AIMC Topic: Neural Networks, Computer

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Bioinspired Coordinated Path Following for Vessels with Speed Saturation Based on Virtual Leader.

Computational intelligence and neuroscience
This paper investigates the coordinated path following of multiple marine vessels with speed saturation. Based on virtual leader strategy, the authors show how the neural dynamic model and passivity-based techniques are brought together to yield a di...

Genome-enabled prediction using probabilistic neural network classifiers.

BMC genomics
BACKGROUND: Multi-layer perceptron (MLP) and radial basis function neural networks (RBFNN) have been shown to be effective in genome-enabled prediction. Here, we evaluated and compared the classification performance of an MLP classifier versus that o...

Modelling the removal of volatile pollutants under transient conditions in a two-stage bioreactor using artificial neural networks.

Journal of hazardous materials
A two-stage biological waste gas treatment system consisting of a first stage biotrickling filter (BTF) and second stage biofilter (BF) was tested for the removal of a gas-phase methanol (M), hydrogen sulphide (HS) and α-pinene (P) mixture. The biore...

High-Performance Personalized Heartbeat Classification Model for Long-Term ECG Signal.

IEEE transactions on bio-medical engineering
Long-term electrocardiogram (ECG) has become one of the important diagnostic assist methods in clinical cardiovascular domain. Long-term ECG is primarily used for the detection of various cardiovascular diseases that are caused by various cardiac arr...

Marginal Space Deep Learning: Efficient Architecture for Volumetric Image Parsing.

IEEE transactions on medical imaging
Robust and fast solutions for anatomical object detection and segmentation support the entire clinical workflow from diagnosis, patient stratification, therapy planning, intervention and follow-up. Current state-of-the-art techniques for parsing volu...

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

IEEE transactions on medical imaging
Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that ha...

Feature-Linking Model for Image Enhancement.

Neural computation
Inspired by gamma-band oscillations and other neurobiological discoveries, neural networks research shifts the emphasis toward temporal coding, which uses explicit times at which spikes occur as an essential dimension in neural representations. We pr...

Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images.

IEEE transactions on medical imaging
Among brain tumors, gliomas are the most common and aggressive, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of oncological patients. Magnetic resonance imaging...

Paraconsistents artificial neural networks applied to the study of mutational patterns of the F subtype of the viral strains of HIV-1 to antiretroviral therapy.

Anais da Academia Brasileira de Ciencias
The high variability of HIV-1 as well as the lack of efficient repair mechanisms during the stages of viral replication, contribute to the rapid emergence of HIV-1 strains resistant to antiretroviral drugs. The selective pressure exerted by the drug ...

Salmonella infections modelling in Mississippi using neural network and geographical information system (GIS).

BMJ open
OBJECTIVES: Mississippi (MS) is one of the southern states with high rates of foodborne infections. The objectives of this paper are to determine the extent of Salmonella and Escherichia coli infections in MS, and determine the Salmonella infections ...