Results 61 to 70 of about 4,712,882 (251)

Evolutionary Artificial Neural Networks in Neutron Spectrometry [PDF]

, 2010
Artificial Neural Networks (ANN), are highly simplified models of the brain processes (Graupe, 2007; Kasabov, 1998). AnANNis a biologically inspired computational model which consists of a large number of simple processing elements called neurons ...
Vega Carrillo, Héctor René, Martínez Blanco, María del Rosario, Ortíz Rodríguez, José Manuel   +2 more
core   +1 more source

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

Clinical Validation of Artificial Intelligence (AI)‐based Cartilage Segmentation Predicting Knee Replacement

Arthritis Care &Research, Accepted Article.
Objective For cartilage morphology to serve as a scalable endpoint in clinical trials, analyses should be performed automatically without human interaction. To clinically validate artificial intelligence (AI)‐based analysis, we studied cartilage loss from MRI prior to knee replacement.
Felix Eckstein, David J. Hunter, C. Kent Kwoh, Wolfgang Wirth   +3 more
wiley   +1 more source

Research Progress of Oilfield Development Index Prediction Based on Artificial Neural Networks

Energies, 2021
Accurately predicting oilfield development indicators (such as oil production, liquid production, current formation pressure, water cut, oil production rate, recovery rate, cost, profit, etc.) is to realize the rational and scientific development of ...
Chenglong Chen, Yikun Liu, Decai Lin, Guohui Qu, Jiqiang Zhi, Shuang Liang, Fengjiao Wang, Dukui Zheng, Anqi Shen, Lifeng Bo, Shiwei Zhu   +10 more
doaj   +1 more source

Dead-end filtration of yeast suspensions: correlating specific resistance and flux data using artificial neural networks [PDF]

, 2006
The specific cake resistance in dead-end filtration is a complex function of suspension properties and operating conditions. In this study, the specific resistance of resuspended dried bakers yeast suspensions was measured in a series of 150 experiments ...
Ní Mhurchú, Jenny, Foley, Greg
core   +2 more sources

Beyond Visual Scoring: Computational Computed Tomography Analysis for High‐Resolution Computed Tomography–Based Quantification of Interstitial Lung Disease in Inflammatory Rheumatic Disease

Arthritis Care &Research, EarlyView.
Interstitial lung disease (ILD) is a significant cause of morbidity and mortality in patients with inflammatory rheumatic disorders (IRDs). High‐resolution computed tomography (HRCT) is widely considered the gold standard for the noninvasive assessment of ILD; however, its interpretation is constrained by substantial interobserver variability and the ...
Alexander Pfeil, Tobias Hoffmann, Ulf Teichgräber, Diane Renz, Denise Beckmann, Gunter Wolf, Nicole Schadewaldt, Bianca Lassen‐Schmidt   +7 more
wiley   +1 more source

Automated Hand Flexor Tendon–Thickness Measurement in Systemic Sclerosis

Arthritis Care &Research, EarlyView.
Objective Systemic sclerosis (SSc) can affect flexor tendons, contributing to hand function problems and reduced quality of life. Tendon changes are currently assessed with ultrasonography and measured manually, a time‐consuming process prone to interobserver variability.
Mark Greveling, Ties van het Erve, Yvette van de Berg, Simon Mastbergen, Julia Spierings   +4 more
wiley   +1 more source

Evolving artificial neural networks [PDF]

Proceedings of the IEEE, 1999
Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures ...
openaire   +2 more sources

Analysis of Artificial Neural-Network [PDF]

International Journal of Trend in Scientific Research and Development, 2018
An Artificial Neural Network ANN is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing ...
Rajesh CVS, Nadikoppula Pardhasaradhi
openaire   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source