Deep Contrastive Learning Network for Small-Sample Hyperspectral Image Classification by Quanyong Liu, Jiangtao Peng, Genwei Zhang, Weiwei Sun, Qian Du

“…By gradually increasing pseudo-labeled samples and refining the contrastive learning network, the model shows good feature learning ability and classification performance with the limited labeled samples. …”
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Study on Score Prediction Model with High Efficiency Based on Deep Learning by Lihong Yang, Zhiming Bai

“…Secondly, the model uses a factorization machine and two kinds of neural networks to consider the influence of first-order features, second-order features, and higher-order features at the same time, and it fully learns the relationship between the features and scores, which improves the prediction effect of the model compared to using only single feature learning. The performance of the model is evaluated on the learning analysis dataset from Fall 2015 to Spring 2021 and includes 412 courses with 600 students. …”
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Attention-Based Temporal-Frequency Aggregation for Speaker Verification by Meng Wang, Dazheng Feng, Tingting Su, Mohan Chen

“…Convolutional neural networks (CNNs) have significantly promoted the development of speaker verification (SV) systems because of their powerful deep feature learning capability. In CNN-based SV systems, utterance-level aggregation is an important component, and it compresses the frame-level features generated by the CNN frontend into an utterance-level representation. …”
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SSCLNet: A Self-Supervised Contrastive Loss-Based Pre-Trained Network for Brain MRI Classification by Animesh Mishra, Ritesh Jha, Vandana Bhattacharjee

“…For the contrastive loss-based pre-training, data augmentation is applied to the dataset, and positive and negative instance pairs are fed into a deep learning model for feature learning. Subsequently, the features are passed through a neural network model to maximize similarity and contrastive learning of the instances. …”
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Transformer-Based Global PointPillars 3D Object Detection Method by Lin Zhang, Hua Meng, Yunbing Yan, Xiaowei Xu

“…After the point cloud is divided into several pillars, global context features and local structure features are extracted through a multi-head attention mechanism, so that the point cloud after feature coding has global context features and local structure features; the two-dimensional pseudo-image generated by this feature is used for feature learning using a two-dimensional convolutional neural network. …”
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GC-MLP: Graph Convolution MLP for Point Cloud Analysis by Yong Wang, Guohua Geng, Pengbo Zhou, Qi Zhang, Zhan Li, Ruihang Feng

“…With the objective of addressing the problem of the fixed convolutional kernel of a standard convolution neural network and the isotropy of features making 3D point cloud data ineffective in feature learning, this paper proposes a point cloud processing method based on graph convolution multilayer perceptron, named GC-MLP. …”
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Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization. by Muhammad Ramzan, Mudassar Raza, Muhammad Irfan Sharif, Faisal Azam, Jungeun Kim, Seifedine Kadry

“…An automatic deep feature learning-based system is proposed for GIT disease classification. …”
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Predicting Multi-Gene Mutation Based on Lung Cancer CT Images and Mut-SeResNet by Lichao Sun, Yunyun Dong, Shuang Xu, Xiufang Feng, Xiaole Fan

“…We introduced a residual structure and extracted small differences between different levels to enhance the feature learning ability. The squeeze and excitation attention mechanism was adapted to fully extract the dependence between different channels of the feature image, and it calibrated the channel feature information. …”
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Adaptive Multiscale Reversible Column Network for SAR Ship Detection by Tianxiang Wang, Zhangfan Zeng

“…First, the idea of disentangled feature learning is applied to construct reversible column networks with a C2f module to alleviate the problem of large-scale differences and the loss of ship information. …”
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Iterated Residual Graph Convolutional Neural Network for Personalized Three-Dimensional Reconstruction of Left Myocardium from Cardiac MR Images by Xuchu Wang, Yue Yuan, Minghua Liu, Yanmin Niu

“…The mesh is then deformed using an iterated residual graph convolutional neural network. A vertex feature learning module is also built to assist the mesh deformation by adopting an encoder–decoder neural network to represent the skeleton of the left myocardium at different receptive fields. …”
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Alternate Optimization Method for 3D Pulmonary Nodules Retrieval Based on Medical Sign by Yanan ZHANG, Juanjuan ZHAO, Wei WU, Xin GENG, Guojie HOU

“…In order to solve the problems such as the complicated process of manual diagnosis and retrieval, high misdiagnosis rate, large amount of data, sparse Hash codes, a 3D ResNet network based on multi-label semantic supervision was proposed to quantify the medical signs of pulmonary nodules and construct a multi-label data set. 3D lung nodules were constructed by trilinear interpolation method, and loss functions were designed by similarity measurement for 3D feature learning. Then Hash codes were constructed. An alternate minimization optimization method was proposed to solve the problem that the traditional method cannot be used because of the discrete Hash code, and the closely expressed Hash code was learned. …”
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Dual-drive collaboration surrogate-assisted evolutionary algorithm by coupling feature reduction and reconstruction by Haibo Yu, Yiyun Gong, Li Kang, Chaoli Sun, Jianchao Zeng

“…To this end, this paper offers a dual-drive collaboration surrogate-assisted evolutionary algorithm (DDCSAEA) by coupling feature reduction and reconstruction, which coordinates two unsupervised feature learning techniques, i.e., principal component analysis and autoencoder, in tandem. …”
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Congestive Heart Failure Category Classification Using Neural Networks in Short-Term Series by Juan L. López, José A. Vásquez-Coronel

“…The results showed that the deep feature learning system obtained better classification rates than MLP, ELM, and RVFL. …”
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Machine Learning Sorting Method of Bauxite Based on SE-Enhanced Network by Pengfei Zhao, Zhengjie Luo, Jiansu Li, Yujun Liu, Baocheng Zhang

“…By introducing the K-means clustering algorithm into the YOLOv4 network and embedding the SE attention module, we calculate the corresponding anchor box value, enhance the feature learning ability of the network to bauxite, automatically learn the importance of different channel features, and improve the accuracy of bauxite target detection. …”
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Relation extraction for biological pathway construction using node2vec by Munui Kim, Seung Han Baek, Min Song

“…Results In this study, we applied node2vec, an algorithmic framework for feature learning in networks, for relationship extraction. …”
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Deep Network-Assisted Quality Inspection of Laser Welding on Power Battery by Dong Wang, Yongjia Zheng, Wei Dai, Ding Tang, Yinghong Peng

“…Moreover, coordinate attention is introduced into feature learning modules of the network to effectively capture the subtle features of defective welds. …”
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Review of Deep Learning Applications in Healthcare by XUE Fenghao, JIANG Haibo, TANG Dan

“…With the rapid development and integration of biomedicine and information technology,massive amounts of imaging data,patient report data,electronic health records,and omics data have been accumulated rapidly in healthcare.These data are cha-racterized by complexity,heterogeneity and high dimensionality.Deep learning has the ability of complex function simulation and automatic feature learning,which can provide efficient technical support for research in medical diagnosis and drug development.Currently,deep learning has been extremely successful in medical imaging and further more,some medical imaging diagnostic systems based on deep learning have achieved performance that is even comparable to that of relevant experts.Due to the progress of natural language processing technology,deep learning has also made remarkable progress in the use of non-image data tasks.This paper first briefly describes the development of deep learning in healthcare.Subsequently,the application of deep learning model in healthcare is statistically analyzed,and some available datasets are sorted out.In addition,this paper also introduces the research progress of deep learning in medical diagnosis and treatment processes such as disease diagnosis and health monitoring,and its research progress in protein structure prediction and drug discovery.Finally,key challenges of deep learning in healthcare applications such as data quality,interpretability,privacy security and practical application limitations are discussed.It also discusses feasible solutions or approaches to these challenges.…”
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Human Perception Intelligent Analysis Based on EEG Signals by Bingrui Geng, Ke Liu, Yiping Duan

“…Experimental and analytical results show that the EEG signals in frequency domain can be used for feature learning and calculation to measure changes in user-perceived audio noise intensity. …”
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Reinforcement Learning Based Vocal Fold Localization in Preoperative Neck CT for Injection Laryngoplasty by Walid Abdullah Al, Wonjae Cha, Il Dong Yun

“…Thus, localization of both folds can be trained using a single training session that utilizes the inter-fold correlation and avoids redundant feature learning. Experiments with 120 CT volumes showed improved localization performance and training efficiency of the proposed method compared with the standard RL method.…”
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A Complicated Case of Catatonia by A. Makela

“…Conclusions This case of a 24-year-old woman with catatonia brought an opportunity to retrospectively review a case in detail in order to feature learning objectives that review very important considerations in the evaluation, differential diagnosis, symptom tracking, and treatment of catatonia. …”
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