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901
An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks
Published 2020-12-01“…In particular, the proposed system is composed of three subsystems: a feature engineering subsystem, a feature learning subsystem, and a traffic classification subsystem. …”
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902
Integration of Multi-Head Self-Attention and Convolution for Person Re-Identification
Published 2022-08-01“…We propose an improved multi-scale feature learning structure, DM-OSNet, with better performance than the original OSNet. …”
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903
Classification of Typical Static Objects in Road Scenes Based on LO-Net
Published 2024-02-01“…Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. …”
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904
A Review of Deep Learning in Multiscale Agricultural Sensing
Published 2022-01-01“…In the past few years, deep learning (DL) has shown great potential to reshape many industries because of its powerful capabilities of feature learning from massive datasets, and the agriculture industry is no exception. …”
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905
EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5
Published 2023-02-01“…Therefore, an efficient and accurate model for apple leaf disease detection based on YOLOv5 is proposed and named EADD-YOLO.MethodsIn the EADD-YOLO, the lightweight shufflenet inverted residual module is utilized to reconstruct the backbone network, and an efficient feature learning module designed through depthwise convolution is proposed and introduced to the neck network. …”
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906
Domain-Generalized EEG Classification With Category-Oriented Feature Decorrelation and Cross-View Consistency Learning
Published 2023-01-01“…To this end, we propose a novel domain-generalized EEG classification framework, named FDCL, to generalize EEG decoding through category-relevant and -irrelevant Feature Decorrelation and Cross-view invariant feature Learning. Specifically, we first devise data augmented regularization through mixing the segments of same-category features from multiple subjects, which increases the diversity of EEG data by spanning the space of subjects. …”
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907
Semantic Segmentation of Agricultural Images Based on Style Transfer Using Conditional and Unconditional Generative Adversarial Networks
Published 2022-08-01“…Using these networks, the proposed framework provides not only image generation for data augmentation, but also automatic labeling based on distinctive feature learning among domains. The Fréchet inception distance (FID) and mean intersection over union (mIoU) were used, respectively, as evaluation metrics for GANs and semantic segmentation. …”
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908
Multiscale Adjacency Matrix CNN: Learning on Multispectral LiDAR Point Cloud via Multiscale Local Graph Convolution
Published 2024-01-01“…The network effectively captures global and local representative features of the point cloud by harnessing the capabilities of convolutional neural networks in local feature modeling and the self-attention mechanism in global semantic feature learning. Experimental results on the Titan dataset demonstrate that the proposed MS-AMCNN network achieves a promising multispectral LiDAR point cloud segmentation performance with an overall accuracy of 94.39% and a mean intersection over union (MIoU) of 86.57%. …”
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909
Exploring Deep Physiological Models for Nociceptive Pain Recognition
Published 2019-10-01“…In contrast to previous works, which rely on carefully designed sets of hand-crafted features, the current work aims at building competitive pain intensity inference models through autonomous feature learning, based on deep neural networks. The assessment of the designed deep learning architectures is based on the <i>BioVid Heat Pain Database (Part A)</i> and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous methods reported in the literature, with respective average performances of <inline-formula> <math display="inline"> <semantics> <mrow> <mn>84.57</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> and <inline-formula> <math display="inline"> <semantics> <mrow> <mn>84.40</mn> <mo>%</mo> </mrow> </semantics> </math> </inline-formula> for the binary classification experiment consisting of the discrimination between the baseline and the pain tolerance level (<inline-formula> <math display="inline"> <semantics> <msub> <mi>T</mi> <mn>0</mn> </msub> </semantics> </math> </inline-formula> vs. …”
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910
Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image
Published 2023-11-01“…In this research, a deep learning approach is introduced for segmenting acute and sub-acute stroke lesions from MRI images. To enhance feature learning through brain hemisphere symmetry, pre-processing techniques are applied to the data. …”
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911
Detail Enhanced Change Detection in VHR Images Using a Self-Supervised Multiscale Hybrid Network
Published 2024-01-01“…In addition, we design a mixed feature pyramid within the DST, which provides interlayer interaction information and intralayer multiscale information for a more complete feature learning within the new network. Furthermore, we impose a self-supervised strategy to guide the VGG16 provide the semantic change information from the output features of the encoder. …”
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912
Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection
Published 2023-01-01“…However, the traditional image classification methods often show low robustness when extracting complex objects from VHR images, with insufficient feature learning, object edge blur and noise. Our objective was to develop a repeatable method—superpixel-enhanced deep neural forests (SDNF)—to detect the UTC distribution from VHR images. …”
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913
An Optimized Smoke Segmentation Method for Forest and Grassland Fire Based on the UNet Framework
Published 2024-02-01“…GFUNet exhibited robust smoke feature learning capability and segmentation stability. …”
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914
A Novel Fully Convolutional Auto-Encoder Based on Dual Clustering and Latent Feature Adversarial Consistency for Hyperspectral Anomaly Detection
Published 2024-02-01“…However, existing methods proposed in recent years still suffer from certain limitations: (1) Constraints are lacking in the deep feature learning process in terms of the issue of the absence of prior background and anomaly information. (2) Hyperspectral anomaly detectors with traditional self-supervised deep learning methods fail to ensure prioritized reconstruction of the background. (3) The architecture of fully connected deep networks in hyperspectral anomaly detectors leads to low utilization of spatial information and the destruction of the original spatial relationship in hyperspectral imagery and disregards the spectral correlation between adjacent pixels. (4) Hypotheses or assumptions for background and anomaly distributions restrict the performance of many hyperspectral anomaly detectors because the distributions of background land covers are usually complex and not assumable in real-world hyperspectral imagery. …”
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915
Early diagnosis and clinical score prediction of Parkinson's disease based on longitudinal neuroimaging data
Published 2024“…Differing from previous studies on PD, we propose a network combining feature selection method with feature learning to obtain the most discriminative feature representation for longitudinal early diagnosis and clinical score prediction. …”
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Journal Article -
916
Constructing brain functional network by Adversarial Temporal-Spatial Aligned Transformer for early AD analysis
Published 2022-11-01“…By incorporating the volume and location of anatomical brain regions, the region-guided feature learning network can roughly focus on local features for each brain region. …”
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917
Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy
Published 2022-11-01“…We leveraged the feature learning capabilities of deep neural networks to predict patterns of adherence for a given participant, based on their past behavior. …”
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918
Deep Learning-Based Forearm Subcutaneous Veins Segmentation
Published 2022-01-01“…These are used for unsupervised feature learning and image-to-image translation applications. …”
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919
DEEPFAKE Image Synthesis for Data Augmentation
Published 2022-01-01“…In this work, a subjective self-attention layer has been added before <inline-formula> <tex-math notation="LaTeX">$256 \times 256$ </tex-math></inline-formula> convolution layer for efficient feature learning and the use of spectral normalization in the discriminator and pixel normalization in the generator for training stabilization - the two tasks resulting into what is referred to as Enhanced-GAN. …”
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920
Pre-Training Autoencoder for Lung Nodule Malignancy Assessment Using CT Images
Published 2020-11-01“…The results reported in this comparison suggested that the feature learning achieved when reconstructing the input with an encoder-decoder based architecture can be considered an useful knowledge that might allow overcoming labelling constraints.…”
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