Results 101 to 110 of about 1,147,666 (317)

Hip morphology‐based osteoarthritis risk prediction models: development and external validation using individual participant data from the World COACH Consortium

Arthritis Care &Research, Accepted Article.
Objectives This study aims to develop hip morphology‐based radiographic hip osteoarthritis (RHOA) risk prediction models and investigates the added predictive value of hip morphology measurements and the generalizability to different populations. Methods We combined data from nine prospective cohort studies participating in the World COACH consortium ...
Myrthe A. van den Berg, Fleur Boel, Michiel M.A. van Buuren, Noortje S. Riedstra, Jinchi Tang, Harbeer Ahedi, Nigel K. Arden, Sita M.A. Bierma‐Zeinstra, Cindy G. Boer, Flavia M. Cicuttini, Timothy F. Cootes, David T. Felson, Willem Paul Gielis, Joshua Heerey, Graeme Jones, Stefan Kluzek, Nancy E. Lane, Claudia Lindner, Joyce B.J. van Meurs, Andrea Mosler, Amanda E. Nelson, Michael C. Nevitt, Edwin H. Oei, Jos Runhaar, Harrie Weinans, Jesse H. Krijthe, Rintje Agricola   +26 more
wiley   +1 more source

A Revised Publication Model for ECML PKDD [PDF]

, 2012
ECML PKDD is the main European conference on machine learning and data mining. Since its foundation it implemented the publication model common in computer science: there was one conference deadline; conference submissions were reviewed by a program ...
Blockeel, Hendrik, Kersting, Kristian, Nijssen, Siegfried, Zelezny, Filip   +3 more
core   +1 more source

Exploring patients’ profiles associated with the resolution of acute calcium pyrophosphate arthritis treatedwith colchicine and prednisone: post hoc analysis of a randomized controlled trial

Arthritis Care &Research, Accepted Article.
Objective The objective was to identify factors determining acute arthritis resolution and safety with colchicine and prednisone in acute calcium pyrophosphate (CPP) crystal arthritis. Methods We conducted a post hoc analysis of the COLCHICORT trial, which compared colchicine and prednisone for the treatment of acute CPP crystal arthritis, using a ...
Tristan Pascart, Laurène Norberciak, Pascal Richette, Pierre Robinet, Aurore Pacaud, Gauthier Marchasson, Thibault Rabin, Hélène Luraschi, Pierre Maciejasz, Anne‐France Georgel, Augustin Latourte, Hang‐Korng Ea, Sébastien Ottaviani, Charlotte Jauffret, Vincent Ducoulombier   +14 more
wiley   +1 more source

Efficient Machine Learning on Heterogeneous Computing Systems through a Coordinated Runtime System [PDF]

, 2019
Department of Computer Science and EngineeringAs machine learning grows, a heterogeneous computing system is actively used for a solution to increase the efficiency of machine learning. Although there are the prior studies for improving the efficiency of
Hyun, Jihoon
core  

Data‐driven forecasting of ship motions in waves using machine learning and dynamic mode decomposition

International Journal of Adaptive Control and Signal Processing, EarlyView.
Summary Data‐driven forecasting of ship motions in waves is investigated through feedforward and recurrent neural networks as well as dynamic mode decomposition. The goal is to predict future ship motion variables based on past data collected on the field, using equation‐free approaches.
Matteo Diez, Mauro Gaggero, Andrea Serani   +2 more
wiley   +1 more source

Optimized Time–Frequency Analysis for Induction Motor Fault Detection Using Hybrid Differential Evolution and Deep Learning Techniques

International Journal of Adaptive Control and Signal Processing, EarlyView.
Workflow of the parameter optimization process for ITSC fault detection, applying Differential Evolution optimization and the Smooth Pseudo Wigner‐Ville Distribution for signal processing. The optimized parameters are then used in the failure identification pipeline, which combines the signal processing with a YOLO‐based architecture for fault severity
Rafael Martini Silva, Avyner Lorran de Oliveira Vitor, Marcelo Favoretto Castoldi, Alessandro Goedtel, Wesley Angelino Souza   +4 more
wiley   +1 more source

Capturing human category representations by sampling in deep feature spaces [PDF]

, 2018
Understanding how people represent categories is a core problem in cognitive science. Decades of research have yielded a variety of formal theories of categories, but validating them with naturalistic stimuli is difficult.
Aghi, Krisha, Griffiths, Thomas L., Ku, Alexander Y., Peterson, Joshua C., Suchow, Jordan W.   +4 more
core   +1 more source

3D In Vitro Models of Breast Cancer: Current Challenges and Future Prospects Toward Recapitulating the Microenvironment and Mimicking Key Processes

Advanced Biology, EarlyView.
In vitro cancer models are advantageous for studying important processes such as tumorigenesis, cancer growth, invasion, and metastasis. The complexity and biological relevance increase depending on the model structure, organization, and composition of materials and cells.
Kyndra S. Higgins, Ah Joung Yu, Cheryl T. Gomillion   +2 more
wiley   +1 more source

Utilizing machine learning algorithms in computer science for drug repurposing

, 2021
Niela Rambocas, Claire B. Joseph
openalex   +2 more sources

Detecting Dengue in Flight: Leveraging Machine Learning to Analyze Mosquito Flight Patterns for Infection Detection

Advanced Biology, EarlyView.
Dengue infection alters mosquito flight behavior, enabling detection using machine learning classifiers. This study analyzes 3D flight trajectories and evaluates multiple models, showing that longer sequence lengths improve classification performance.
Nouman Javed, Adam J. López‐Denman, Prasad N. Paradkar, Asim Bhatti   +3 more
wiley   +1 more source