Results 61 to 70 of about 2,288,742 (339)
Gating Artificial Neural Network Based Soft Sensor [PDF]
, 2008 This work proposes a novel approach to Soft Sensor modelling, where the Soft Sensor is built by a set of experts which are artificial neural networks with randomly generated topology.
Gabrys, Bogdan, Kadlec, Petr
core
A Hardware Implementation of Artificial Neural Network Using Field Programmable Gate Arrays [PDF]
, 2007 An artificial neural network algorithm is implemented using a field programmable gate array hardware. One hidden layer is used in the feed-forward neural network structure in order to discriminate one class of patterns from the other class in real time ...
Badala +10 more
core +2 more sources
Pancreatic Cancer Prediction Through an Artificial Neural Network
Frontiers in Artificial Intelligence, 2019 Early detection of pancreatic cancer is challenging because cancer-specific symptoms occur only at an advanced stage, and a reliable screening tool to identify high-risk patients is lacking.
W. Muhammad +6 more
semanticscholar +1 more source
Molecular Oncology, EarlyView.Etoposide induces DNA damage, activating p53‐dependent apoptosis via caspase‐3/7, which cleaves PARP1. Dammarenediol II enhances this apoptotic pathway by suppressing O‐GlcNAc transferase activity, further decreasing O‐GlcNAcylation. The reduction in O‐GlcNAc levels boosts p53‐driven apoptosis and influences the Akt/GSK3β/mTOR signaling pathway ...
Jaehoon Lee +8 more
wiley +1 more source
Development of neural network model of disc brake operation [PDF]
FME Transactions, 2010 The quality of artificial neural network models mostly depends on a proper setting of neural network architecture i.e. learning algorithm, transfer functions, range and distribution of data used for training, validation, and testing, etc.
Ćirović Velimir, Aleksendrić Dragan
doaj
New Journal of Physics, 2020 We construct an artificial neural network to study the pairing symmetries in disordered superconductors. For Hamiltonians on square lattice with s-wave, d-wave, and nematic pairing potentials, we use the spin-polarized local density of states near a ...
Liang Chen +3 more
doaj +1 more source
A multi-stage recurrent neural network better describes decision-related activity in dorsal premotor cortex [PDF]
, 2019 We studied how a network of recurrently connected artificial units solve a visual perceptual decision-making task. The goal of this task is to discriminate the dominant color of a central static checkerboard and report the decision with an arm ...
Chandrasekaran, Chandramouli +2 more
core +1 more source
Diffusion Tractography Biomarker for Epilepsy Severity in Children With Drug‐Resistant Epilepsy
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To develop a novel deep‐learning model of clinical DWI tractography that can accurately predict the general assessment of epilepsy severity (GASE) in pediatric drug‐resistant epilepsy (DRE) and test if it can screen diverse neurocognitive impairments identified through neuropsychological assessments.
Jeong‐Won Jeong +7 more
wiley +1 more source
Applications of artificial neural networks for enhanced livestock productivity: A review
Indian Journal of Animal Sciences, 2016 Artificial neural network models are machine-learning systems, a type of artificial intelligence. They have been inspired by and developed along the working principles of the human brain and its nerve cells.
V B DONGRE, R S GANDHI
doaj +1 more source
Prediction of subsidence due to underground mining by artificial neural networks [PDF]
, 2003 Alternatively to empirical prediction methods, methods based on influential functions and on mechanical model, artificial neural networks (ANNs) can be used for the surface subsidence prediction.
Ambrožič, Tomaž, Turk, Goran
core +1 more source