Results 101 to 110 of about 4,693,052 (300)
Advanced Engineering Materials, EarlyView.In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi +4 more
wiley +1 more source
Machine Learning‐Guided Discovery of Factors Governing Deformation Twinning in Mg–Y Alloys
Advanced Engineering Materials, EarlyView.This study uses interpretable machine learning to identify key microstructural and processing parameters related to twinning in magnesium‐yttrium (Mg–Y) alloys. It is identified that using only grain size, grain orientation, and total applied strain, grains can be classified with 84% accuracy based on whether the grain contains a twin.
Peter Mastracco +8 more
wiley +1 more source
The New Approach on Fuzzy Decision Trees [PDF]
Jooyeol Yun, Jun won Seo, and Taeseon Yoon (2014) THE NEW APPROACH
ON FUZZY DECISION TREES International Journal of Fuzzy Logic Systems (IJFLS)
Vol.4, No.3, July 2014, 2014 Decision trees have been widely used in machine learning. However, due to some reasons, data collecting in real world contains a fuzzy and uncertain form. The decision tree should be able to handle such fuzzy data. This paper presents a method to construct fuzzy decision tree.
arxiv
Optimal decision trees for categorical data via integer programming
Journal of Global Optimization, 2021 O. Günlük +4 more
semanticscholar +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
Advanced Engineering Materials, EarlyView.This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
wiley +1 more source
Shopping intention prediction using decision trees
Millenium, 2017 Introduction: The price is considered to be neglected marketing mix element due to the complexity of price management and sensitivity of customers on price changes. It pulls the fastest customer reactions to that change.
Dario Šebalj +2 more
doaj
, 2020 Decision trees (DTs) epitomize what have become to be known as interpretable machine learning (ML) models. This is informally motivated by paths in DTs being often much smaller than the total number of features. This paper shows that in some settings DTs can hardly be deemed interpretable, with paths in a DT being arbitrarily larger than a PI ...
Izza, Yacine +2 more
openaire +3 more sources
Advanced Functional Materials, EarlyView.The seed pod valves of Australian Banksia attenuata plants are not simply bi‐layers which bend when dry. These experiments and models reveal complex mechanics, which allow seed release only after several steps of seed pod opening. Stiffness gradients prevent delamination of the valves during loading, and a shape‐memory function protects the seeds ...
Friedrich Reppe +7 more
wiley +1 more source
A New Pruning Method for Solving Decision Trees and Game Trees [PDF]
arXiv, 2013 The main goal of this paper is to describe a new pruning method for solving decision trees and game trees. The pruning method for decision trees suggests a slight variant of decision trees that we call scenario trees. In scenario trees, we do not need a conditional probability for each edge emanating from a chance node.
arxiv
Advanced Functional Materials, EarlyView.A novel stratum corneum‐inspired zwitterionic hydrogel is developed for intelligent, flexible sensors, featuring intrinsic water retention and anti‐freezing properties. The quasi‐gel, composed of hygroscopic polymers and bound water, maintains its softness across a wide range of humidity.
Meng Wu +8 more
wiley +1 more source