AIMC Topic: Neural Networks, Computer

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Development of an artificial neural network as a tool for predicting the chemical attributes of fresh peach fruits.

PloS one
This investigation aimed to develop a method to predict the total soluble solids (TSS), titratable acidity, TSS/titratable acidity, vitamin C, anthocyanin, and total carotenoids contents using surface color values (L*, Hue and chroma), single fruit w...

Estimating Human Pose Efficiently by Parallel Pyramid Networks.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Good performance and high efficiency are both critical for estimating human pose in practice. Recent state-of-the-art methods have greatly boosted the pose detection accuracy through deep convolutional neural networks, however, the strong performance...

CommPOOL: An interpretable graph pooling framework for hierarchical graph representation learning.

Neural networks : the official journal of the International Neural Network Society
Recent years have witnessed the emergence and flourishing of hierarchical graph pooling neural networks (HGPNNs) which are effective graph representation learning approaches for graph level tasks such as graph classification. However, current HGPNNs ...

Application of a Poisson deep neural network model for the prediction of count data in genome-based prediction.

The plant genome
Genomic selection (GS) is revolutionizing conventional ways of developing new plants and animals. However, because it is a predictive methodology, GS strongly depends on statistical and machine learning to perform these predictions. For continuous ou...

Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging.

Human brain mapping
The advent of susceptibility-sensitive MRI techniques, such as susceptibility weighted imaging (SWI), has enabled accurate in vivo visualization and quantification of iron deposition within the human brain. Although previous approaches have been intr...

Deep ReLU neural networks in high-dimensional approximation.

Neural networks : the official journal of the International Neural Network Society
We study the computation complexity of deep ReLU (Rectified Linear Unit) neural networks for the approximation of functions from the Hölder-Zygmund space of mixed smoothness defined on the d-dimensional unit cube when the dimension d may be very larg...

COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network.

Journal of infection and public health
BACKGROUND: COVID-19 diagnosis in symptomatic patients is an important factor for arranging the necessary lifesaving facilities like ICU care and ventilator support. For this purpose, we designed a computer-aided diagnosis and severity detection meth...

TEM virus images: Benchmark dataset and deep learning classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: To achieve the full potential of deep learning (DL) models, such as understanding the interplay between model (size), training strategy, and amount of training data, researchers and developers need access to new dedicated im...

Sharp U-Net: Depthwise convolutional network for biomedical image segmentation.

Computers in biology and medicine
The U-Net architecture, built upon the fully convolutional network, has proven to be effective in biomedical image segmentation. However, U-Net applies skip connections to merge semantically different low- and high-level convolutional features, resul...

Software-Based Method for Automated Segmentation and Measurement of Wounds on Photographs Using Mask R-CNN: a Validation Study.

Journal of digital imaging
In clinical routine, wound documentation is one of the most important contributing factors to treating patients with acute or chronic wounds. The wound documentation process is currently very time-consuming, often examiner-dependent, and therefore im...