EEG-based emotion recognition using machine learning techniques by Lan, Zirui

“…Cross-subject transfer learning for calibration-less affective Brain-Computer Interfaces. 3. Unsupervised feature learning for affective Brain-Computer Interfaces. …”
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An efficient technique for human verification using finger stripes geometry by Rahman, Md. Arafatur, Azad, Md. Saiful, Anwar, Farhat

“…This finger stripe based verification consists of two main attributes, feature extraction by image processing and feature learning by ANN (Artificial Neural Network). The Distance Based Nearest Neighbor Algorithm, which shows greater accuracy than NN is also applied here. …”
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Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: a review by Saufi, Syahril Ramadhan, Ahmad, Zair Asrar, Leong, Mohd. Salman, Lim, Meng Hee

“…The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. …”
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Review of deep convolution neural network in image classification by Al-Saffar, Ahmed Ali Mohammed, Tao, Hai, Mohammed, Ahmed Talab

“…With the development of large data age, Convolutional neural networks (CNNs) with more hidden layers have more complex network structure and more powerful feature learning and feature expression abilities than traditional machine learning methods. …”
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Attention-Based Sequence-to-Sequence Model for Time Series Imputation by Yurui Li, Mingjing Du, Sheng He

“…To solve the problem of high-dimensional time series with missing values, this paper proposes an attention-based sequence-to-sequence model to imputation missing values in time series (ASSM), which is a sequence-to-sequence model based on the combination of feature learning and data computation. The model consists of two parts, encoder and decoder. …”
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HDS-Net: Achieving fine-grained skin lesion segmentation using hybrid encoding and dynamic sparse attention. by You Xue, Xinya Chen, Pei Liu, Xiaoyi Lv

“…Due to the capacity of deep learning models to conduct adaptive feature learning through end-to-end training, they have been widely applied in medical image segmentation tasks. …”
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Distinct neurocognitive fingerprints reflect differential associations with risky and impulsive behavior in a neurotypical sample by Sonia G. Ruiz, Inti A. Brazil, Arielle Baskin-Sommers

“…Using the neurotypical Nathan Kline Institute Rockland Sample (N = 673), we applied a Bayesian latent feature learning model—the Indian Buffet Process—to identify nuanced, individual-specific profiles of multiple neurocognitive subfunctions and examine their relationship to risky and impulsive behavior. …”
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Kernel Matrix-Based Heuristic Multiple Kernel Learning by Stanton R. Price, Derek T. Anderson, Timothy C. Havens, Steven R. Price

“…Experiments on benchmark datasets and a computer vision feature learning task in explosive hazard detection demonstrate the effectiveness and generalizability of our proposed methods.…”
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Sub-convolutional U-Net with transformer attention network for end-to-end single-channel speech enhancement by Sivaramakrishna Yecchuri, Sunny Dayal Vanambathina

“…Instead of adopting conventional RNNs and temporal convolutional networks for sequence modeling, we employ a novel transformer-based attention network between the sub-convolutional U-Net encoder and decoder for better feature learning. More specifically, it is composed of several adaptive time―frequency attention modules and an adaptive hierarchical attention module, aiming to capture long-term time-frequency dependencies and further aggregate hierarchical contextual information. …”
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EMG Pattern Recognition in the Era of Big Data and Deep Learning by Angkoon Phinyomark, Erik Scheme

“…These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on “deep learning”. …”
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Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition by Shaona Wang, Yang Liu, Linlin Li

“…In this study, a novel feature learning method for synthetic aperture radar (SAR) image automatic target recognition is presented. …”
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Classification of West Java Batik Motifs Using Convolutional Neural Network by Firman Yosep Tember, Ina Najiyah

“…The results carried out for the classification of West Java batik image types using the Convolutional Neural Network (CNN) method that the feature extraction process can be carried out outside the process contained in the CNN algorithm or using feature learning depending on the needs of the research itself, and the results of the classification at 20 epochs and a learning rate value of 0.001 obtained an accuracy of 90% with a precision of 90% and a recall of 90%. …”
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Semi‐supervised domain adaptation via subspace exploration by Zheng Han, Xiaobin Zhu, Chun Yang, Zhiyu Fang, Jingyan Qin, Xucheng Yin

“…To be concrete, for disentangling the intricate relationship between feature learning and subspace exploration, the authors iterate and optimise them in two steps: in the first step, the authors aim to learn well‐clustered latent representations by aggregating the target feature around the estimated class‐wise prototypes; in the second step, the authors adaptively explore a subspace of an autoencoder for robust SSDA. …”
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A hybrid U‐shaped and transformer network for change detection in high‐resolution remote sensing images by Huapeng Wu, Mengxue Yuan, Tianming Zhan

“…Specifically, a UNet++‐based backbone to facilitate feature learning across different scales. In addition, we introduce a transformer‐based feature fusion module for extracting long‐range dependencies, which can enhance the representation ability of the network. …”
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MDBF: Meta-Path-Based Depth and Breadth Feature Fusion for Recommendation in Heterogeneous Network by Hongjuan Liu, Huairui Zhang

“…Using a random walk for depth feature learning, we can extract a depth feature meta-path that reflects the overall structure of the network. …”
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Analysis of the Teaching Path of Animation Film Majors Integrating Digital Visual Space in the Context of Digital Media Art by He Zhifang

“…And under the condition of having extra bandwidth, the animation feature learning evaluation dimension is 2589, and increasing the animation visual frame rate can improve the output picture quality of animation more effectively. …”
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A CNN-ELM Classification Model for Automated Tomato Maturity Grading by John Paul Tan Yusiong

“…This paper proposes a CNN-ELM classification model for automated tomato maturity grading that combines CNNs’ automated feature learning capabilities with the efficiency of extreme learning machines to perform fast and accurate classification even with limited training data. …”
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CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation by Yingbo Zhou, Pengcheng Zhao, Weiqin Tong, Yongxin Zhu

“…The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. …”
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Learning Polynomial-Based Separable Convolution for 3D Point Cloud Analysis by Ruixuan Yu, Jian Sun

“…Point convolution is an essential operation when designing a network on point clouds for these tasks, which helps to explore 3D local points for feature learning. In this paper, we propose a novel point convolution (PSConv) using separable weights learned with polynomials for 3D point cloud analysis. …”
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Texture synthesis of ecological plant protection image based on convolution neural network by Libing Hu, Fei Zhou, Xianjun Fu

“…Convolutional neural network model can learn the features in data and realize intelligent processing through the feature learning in data. Later, with the rapid improvement of convolutional neural network, texture synthesis technology based on neural network came into being. …”
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