Results 21 to 30 of about 4,214,964 (288)
Predictive Equations for Estimation of the Slump of Concrete Using GEP and MARS Methods [PDF]
Journal of Soft Computing in Civil EngineeringThis paper developed two robust data-driven models, namely gene expression programming (GEP) and multivariate adaptive regression splines (MARS), for the estimation of the slump of concrete (SL). The main feature of the proposed data-driven methods is to
Ismail Husein +5 more
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The analog data assimilation [PDF]
, 2017 In light of growing interest in data-driven methods for oceanic, atmospheric, and climate sciences, this work focuses on the field of data assimilation and presents the analog data assimilation (AnDA).
Ailliot, Pierre +4 more
core +3 more sources
Data-Driven State Fragility Index Measurement Through Classification Methods
Frontiers in Physics, 2022 As environmental changes cause a series of complex issues and unstable situation, exploring the impact of environmental changes is essential for national stability, which is helpful for early warning and provides guidance solutions for a country.
Xin Li +3 more
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Data-Driven Methods for Advancing Precision Oncology. [PDF]
Curr Pharmacol Rep, 2018 This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice.Precision oncology provides individually tailored cancer treatment by considering an individual's genetic makeup, clinical, environmental, social,
Nedungadi P +4 more
europepmc +4 more sources
Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework [PDF]
, 2019 This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty.
Espuña Camarasa, Antonio +4 more
core +2 more sources
Optimal Data-Generation Strategy for Machine Learning Yield Functions in Anisotropic Plasticity
Frontiers in Materials, 2022 Trained machine learning (ML) algorithms can serve as numerically efficient surrogate models of sophisticated but numerically expensive constitutive models of material behavior. In the field of plasticity, ML yield functions have been proposed that serve
Ronak Shoghi, Alexander Hartmaier
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Nonparametric Method for Data-driven Image Captioning [PDF]
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2014 We present a nonparametric density estimation technique for image caption generation. Data-driven matching methods have shown to be effective for a variety of complex problems in Computer Vision. These methods reduce an inference problem for an unknown image to finding an existing labeled image which is semantically similar. However, related approaches
Rebecca Mason, Eugene Charniak
openaire +1 more source
Data-Driven Method to Quantify Correlated Uncertainties
IEEE Access, 2023 Polynomial chaos (PC) has been proven to be an efficient method for uncertainty quantification, but its applicability is limited by two strong assumptions: the mutual independence of random variables and the requirement of exact knowledge about the distribution of the random variables.
Jeahan Jung, Minseok Choi
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Data-driven extract method recommendations: a study at ING [PDF]
Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021 The sound identification of refactoring opportunities is still an open problem in software engineering. Recent studies have shown the effectiveness of machine learning models in recommending methods that should undergo different refactoring operations.
David van der Leij +5 more
openaire +3 more sources
Predicting personal thermal preferences based on data-driven methods [PDF]
E3S Web of Conferences, 2019 One of the prevalent models to account for thermal comfort in HVAC design is the Predicted Mean Vote (PMV). However, the model is based on parameters difficult to estimate in real applications and it focuses on mean votes of large groups of people ...
Aguilera José Joaquín +2 more
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