AIMC Topic: Neural Networks, Computer

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GMLM-CNN: A Hybrid Solution to SWIR-VIS Face Verification with Limited Imagery.

Sensors (Basel, Switzerland)
Cross-spectral face verification between short-wave infrared (SWIR) and visible light (VIS) face images poses a challenge, which is motivated by various real-world applications such as surveillance at night time or in harsh environments. This paper p...

Comparative Study between Physics-Informed CNN and PCA in Induction Motor Broken Bars MCSA Detection.

Sensors (Basel, Switzerland)
In this article, two methods for broken bar detection in induction motors are considered and tested using data collected from the LIAS laboratory at the University of Poitiers. The first approach is Motor Current Signature Analysis (MCSA) with Convol...

A Survey on Graph Neural Networks for Microservice-Based Cloud Applications.

Sensors (Basel, Switzerland)
Graph neural networks (GNNs) have achieved great success in many research areas ranging from traffic to computer vision. With increased interest in cloud-native applications, GNNs are increasingly being investigated to address various challenges in m...

GC-MLP: Graph Convolution MLP for Point Cloud Analysis.

Sensors (Basel, Switzerland)
With the objective of addressing the problem of the fixed convolutional kernel of a standard convolution neural network and the isotropy of features making 3D point cloud data ineffective in feature learning, this paper proposes a point cloud process...

An Efficient Approach to Large-Scale Ab Initio Conformational Energy Profiles of Small Molecules.

Molecules (Basel, Switzerland)
Accurate conformational energetics of molecules are of great significance to understand maby chemical properties. They are also fundamental for high-quality parameterization of force fields. Traditionally, accurate conformational profiles are obtaine...

Long-distance dependency combined multi-hop graph neural networks for protein-protein interactions prediction.

BMC bioinformatics
BACKGROUND: Protein-protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of...

A fully-automated paper ECG digitisation algorithm using deep learning.

Scientific reports
There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECG...

Automatic lower limb bone segmentation in radiographs with different orientations and fields of view based on a contextual network.

International journal of computer assisted radiology and surgery
PURPOSE: Bone identification and segmentation in X-ray images are crucial in orthopedics for the automation of clinical procedures, but it often involves some manual operations. In this work, using a modified SegNet neural network, we automatically i...

Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques.

Risk analysis : an official publication of the Society for Risk Analysis
Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more p...

A two-step downscaling method for high-scale super-resolution of daily temperature - a case study of Wei River Basin, China.

Environmental science and pollution research international
Climate data with high spatial and temporal resolution were of great significance for regional environmental management, such as for early response to possible predicted local climate changes and extreme weather. However, the current downscaling targ...