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721
Synergistic 2D/3D Convolutional Neural Network for hyperspectral image classification
Published 2021“…The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. …”
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Journal Article -
722
Learning disentangled representation implicitly via transformer for occluded person re-identification
Published 2022“…To better eliminate interference from occlusions, we design a contrast feature learning technique (CFL) for better separation of occlusion features and discriminative ID features. …”
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Journal Article -
723
Fingerprint-enhanced graph attention network (FinGAT) model for antibiotic discovery
Published 2023“…In this paper, we propose a fingerprint-enhanced graph attention network (FinGAT) model by the combination of sequence-based 2D fingerprints and structure-based graph representation. In our feature learning process, sequence information is transformed into a fingerprint vector, and structural information is encoded through a GAT module into another vector. …”
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Journal Article -
724
Knowledge-based BERT word embedding fine-tuning for emotion recognition
Published 2023“…By combining the emotionally discriminative fine-tuned embedding with contextual information-rich embedding from pre-trained BERT model, the emotional features underlying the texts could be more effectively captured in the subsequent feature learning module, which in turn leads to improved emotion recognition performance. …”
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Journal Article -
725
BELMKN : Bayesian extreme learning machines Kohonen Network
Published 2018“…In the first level, the Extreme Learning Machine (ELM)-based feature learning approach captures the nonlinearity in the data distribution by mapping it onto a d-dimensional space. …”
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Journal Article -
726
Reconstructing 3D cardiac anatomies from misaligned multi-view magnetic resonance images with Mesh Deformation U-Nets
Published 2022“…Its architecture combines spectral graph convolutions and mesh sampling operations in a hierarchical encoder-decoder structure to enable efficient multi-scale feature learning directly on mesh data. A targeted preprocessing step approximately fits a template mesh to the sparse MRI contours, before the Mesh Deformation U-Net corrects for motion-induced slice misalignment by simultaneously utilising information from multiple MRI views and the template-induced anatomical shape prior. …”
Conference item -
727
Domain adaptation : methods and applications
Published 2019“…First of all, we develop methods, specifically, one deep feature learning method and one subspace-based method, for homogeneous problem settings. …”
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Thesis -
728
Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis
Published 2021“…A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. …”
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Article -
729
3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
Published 2024“…With its improved accessibility in the recent years, the advent of deep learning had allowed feature learning from sparse 3D point clouds. Hence, this leads a plethora of methods in object detection for 3D sparse point clouds. …”
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Conference or Workshop Item -
730
Distributed denial of service attack Detection in IoT networks using deep learning and feature fusion : A review
Published 2024“…The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. …”
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Article -
731
Distributed Denial of Service Attack Detection in IoT Networks using Deep Learning and Feature Fusion: A Review
Published 2024“…The deep learning technique shows great promise because automatic feature learning capabilities are well suited for the complex and high-dimensional data of IoT systems. …”
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Article -
732
A novel medical image segmentation approach by using multi-branch segmentation network based on local and global information synchronous learning
Published 2023-04-01“…Abstract In recent years, there have been several solutions to medical image segmentation, such as U-shaped structure, transformer-based network, and multi-scale feature learning method. However, their network parameters and real-time performance are often neglected and cannot segment boundary regions well. …”
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Article -
733
A neural network approach for short-term water demand forecasting based on a sparse autoencoder
Published 2023-01-01“…In this method, the SAE is used as a feature learning method to extract useful information from hourly water demand data in an unsupervised manner. …”
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Article -
734
V2PNet: Voxel-to-Point Feature Propagation and Fusion That Improves Feature Representation for Point Cloud Registration
Published 2023-01-01“…However, although point-based methods are geometrically precise, the discrete nature of point clouds negatively affects feature learning performance. Moreover, although voxel-based methods can exploit the learning power of convolutional neural networks, their resolution and detail extraction may be inadequate. …”
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Article -
735
Customized 2D CNN Model for the Automatic Emotion Recognition Based on EEG Signals
Published 2023-05-01“…In this research, different feature learning and hand-crafted feature selection/extraction algorithms were investigated and compared with each other in order to classify emotions. …”
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Article -
736
Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
Published 2023-10-01“…Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. …”
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Article -
737
Smoking behavior detection algorithm based on YOLOv8-MNC
Published 2023-08-01“…The YOLOv8-MNC algorithm employs three key strategies: (1) It utilizes NWD Loss to mitigate the effects of minor deviations in object positions on IoU, thereby enhancing training accuracy; (2) It incorporates the Multi-head Self-Attention Mechanism (MHSA) to bolster the network’s global feature learning capacity; and (3) It implements the lightweight general up-sampling operator CARAFE, in place of conventional nearest-neighbor interpolation up-sampling modules, minimizing feature information loss during the up-sampling process.ResultsExperimental results from a customized smoking behavior dataset demonstrate significant improvement in detection accuracy. …”
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Article -
738
MSGNN-DTA: Multi-Scale Topological Feature Fusion Based on Graph Neural Networks for Drug–Target Binding Affinity Prediction
Published 2023-05-01“…To address the challenge of accurately extracting drug and target protein features, we introduce a gated skip-connection mechanism during the feature learning process to fuse multi-scale topological features, resulting in information-rich representations of drugs and proteins. …”
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Article -
739
Vehicle Classification Using Deep Feature Fusion and Genetic Algorithms
Published 2023-01-01“…After performing appropriate data preparation and preprocessing steps, feature learning and extraction is carried out using pre-trained VGG16 first that learns and extracts deep features from the set of input images. …”
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Article -
740
Effectiveness of Human–Artificial Intelligence Collaboration in Cephalometric Landmark Detection
Published 2022-03-01“…We selected 1193 cephalograms and used them to train the deep anatomical context feature learning (DACFL) model. The number of target landmarks was 41. …”
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Article