A Novel Feature Extraction Method for Nonintrusive Appliance Load Monitoring by Khaled Chahine, Khalil El Khamlichi Drissi

“…This system captures the signals from the aggregate consumption, extracts the features from these signals and classifies the extracted features in order to identify the switched-on appliances. …”
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Bearing Fault Diagnosis based on Feature Visualization and Depth Adaptive Network by Hong Jiang, Yu Feng, Rong Fu

“…Aiming at the problem that feature extraction in bearing fault diagnosis needs to rely heavily on manual experience and expert knowledge，a bearing fault diagnosis method based on Gramian angle field（GAF） transformation and adaptive depth network is proposed. …”
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Speaker verification method based on cross-domain attentive feature fusion by Zhen YANG, Tianlang WANG, Haiyan GUO, Tingting WANG

“…Aiming at the problem that the lack of structure information among speech signal sample in the front-end acoustic features of speaker verification system, a speaker verification method based on cross-domain attentive feature fusion was proposed.Firstly, a feature extraction method based on the graph signal processing (GSP) was proposed to extract the structural information of speech signals, each sample point in a speech signal frame was regarded as a graph node to construct the speech graph signal and the graph frequency information of the speech signal was extracted through the graph Fourier transform and filter banks.Then, an attentive feature fusion network with the residual neural network and the squeeze-and- excitation block was proposed to fuse the features in the traditional time-frequency domain and those in the graph frequency domain to promote the speaker verification system performance.Finally, the experiment was carried out on the VoxCeleb, SITW, and CN-Celeb datasets.The experimental results show that the proposed method performs better than the baseline ECAPA-TDNN model in terms of equal error rate (EER) and minimum detection cost function (min-DCF).…”
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Speaker verification method based on cross-domain attentive feature fusion by Zhen YANG, Tianlang WANG, Haiyan GUO, Tingting WANG

“…Aiming at the problem that the lack of structure information among speech signal sample in the front-end acoustic features of speaker verification system, a speaker verification method based on cross-domain attentive feature fusion was proposed.Firstly, a feature extraction method based on the graph signal processing (GSP) was proposed to extract the structural information of speech signals, each sample point in a speech signal frame was regarded as a graph node to construct the speech graph signal and the graph frequency information of the speech signal was extracted through the graph Fourier transform and filter banks.Then, an attentive feature fusion network with the residual neural network and the squeeze-and- excitation block was proposed to fuse the features in the traditional time-frequency domain and those in the graph frequency domain to promote the speaker verification system performance.Finally, the experiment was carried out on the VoxCeleb, SITW, and CN-Celeb datasets.The experimental results show that the proposed method performs better than the baseline ECAPA-TDNN model in terms of equal error rate (EER) and minimum detection cost function (min-DCF).…”
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Enhancing Robustness in Feature Importance Methods with NAFIC and CESHAP for Improved Interpretability by Grigorios Tzionis, Georgia Kougka, Ilias Gialampoukidis, Stefanos Vrochidis, Ioannis Kompatsiaris, Maro Vlachopoulou

“…To address this problem, we introduce the Complexity and Interaction Enhanced SHAP (CESHAP), a novel feature importance method that incorporates model complexity and feature interactions. …”
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The Impact of Feature Extraction in Random Forest Classifier for Fake News Detection by Dhani Ariatmanto, Anggi Muhammad Rifai

“…The influence of fake news has become a pressing social problem, shaping public opinion in important events such as elections. …”
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Real-time Semantic Segmentation Method Based on Improved Feature Fusion by WANG Xiao yu, LI Zhi bin

“… Aiming at the problem that both location information and semantic information need to be considered in real-time semantic segmentation tasks, we proposed a real-time semantic segmentation method based on improved feature fusion. …”
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Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection by Yijie Zhang, Yuhang Cai

“…This paper proposes a novel binary Gray Wolf Optimization algorithm to address the feature selection problem in classification tasks. …”
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Acoustic fault diagnosis of traction motor bearing based on fusion feature by YANG Gang, WEI Yuqian, LI Fu

“…Aiming at the problem that a small number of features cannot fully characterize the bearing fault when applying machine learning to acoustic fault diagnosis, this paper proposes to superimpose and fuse the Gramian angular field (GAF) and wavelet time-frequency figure to form a six channel fusion feature map to effectively characterize the bearing fault. …”
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Two-branch Shape Complement Network for Feature Missing Splicing Mode by SUNJin, MAHaotian, LEIZhenting, LIANGLi

“…By decoding the global features , the target skeleton point cloud is obtained , and the global features of the point cloud are guaranteed. …”
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Unified Depth-Guided Feature Fusion and Reranking for Hierarchical Place Recognition by Kunmo Li, Yongsheng Ou, Jian Ning, Fanchang Kong, Haiyang Cai, Haoyang Li

“…Prevailing VPR methods predominantly employ RGB-based features for query image retrieval and correspondence establishment. …”
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Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search by Simon Fong, Yan Zhuang, Rui Tang, Xin-She Yang, Suash Deb

“…It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. …”
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Key nodes identification in complex networks based on subnetwork feature extraction by Luyuan Gao, Xiaoyang Liu, Chao Liu, Yihao Zhang, Giacomo Fiumara, Pasquale De Meo

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Video Action Recognition Based on Two‑stream Feature Enhancement Network by ZHAO Chen, FENG Xiufang, DONG Yunyun, WEN Xin, CAO Ruochen

“…To solve the problem of feature damage caused by temporal shift, a Spatial Enhancement-Temporal Shift Module (SE-TSM) and a Channel Enhancement-Temporal Shift Module (CE-TSM) were proposed to enhance features after each time shift, for improving damaged features. …”
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Extensive Feature-Inferring Deep Network for Hyperspectral and Multispectral Image Fusion by Abdolraheem Khader, Jingxiang Yang, Sara Abdelwahab Ghorashi, Ali Ahmed, Zeinab Dehghan, Liang Xiao

“…The proposed network retains the most vital information through the extensive-scale feature-interacting module in various feature scales. …”
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Feature of Ocular-Ischemic Syndrome in Patients with Cardiovascular Pathology. Literature Review by M. A. Frolov, N. A. Sakhovskaya, A. M. Frolov, A. D. Pryamikov

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Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization by Jomana Yousef Khaseeb, Arabi Keshk, Anas Youssef

“…Feature selection is a preprocessing step for various classification tasks. …”
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Infrared dim tiny-sized target detection based on feature fusion by Peng Zhang, Yaman Jing, Guodong Liu, Ziyang Chen, Xiaoyan Wu, Osami Sasaki, Jixiong Pu

“…The module aims to extract and enhance the position information of the target, serving as a feature map for subsequent guidance. Furthermore, to tackle the problem of the fuzzy and ambiguous shape of the target caused by a weak signal and tiny dimension, a residual-based pyramid-like (RBPL) module is designed, which extracts the deep information (i.e. the shape information) from the images to compensate for the lack of expressive ability of the fixed convolution kernel for shape information. …”
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Surface Defect Detection Based on Adaptive Multi-Scale Feature Fusion by Guochen Wen, Li Cheng, Haiwen Yuan, Xuan Li

“…To this end, this study proposes a new adaptive multi-scale feature fusion network (AMSFF-Net) to solve the SOD problem of object surface defects. …”
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Features of the analysis of social sciences as an information process in a digital environment by A. B. Antopolsky

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