ren,H Fanous,...
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...
DRL architectures can be either model-based or model-free. Scientific machine learning The nascent field of scientific machine learning (SciML)80 is creating new opportunities across all paradigms of machine learning, and deep learning in particular. SciML is focused on creating ML systems that ...
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...
A Refined Model for the Evaluation of Module Faults on the Performance of Photovoltaic Power Plants Suzhou Nuclear Power Research Institute,Suzhou,Jiangsu,China,215004Hong Tao FanSchool of Information Science and Technology, Fudan University,Shanghai,China,... Yi Min Guo,Xin Zhang,Hong Tao Fan,.....
The model can instantly predict relatively long-range radial distribution functions, offering in situ local structure analysis of materials. The advent of high-throughput XAS databases has recently unveiled more possibilities for machine learning models to be deployed using XAS data. For example, Zheng...