Results 21 to 30 of about 2,045,405 (349)
Statistics of Solar Wind Electron Breakpoint Energies Using Machine Learning Techniques [PDF]
, 2020 Solar wind electron velocity distributions at 1 au consist of a thermal "core" population and two suprathermal populations: "halo" and "strahl". The core and halo are quasi-isotropic, whereas the strahl typically travels radially outwards along the ...
Bakrania, Mayur R. +6 more
core +2 more sources
bioRxiv, 2023 Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (
William DeGroat +5 more
semanticscholar +1 more source
Remote Sensing, 2021 Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu ...
Jun Liu +6 more
semanticscholar +1 more source
Correlations Between Learning Environments and Dropout Intention [PDF]
, 2021 This research is comparing learning environments to students dropout intentions. While using statistics I looked at data and the correlations between two articles to see how the two studies looked side to side. Learning environments and dropout intentions can both have vary effects on students.
arxiv +1 more source
Predicting the Type of Auditor Opinion: Statistics, Machine Learning, or a Combination of the Two?
European Journal of Applied Economics, 2019 The goal of this study is to overcome the identified methodological limitations of prior studies aimed at predicting the type of auditor opinion and draw definite conclusions on the relative predictive performance of different predictive methods for this
Nemanja Stanišić +2 more
semanticscholar +1 more source
A Variational Beam Model for Failure of Cellular and Truss‐Based Architected Materials
Advanced Engineering Materials, EarlyView., 2023 Herein, a versatile and efficient beam modeling framework is developed to predict the nonlinear response and failure of cellular, truss‐based, and woven architected materials. It enables the exploration of their design space and the optimization of their mechanical behavior in the nonlinear regime. A variational formulation of a beam model is presented
Konstantinos Karapiperis +3 more
wiley +1 more source
When and How to Apply Statistics, Machine Learning and Deep Learning Techniques
International Conference on Transparent Optical Networks, 2018 Machine Learning has become 'commodity' in engineering and experimental sciences, as calculus and statistics did before. After the hype produced during the 00's, machine learning (statistical learning, neural networks, etc.) has become a solid and ...
Josep Lluis Berral-Garcia
semanticscholar +1 more source
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges [PDF]
Statistics Survey, 2021 Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic.
C. Rudin +5 more
semanticscholar +1 more source
Advanced Engineering Materials, EarlyView., 2023 A novel method for tracking structural changes in gels using widely accessible microcomputed tomography is presented and validated for various hydro‐, alco‐, and aerogels. The core idea of the method is to track positions of micrometer‐sized tracer particles entrapped in the gel and relate them to the density of the gel network.
Anja Hajnal +3 more
wiley +1 more source
Rates of convergence in active learning [PDF]
Annals of Statistics 2011, Vol. 39, No. 1, 333-361, 2011 We study the rates of convergence in generalization error achievable by active learning under various types of label noise. Additionally, we study the general problem of model selection for active learning with a nested hierarchy of hypothesis classes and propose an algorithm whose error rate provably converges to the best achievable error among ...
arxiv +1 more source