AIMC Topic: Neural Networks, Computer

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Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent.

International journal of molecular sciences
Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. T...

OrgaQuant: Human Intestinal Organoid Localization and Quantification Using Deep Convolutional Neural Networks.

Scientific reports
Organoid cultures are proving to be powerful in vitro models that closely mimic the cellular constituents of their native tissue. Organoids are typically expanded and cultured in a 3D environment using either naturally derived or synthetic extracellu...

Metabolic perceptrons for neural computing in biological systems.

Nature communications
Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing usi...

Neural computational model GrowthEstimate: A model for studying living resources through digestive efficiency.

PloS one
The neural computational model GrowthEstimate is introduced with focusing on new perspectives for the practical estimation of weight specific growth rate (SGR, % day-1). It is developed using recurrent neural networks of reservoir computing type, for...

Asymptotic Soft Filter Pruning for Deep Convolutional Neural Networks.

IEEE transactions on cybernetics
Deeper and wider convolutional neural networks (CNNs) achieve superior performance but bring expensive computation cost. Accelerating such overparameterized neural network has received increased attention. A typical pruning algorithm is a three-stage...

A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning.

IEEE journal of biomedical and health informatics
Automatic sleep staging methods usually extract hand-crafted features or network trained features from signals recorded by polysomnography (PSG), and then estimate the stages by various classifiers. In this study, we propose a classification approach...

Transformed ℓ regularization for learning sparse deep neural networks.

Neural networks : the official journal of the International Neural Network Society
Deep Neural Networks (DNNs) have achieved extraordinary success in numerous areas. However, DNNs often carry a large number of weight parameters, leading to the challenge of heavy memory and computation costs. Overfitting is another challenge for DNN...

Technical Note: PYRO-NN: Python reconstruction operators in neural networks.

Medical physics
PURPOSE: Recently, several attempts were conducted to transfer deep learning to medical image reconstruction. An increasingly number of publications follow the concept of embedding the computed tomography (CT) reconstruction as a known operator into ...

Active deep learning for the identification of concepts and relations in electroencephalography reports.

Journal of biomedical informatics
The identification of medical concepts, their attributes and the relations between concepts in a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. However, th...

Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained t...