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461
Improved YOLOv4 Marine Target Detection Combined with CBAM
Published 2021-04-01“…This article uses the advantages of deep learning in big data feature learning to propose the YOLOv4 marine target detection method fused with a convolutional attention module. …”
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462
Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network
Published 2022-05-01“…In order to efficiently represent the state characteristics of vibration signals in image form and improve the feature learning capability of the network, this paper proposes fault diagnosis model MTF-ResNet based on a Markov transition field and deep residual network. …”
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463
A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition
Published 2024-03-01“…The results showed that using an ensemble of a Dense Neural Network and a Convolutional Neural Network architecture resulted in a state-of-the-art 80.20% F1 score, an improvement of around 5% considering the best baseline results, concluding that future research should take advantage of both paradigms, that is, combining handcrafted features with feature learning.…”
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464
<i>D-dCNN</i>: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics
Published 2021-08-01“…Against the limitations of traditional machine learning (ML)-based classifiers, the convolutional neural network (CNN) and deep neural network (DNN) are not only superior for real-time applications, but they also come with other benefits including ease-of-use, automated feature learning, and higher predictive accuracies. This study proposes a hybrid DNN and one-dimensional CNN diagnostics model (<i>D-dCNN</i>) which automatically extracts high-level discriminative features from vibration signals for FDI. …”
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465
Deep learning-based EEG emotion recognition: Current trends and future perspectives
Published 2023-02-01“…Inspired by the powerful feature learning ability of recently-emerged deep learning techniques, various advanced deep learning models have been employed increasingly to learn high-level feature representations for EEG emotion recognition. …”
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466
Maize seed appearance quality assessment based on improved Inception-ResNet
Published 2023-08-01“…In addition, an attention mechanism was applied to improve the feature learning performance of the network model and extract the best image information to express the appearance quality. …”
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467
MGATs: Motif-Based Graph Attention Networks
Published 2024-01-01“…Furthermore, through various experimental studies on real datasets, we demonstrate that the introduction of network structural motifs can effectively enhance the expressive power of graph neural networks, indicating that both high-order structural features and attribute features are important components of network feature learning.…”
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468
Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet
Published 2021-11-01“…The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. …”
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469
Dual Autoencoders Generative Adversarial Network for Imbalanced Classification Problem
Published 2021“…Meanwhile, the feature information of the fraud samples with better classification capabilities cannot be mined directly by feature learning methods due to too few fraud samples. …”
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470
Pairwise confusion for fine-grained visual classification
Published 2021“…While prior work has addressed intra-class variation using localization and segmentation techniques, inter-class similarity may also affect feature learning and reduce classification performance. …”
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471
R-ELMNet: regularized extreme learning machine network
Published 2022“…Experiments on image classification show the effectiveness compared to supervised convolutional neural networks and related shallow networks on unsupervised feature learning.…”
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Journal Article -
472
Deep FusionNet for point cloud semantic segmentation
Published 2020“…Other approaches (such as PointNets and point-wise convolutions) can take irregular points for feature learning. But their high memory and computational costs (such as for neighborhood search and ball-querying) limit their ability and accuracy for large-scale point cloud processing. …”
Conference item -
473
Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition
Published 2023-03-01“…This method uses STL for series decomposition, temporal convolution, a bidirectional long short-term memory network (TCN-BiLSTM) for feature learning of the decomposed series, and DMAttention for interdependent moment feature emphasizing. …”
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474
Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems
Published 2022-04-01“…Deep learning has been employed as an appealing alternative for channel estimation and signal detection in OFDM-based communication systems due to its better potential for feature learning and representation. In this study, we examine the deep neural network (DNN) layers created from long-short term memory (LSTM) for detecting the signals by learning the received signal as well as channel information. …”
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475
Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model
Published 2022-12-01“…Moreover, the latent correlation of images might be disregarded in CNN feature learning and thereby influence the representative capability of the CNN feature. …”
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476
Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition
Published 2022-01-01“…First, we designed a multi-scale spatial GCN to aggregate the remote and multi-order semantic information of the skeleton data and comprehensively model the internal relations of the human body for feature learning. Then, the multi-scale temporal module adaptively selects convolution kernels of different temporal lengths to obtain a more flexible temporal map. …”
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477
Improving RNA Base Interactions Prediction Based on Transfer Learning and Multi-view Feature Fusion
Published 2023-03-01“…RNA base interactions play an important role in maintaining the stability of its three-dimensional structure,and accurate prediction of base interactions can help predict the three-dimensional structure of RNA.However,due to the small amount of data,the model could not effectively learn the feature distribution of the training data,and existing data characteristics(symmetry and class imbalance) affect the performance of the RNA base interactions prediction model.Aiming at the problems of insufficient model learning and data characteristics,a high-performance RNA base interactions prediction method called tpRNA is proposed based on deep learning.tpRNA introduces transfer learning in RNA base interactions prediction task to weak the influence of insufficient learning in the training process due to the small amount of data,and an efficient loss function and feature extraction module is proposed to give full play to the advantages of transfer learning and convolutional neural network in feature learning to alleviate the problem of data characteristics.Results show that transfer learning can reduce the model deviation caused by less data,the proposed loss function can optimize the model training,and the feature extraction module can extract more effective features.Compared with the state-of-the-art method,tpRNA also has significant advantages in the case of low-quality input features.…”
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478
Scene Classification Based on Heterogeneous Features of Multi-Source Data
Published 2023-01-01“…Firstly, a multi-granularity feature learning module is designed, which can conduct uniform grid sampling of images to learn multi-granularity features. …”
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479
Enhancing Secret Data Detection Using Convolutional Neural Networks With Fuzzy Edge Detection
Published 2023-01-01“…Nevertheless, current CNNs encounter challenges related to the inadequate quality and quantity of available datasets, high imperceptibility of low payload capacities, and suboptimal feature learning processes. This paper proposes an enhanced secret data detection approach with a CNN architecture that includes convolutional, depth-wise, separable, pooling, and spatial dropout layers. …”
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480
Connecting Images through Sources: Exploring Low-Data, Heterogeneous Instance Retrieval
Published 2021-08-01“…However, while the latest propositions in deep learning have shown impressive results in applications linked to feature learning, they often rely on the hypothesis that there exists a training dataset matching the use case. …”
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Article