Improved YOLOv4 Marine Target Detection Combined with CBAM by Huixuan Fu, Guoqing Song, Yuchao Wang

“…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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Rolling Bearing Fault Diagnosis Based on Markov Transition Field and Residual Network by Jialin Yan, Jiangming Kan, Haifeng Luo

“…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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A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition by Pedro Lima Louro, Hugo Redinho, Ricardo Malheiro, Rui Pedro Paiva, Renato Panda

“…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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<i>D-dCNN</i>: A Novel Hybrid Deep Learning-Based Tool for Vibration-Based Diagnostics by Ugochukwu Ejike Akpudo, Jang-Wook Hur

“…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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Deep learning-based EEG emotion recognition: Current trends and future perspectives by Xiaohu Wang, Yongmei Ren, Ze Luo, Wei He, Jun Hong, Yinzhen Huang

“…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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Maize seed appearance quality assessment based on improved Inception-ResNet by Chang Song, Bo Peng, Huanyue Wang, Yuhong Zhou, Lei Sun, Xuesong Suo, Xiaofei Fan

“…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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MGATs: Motif-Based Graph Attention Networks by Jinfang Sheng, Yufeng Zhang, Bin Wang, Yaoxing Chang

“…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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Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet by Shih-Lin Lin

“…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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Dual Autoencoders Generative Adversarial Network for Imbalanced Classification Problem by Wu, Ensen, Cui, Hongyan, Welsch, Roy E

“…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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Pairwise confusion for fine-grained visual classification by Dubey, Abhimanyu, Gupta, Otkrist, Guo, Pei, Raskar, Ramesh, Farrell, Ryan

“…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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R-ELMNet: regularized extreme learning machine network by Zhang, Guanghao, Li, Yue, Cui, Dongshun, Mao, Shangbo, Huang, Guang-Bin

“…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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Deep FusionNet for point cloud semantic segmentation by Zhang, F, Fang, J, Wah, B, Torr, PHS

“…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. …”

Prediction of air pollutant concentrations based on TCN-BiLSTM-DMAttention with STL decomposition by Wenlin Li, Xuchu Jiang

“…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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Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems by Kah Jing Wong, Filbert Juwono, Regina Reine

“…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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Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model by Rasha A. Mansouri, Mahmoud Ragab

“…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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Multi-Scale Adaptive Aggregate Graph Convolutional Network for Skeleton-Based Action Recognition by Zhiyun Zheng, Yizhou Wang, Xingjin Zhang, Junfeng Wang

“…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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Improving RNA Base Interactions Prediction Based on Transfer Learning and Multi-view Feature Fusion by WANG Xiaofei, FAN Xueqiang, LI Zhangwei

“…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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Scene Classification Based on Heterogeneous Features of Multi-Source Data by Chengjun Xu, Jingqian Shu, Guobin Zhu

“…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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Enhancing Secret Data Detection Using Convolutional Neural Networks With Fuzzy Edge Detection by Ntivuguruzwa Jean De La Croix, Tohari Ahmad, Fengling Han

“…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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Connecting Images through Sources: Exploring Low-Data, Heterogeneous Instance Retrieval by Dimitri Gominski, Valérie Gouet-Brunet, Liming Chen

“…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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