AIMC Topic: Neural Networks, Computer

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Artificial neural network modelling for biodecolorization of Basic Violet 03 from aqueous solution by biochar derived from agro-bio waste of groundnut hull: Kinetics and thermodynamics.

Chemosphere
In this study, Levenberg Marquardt back propagation algorithm was used to train the Artificial Neural Network (ANN) and to predict the adsorptive removal of cationic dye Basic Violet 03 (BV03) by biochar derived from biowaste of groundnut hull. The e...

Modeling of photolytic degradation of sulfamethoxazole using boosted regression tree (BRT), artificial neural network (ANN) and response surface methodology (RSM); energy consumption and intermediates study.

Chemosphere
This study explores the boosted regression trees (BRT), artificial neural network (ANN) and response surface methodology (RSM) to model and optimize the operational variables for the simulation of the Photolytic degradation of Sulfamethoxazole (SMX) ...

Long-term Cognitive Network-based architecture for multi-label classification.

Neural networks : the official journal of the International Neural Network Society
This paper presents a neural system to deal with multi-label classification problems that might involve sparse features. The architecture of this model involves three sequential blocks with well-defined functions. The first block consists of a multil...

TractLearn: A geodesic learning framework for quantitative analysis of brain bundles.

NeuroImage
Deep learning-based convolutional neural networks have recently proved their efficiency in providing fast segmentation of major brain fascicles structures, based on diffusion-weighted imaging. The quantitative analysis of brain fascicles then relies ...

Improved motor outcome prediction in Parkinson's disease applying deep learning to DaTscan SPECT images.

Computers in biology and medicine
PURPOSE: Dopamine transporter (DAT) SPECT imaging is routinely used in the diagnosis of Parkinson's disease (PD). Our previous efforts demonstrated the use of DAT SPECT images in a data-driven manner by improving prediction of PD clinical assessment ...

A novel lightweight deep convolutional neural network for early detection of oral cancer.

Oral diseases
OBJECTIVES: To develop a lightweight deep convolutional neural network (CNN) for binary classification of oral lesions into benign and malignant or potentially malignant using standard real-time clinical images.

Empirical strategy for stretching probability distribution in neural-network-based regression.

Neural networks : the official journal of the International Neural Network Society
In regression analysis under artificial neural networks, the prediction performance depends on determining the appropriate weights between layers. As randomly initialized weights are updated during back-propagation using the gradient descent procedur...

Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Journal of the American Heart Association
Background Because chest compressions induce artifacts in the ECG, current automated external defibrillators instruct the user to stop cardiopulmonary resuscitation (CPR) while an automated rhythm analysis is performed. It has been shown that minimiz...