Results 141 to 150 of about 512,844 (338)

Improving Neural Network Generalization by Combining Parallel Circuits with Dropout [PDF]

, 2016
Kien Tuong Phan, Tomás Maul, Tuong Thuy Vu, Weng Kin Lai   +3 more
openalex   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns

Advanced Intelligent Discovery, EarlyView.
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer, André Orth, Robert Wang, Ben Breitung, Simon Schweidler, Jasmin Aghassi‐Hagmann, Markus Reischl   +6 more
wiley   +1 more source

Differences in perception - How deviations in quality perception of trainees and trainers affect dropout in VET [PDF]

, 2020
Maximilian Krötz, Viola Deutscher
openalex  

Correction to: Predictors of Dropout in Disordered Gamblers in UK Residential Treatment [PDF]

, 2019
Amanda Roberts, Raegan Murphy, John Turner, Steve Sharman   +3 more
openalex   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Sublingual versus subcutaneous immunotherapy: patient adherence at a large German allergy center

Patient Preference and Adherence, 2017
Marie-Luise Lemberg,1 Till Berk,2 Kija Shah-Hosseini,1 Elena-Manja Kasche,1,3 Ralph Mösges1 1Faculty of Medicine, Institute of Medical Statistics, Informatics and Epidemiology, University of Cologne, Cologne, Germany; 2Department of Trauma Surgery,
Lemberg M, Berk T, Shah-Hosseini K, Kasche E, Mösges R   +4 more
doaj  

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

A survey on dropout risk factors among medical students, Shiraz Medical University, 1999

Majallah-i Dānishgāh-i ̒Ulūm-i Pizishkī-i Bābul, 2003
Background and Objective: Dropout of medical students is an important problem in medical education. If not controlled, it will result a low scientific knowledge of physicians in future years.
GH.R Dehbozorgi
doaj