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981
Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging
Published 2025-05-01“…Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. …”
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982
PCCNN: A CNN classification model integrating EEG time-frequency features for stroke classification
Published 2025-01-01“…Each DWT and EMD feature is processed by an independent one-dimensional convolutional neural networks (1D-CNN) branch for targeted extraction. …”
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983
A GNSS-IR Soil Moisture Inversion Method Considering Multi-Factor Influences Under Different Vegetation Covers
Published 2025-04-01“…Additionally, the analysis of different surface types showed that the method achieved higher accuracy in grassland and open shrubland areas, with all models reaching R<sup>2</sup> values above 0.9. …”
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984
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985
Importance Analysis of Vegetation Change Factors in East Africa Based on Machine Learning
Published 2023-12-01“…[Objective] A factor importance analysis of vegetation changes in East Africa based on different machine learning algorithms was conducted to measure the accuracy and applicability of the different algorithms in order to provide a scientific basis for protecting, restoring, and promoting sustainable forest management and comprehensive prevention and control of soil erosion. …”
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986
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987
The potential role of synthetic computed tomography in spinal surgery: generation, applications, and implications for future clinical practice
Published 2024-12-01“…This qualitative literature review evaluated various sCT generation methods, encompassing traditional atlas-based and bulk-density models, as well as advanced convolutional neural network (CNN) architectures, including U-net, V-net, and generative adversarial network models. …”
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988
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989
Temporal Enhancement of Top-N Recommendation on Heterogeneous Graphs
Published 2025-04-01Get full text
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990
Fourier Neural Operator Networks for Solving Reaction–Diffusion Equations
Published 2024-11-01“…Next, we conducted experiments on the number of convolutional layers. The results showed that the performance of the models did not differ significantly when using two, three, or four layers, with the performance of two or three layers even slightly surpassing that of four layers. …”
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991
Deep hybrid architecture with stacked ensemble learning for binary classification of retinal disease
Published 2024-12-01“…Methods: This work experimented one hundred and forty-four different hybrid architectures amalgamating each of the eight convolutional neural architectures (VGG, EfficientNet, Inception, ResNet, NasNet, DenseNet, InceptionResNet, Xception) with seven classifiers (Logistic regression, K-Nearest Neighbours, Support Vector Classifier, Decision Tree, Bagging classifier, Random Forest, Adaptive Boosting, Light Gradient Boost and Extra tree classifier). …”
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992
CMENet: A Cross-Modal Enhancement Network for Tobacco Leaf Grading
Published 2023-01-01Get full text
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993
A Capsule Decision Neural Network Based on Transfer Learning for EEG Signal Classification
Published 2025-04-01Get full text
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994
GPR-Based Leakage Reconstruction of Shallow-Buried Water Supply Pipelines Using an Improved UNet++ Network
Published 2025-06-01“…The network employs an encoder–decoder architecture, in which the encoder incorporates multi-scale directional convolutions with coordinate attention to extract and compress features across different scales effectively. …”
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995
Prediction and Parameter Optimization of Surface Settlement Induced by Shield Tunneling Using Improved Informer Algorithm
Published 2025-06-01“…To address the limitations of the Informer algorithm in predicting surface settlement during shield tunneling, the standard convolution was replaced with dilated causal convolution, and three measures were employed: soil layer classification and characterization, a moving prediction window, and special factor handling. …”
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996
Remote Sensing Image-Based Building Change Detection: A Case Study of the Qinling Mountains in China
Published 2025-06-01“…With the widespread application of deep learning in Earth observation, remote sensing image-based building change detection has achieved numerous groundbreaking advancements. However, differences across time periods caused by temporal variations in land cover, as well as the complex spatial structures in remote sensing scenes, significantly constrain the performance of change detection. …”
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997
A Comprehensive Review on Unsupervised Domain Adaptation for 3D Segmentation and Reconstruction in CT Urography Imaging
Published 2023-12-01“…The most important part of this review is the discussion on 3D kidney segmentation and reconstruction from urographic images, which has helped doctors a lot with the accurate diagnosis and planning of treatment for kidney diseases. Even though 3D convolution networks have been used a lot in medical picture segmentation, it can be hard to adapt them to clinical data from different modalities that have not been seen before. …”
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998
A new CNN deep learning model for computer-intelligent color matching
Published 2025-05-01“…In practical applications, the model had an average color difference of only 0.51, 0.49, and 0.47 for the three primary colors of red, green, and blue, with small color differences and high color-matching accuracy. …”
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999
Automatic assessment of lower limb deformities using high-resolution X-ray images
Published 2025-05-01“…Methods The proposed approach uses a Convolutional Neural Network (CNN) that receives the raw X-ray image as input and produces the coordinates of the landmarks. …”
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1000
SpeakerNet for Cross-lingual Text-Independent Speaker Verification
Published 2020-11-01“…Extracted features from Siamese then can be classified using difference or correlation measures. We have implemented a customized scoring scheme that utilizes Siamese’ capability of applying distance measures with the convolutional learning. …”
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