AIMC Topic: Neural Networks, Computer

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Prediction of sediment heavy metal at the Australian Bays using newly developed hybrid artificial intelligence models.

Environmental pollution (Barking, Essex : 1987)
Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is propo...

Analysis of the transferability and robustness of GANs evolved for Pareto set approximations.

Neural networks : the official journal of the International Neural Network Society
The generative adversarial network (GAN) is a good example of a strong-performing, neural network-based generative model, even though it does have some drawbacks of its own. Mode collapsing and the difficulty in finding the optimal network structure ...

Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks.

Clinical journal of the American Society of Nephrology : CJASN
BACKGROUND AND OBJECTIVES: Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective tool in the elaboration of medical imaging. We wanted to evaluate the role of a convolutional neura...

Emulating Docking Results Using a Deep Neural Network: A New Perspective for Virtual Screening.

Journal of chemical information and modeling
Docking is one of the most important steps in virtual screening pipelines, and it is an established method for examining potential interactions between ligands and receptors. However, this method is computationally expensive, and it is often among th...

CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification.

BMC bioinformatics
BACKGROUND: Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization...

DeepHE: Accurately predicting human essential genes based on deep learning.

PLoS computational biology
Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods ...

Modeling adult skeletal stem cell response to laser-machined topographies through deep learning.

Tissue & cell
The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a deep neural network. The network is capable of predicting cell response to a statistically signific...

A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction.

Computer methods and programs in biomedicine
BACKGROUND AND AIM: deep learning algorithms have not been successfully used for the left ventricle (LV) detection in echocardiographic images due to overfitting and vanishing gradient descent problem. This research aims to increase accuracy and impr...

Boundary Mittag-Leffler stabilization of fractional reaction-diffusion cellular neural networks.

Neural networks : the official journal of the International Neural Network Society
Mittag-Leffler stabilization is studied for fractional reaction-diffusion cellular neural networks (FRDCNNs) in this paper. Different from previous literature, the FRDCNNs in this paper are high-dimensional systems, and boundary control and observed-...