AIMC Topic: Neural Networks, Computer

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Approximation rates for neural networks with general activation functions.

Neural networks : the official journal of the International Neural Network Society
We prove some new results concerning the approximation rate of neural networks with general activation functions. Our first result concerns the rate of approximation of a two layer neural network with a polynomially-decaying non-sigmoidal activation ...

Macroscale and microcircuit dissociation of focal and generalized human epilepsies.

Communications biology
Thalamo-cortical pathology plays key roles in both generalized and focal epilepsies, but there is little work directly comparing these syndromes at the level of whole-brain mechanisms. Using multimodal imaging, connectomics, and computational simulat...

SS-SWT and SI-CNN: An Atrial Fibrillation Detection Framework for Time-Frequency ECG Signal.

Journal of healthcare engineering
Atrial fibrillation is the most common arrhythmia and is associated with high morbidity and mortality from stroke, heart failure, myocardial infarction, and cerebral thrombosis. Effective and rapid detection of atrial fibrillation is critical to redu...

Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder.

Computational and mathematical methods in medicine
Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specif...

Prediction of the Influential Factors on Eating Behaviors: A Hybrid Model of Structural Equation Modelling-Artificial Neural Networks.

TheScientificWorldJournal
The importance of eating behavior risk factors in the primary prevention of obesity has been established. Researchers mostly use the linear model to determine associations among these risk factors. However, in reality, the presence of nonlinearity am...

ToyArchitecture: Unsupervised learning of interpretable models of the environment.

PloS one
Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations. In this paper we a...

Prediction of the output factor using machine and deep learning approach in uniform scanning proton therapy.

Journal of applied clinical medical physics
PURPOSE: The purpose of this work is to develop machine and deep learning-based models to predict output and MU based on measured patient quality assurance (QA) data in uniform scanning proton therapy (USPT).

Spectral resolution of quaternary components in a sinus and congestion mixture; Multivariate algorithms to approach extremes of concentration levels.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Sinus and congestion mixture of three drugs and an impurity was studied for their spectral resolution using four multivariate algorithms. The studied drugs present in extremes of low and high concentrations. Low concentration levels of phenylephrine ...

Embedding and approximation theorems for echo state networks.

Neural networks : the official journal of the International Neural Network Society
Echo State Networks (ESNs) are a class of single-layer recurrent neural networks that have enjoyed recent attention. In this paper we prove that a suitable ESN, trained on a series of measurements of an invertible dynamical system, induces a C map fr...

Channel width optimized neural networks for liver and vessel segmentation in liver iron quantification.

Computers in biology and medicine
INTRODUCTION: MRI T2* relaxometry protocols are often used for Liver Iron Quantification in patients with hemochromatosis. Several methods exist to semi-automatically segment parenchyma and exclude vessels for this calculation.