AIMC Topic: Neural Networks, Computer

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Signed Curvature Graph Representation Learning of Brain Networks for Brain Age Estimation.

IEEE journal of biomedical and health informatics
Graph Neural Networks (GNNs) play a pivotal role in learning representations of brain networks for estimating brain age. However, the over-squashing impedes interactions between long-range nodes, hindering the ability of message-passing mechanism-bas...

Fully Convolutional Hybrid Fusion Network With Heterogeneous Representations for Identification of S1 and S2 From Phonocardiogram.

IEEE journal of biomedical and health informatics
Heart auscultation is a simple and inexpensive first-line diagnostic test for the early screening of heart abnormalities. A phonocardiogram (PCG) is a digital recording of an analog heart sound acquired using an electronic stethoscope. A computerized...

Identifying Associations Between Small Nucleolar RNAs and Diseases via Graph Convolutional Network and Attention Mechanism.

IEEE journal of biomedical and health informatics
Research has shown that small nucleolar RNAs (snoRNAs) play crucial roles in various biological processes, and understanding disease pathogenesis by studying their relationship with diseases is beneficial. Currently, known associations are insufficie...

tDKI-Net: A Joint q-t Space Learning Network for Diffusion-Time-Dependent Kurtosis Imaging.

IEEE journal of biomedical and health informatics
Time-dependent diffusion magnetic resonance imaging (TDDMRI) is useful for the non-invasive characterization of tissue microstructure. These models require densely sampled q-t space data for microstructural fitting, leading to very time-consuming acq...

Research on intelligent decision support systems for oil and gas exploration based on machine learning.

PloS one
The process of extracting oil and gas via borehole drilling is largely dependent on subsurface structures, and thus, well log analysis is a major concern for economic feasibility. Well logs are essential for understanding the geology below the earth'...

geodl: An R package for geospatial deep learning semantic segmentation using torch and terra.

PloS one
Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are es...

Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification.

PloS one
Tomato cultivation is expanding rapidly, but the tomato sector faces significant challenges from various sources, including environmental (abiotic stress) and biological (biotic stress or disease) threats, which adversely impact the crop's growth, re...

Exploiting the features of deep residual network with SVM classifier for human posture recognition.

PloS one
Over the last decade, there have been a lot of advances in the area of human posture recognition. Among multiple approaches proposed to solve this problem, those based on deep learning have shown promising results. Taking another step in this directi...

Enhancing land cover object classification in hyperspectral imagery through an efficient spectral-spatial feature learning approach.

PloS one
The classification of land cover objects in hyperspectral imagery (HSI) has significantly advanced due to the development of convolutional neural networks (CNNs). However, challenges such as limited training data and high dimensionality negatively im...

SE-MAConvLSTM: A deep learning framework for short-term traffic flow prediction combining Squeeze-and-Excitation Network and Multi-Attention Convolutional LSTM Network.

PloS one
Traffic flow prediction is an important part of transportation management and planning. For example, accurate demand prediction of taxis and online car-hailing can reduce the waste of resources caused by empty cars. The prediction of public bicycle f...