AIMC Topic: Neural Networks, Computer

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A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network.

Journal of digital imaging
Maximum intensity projection (MIP) technology is a computer visualization method that projects three-dimensional spatial data on a visualization plane. According to the specific purposes, the specific lab thickness and direction can be selected. This...

Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning.

Journal of applied clinical medical physics
PURPOSE: The deformable registration of 3D chest computed tomography (CT) images is one of the most important tasks in the field of medical image registration. However, the nonlinear deformation and large-scale displacement of lung tissues caused by ...

Neural-Network Based Modeling of I/O Buffer Predriver under Power/Ground Supply Voltage Variations.

Sensors (Basel, Switzerland)
This paper presents a neural-network based nonlinear behavioral modelling of I/O buffer that accounts for timing distortion introduced by nonlinear switching behavior of the predriver electrical circuit under power and ground supply voltage (PGSV) va...

Hierarchical Pooling in Graph Neural Networks to Enhance Classification Performance in Large Datasets.

Sensors (Basel, Switzerland)
Deep learning methods predicated on convolutional neural networks and graph neural networks have enabled significant improvement in node classification and prediction when applied to graph representation with learning node embedding to effectively re...

Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults.

Sensors (Basel, Switzerland)
Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe ope...

Depth Estimation from Light Field Geometry Using Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Depth estimation based on light field imaging is a new methodology that has succeeded the traditional binocular stereo matching and depth from monocular images. Significant progress has been made in light-field depth estimation. Nevertheless, the bal...

NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data.

Communications biology
Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly challenging. This challenge is primarily due to three dominant factors: data accuracy, data scarceness, and problem complexity. Here, we showcase that "shallow...

Image Features of Magnetic Resonance Angiography under Deep Learning in Exploring the Effect of Comprehensive Rehabilitation Nursing on the Neurological Function Recovery of Patients with Acute Stroke.

Contrast media & molecular imaging
This study was to explore the effects of imaging characteristics of magnetic resonance angiography (MRA) based on deep learning on the comprehensive rehabilitation nursing on the neurological recovery of patients with acute stroke. In this study, 84 ...

Relation classification via BERT with piecewise convolution and focal loss.

PloS one
Recent relation extraction models' architecture are evolved from the shallow neural networks to natural language model, such as convolutional neural networks or recurrent neural networks to Bert. However, these methods did not consider the semantic i...

BERTtoCNN: Similarity-preserving enhanced knowledge distillation for stance detection.

PloS one
In recent years, text sentiment analysis has attracted wide attention, and promoted the rise and development of stance detection research. The purpose of stance detection is to determine the author's stance (favor or against) towards a specific targe...