Results 71 to 80 of about 1,371,617 (304)

Spatial Statistics & Machine Learning.

Proposed for presentation at the Sandia Machine Learning/Deep Learning Workshop held July 25-28, 2022 in ,, 2022
Lyndsay Shand
semanticscholar   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning

Neurosurgery, 2019
BACKGROUND Although survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata.
J. Senders, Patrick C. Staples, Alireza Mehrtash, D. Cote, M. Taphoorn, D. Reardon, W. Gormley, T. Smith, M. Broekman, O. Arnaout   +9 more
semanticscholar   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

New algorithms for learning incoherent and overcomplete dictionaries

, 2013
In sparse recovery we are given a matrix A∈R[superscript n×m] (“the dictionary”) and a vector of the form AX where X is sparse, and the goal is to recover X. This is a central notion in signal processing, statistics and machine learning.
Arora, Sanjeev, Ge, R, Ankur Moitra, Moitra, A, Sanjeev Arora, Rong Ge, Ge, Rong, Moitra, Ankur   +7 more
core  

TGPred: efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning and optimization. [PDF]

NAR Genom Bioinform, 2023
Cao X, Zhang L, Islam MK, Zhao M, He C, Zhang K, Liu S, Sha Q, Wei H.   +8 more
europepmc   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Zein‐Based Adhesives: Sustainable Extraction and Application in Bioadhesive Technologies

Advanced Engineering Materials, EarlyView.
Zein is extracted from corn gluten meal using a simple and scalable process with high yield (~90%). The resulting protein is applied in bioadhesives modified with Ca2+ and Fe3+ ions, exhibiting substrate‐dependent adhesion. The findings demonstrate competitive bonding performance and highlight the role of ionic interactions in tuning adhesion ...
Paula Bertolino Sanvezzo, Daniela Winsley Valereto Friozi, Vinícius de Oliveira, Marcia Cristina Branciforti   +3 more
wiley   +1 more source

Learning Features that Predict Cue Usage

, 1997
Our goal is to identify the features that predict the occurrence and placement of discourse cues in tutorial explanations in order to aid in the automatic generation of explanations.
Moore, Johanna D., Eugenio, Barbara Di, Barbara Di Eugenio, Johanna D. Moore, Massimo Paolucci, Paolucci, Massimo   +5 more
core   +1 more source

Predicting and improving complex beer flavor through machine learning

Nature Communications
The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict.
Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, C. Vanderaa, Florian A Theßeling, Łukasz Kreft, Alexander Botzki, Philippe Malcorps, L. Daenen, T. Wenseleers, K. Verstrepen   +12 more
semanticscholar   +1 more source