Day-ahead photovoltaic power forecasting with multi-source temporal-feature convolutional networks by Ziming Ouyang, Zhaohui Li, Xiangdong Chen

“…MSTFCN employs parallel convolution to capture local temporal patterns and improves global feature representation via a channel attention mechanism. …”
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Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction by Wei Bai, Lan Xiong, Yubei Liao, Zhengyang Tan, Jingang Wang, Zhanlong Zhang

“…In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. …”
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Terrain and Atmosphere Classification Framework on Satellite Data Through Attentional Feature Fusion Network by Antoni Jaszcz, Dawid Połap

“…The neural architecture model takes into account different types of features by extracting them by focusing on spatial, local patterns and multi-scale representation. …”
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Sensitivity of mesoscale modeling to urban morphological feature inputs and implications for characterizing urban sustainability by Melissa R. Allen-Dumas, Levi T. Sweet-Breu, Christa M. Brelsford, Linying Wang, Joshua R. New, Brett C. Bass

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Comparative experimental study of different bearings vibration using FFT and statistical feature extraction by Wafa Abd Soud, Baqer Ihsan A.

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SwinDenoising: A Local and Global Feature Fusion Algorithm for Infrared Image Denoising by Wenhao Wu, Xiaoqing Dong, Ruihao Li, Hongcai Chen, Lianglun Cheng

“…This method leverages the powerful feature extraction capabilities of Swin Transformers to capture both local and global image features, thereby enhancing the denoising process. …”
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Looming Detection in Complex Dynamic Visual Scenes by Interneuronal Coordination of Motion and Feature Pathways by Bo Gu, Jianfeng Feng, Zhuoyi Song

“…Existing insect‐inspired looming detection models typically rely on either motion‐pathway or feature‐pathway signals, yet both are susceptible to dynamic visual scene interference. …”
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Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head by Xu Guo, Xingmeng Wang, Wenhao Zhu, Simon X. Yang, Lepeng Song, Ping Li, Qinzheng Li

“…It introduces the DR module structure for effective feature enhancement and the Detect_Shared architecture for parameter efficiency. …”
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Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection by Sangita Baruah, Vaskar Deka, Dulumani Das, Utpal Barman, Manob Jyoti Saikia

“…Employing differential evolution, we propose a feature selection approach that enhances the ability to discern peer-to-peer (P2P) botnet traffic amidst evolving cyber threats. …”
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Feature selection for specific prediction targets at the user level in a district heating network by Samanta A. Weber, Michael Fischlschweiger, Dirk Volta, Jens Geisler

“…For this, statistical and machine learning methods are applied to engineer respective features and establish specific interdependencies, including meteorological conditions, human behavioral patterns, and operational parameters, based on a model region in northern Germany. …”
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Sequence-Aware Vision Transformer with Feature Fusion for Fault Diagnosis in Complex Industrial Processes by Zhong Zhang, Ming Xu, Song Wang, Xin Guo, Jinfeng Gao, Aiguo Patrick Hu

“…Traditional image processing methods are effective for local feature extraction but often miss global temporal patterns, crucial for accurate diagnosis. …”
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Spectral feature modeling with graph signal processing for brain connectivity in autism spectrum disorder by Ayesha Jabbar, Huang Jianjun, Muhammad Kashif Jabbar, Khalil ur Rehman, Anas Bilal

“…We extract advanced GSP features such as Graph Fourier Transform coefficients, spectral entropy, and clustering coefficients, and combine them using Principal Component Analysis (PCA). …”
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Motor Imagery EEG Classification Based on Multi-Domain Feature Rotation and Stacking Ensemble by Xianglong Zhu, Ming Meng, Zewen Yan, Zhizeng Luo

“…Next, the rotated features were fused with the original significant features from each domain to obtain composite features for each domain. …”
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Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition by Pawel Powroznik, Maria Skublewska-Paszkowska, Krzysztof Dziedzic, Marcin Barszcz

“…Deep learning models, particularly when combined with sensor data fusion, can significantly enhance various applications by learning complex patterns and relationships from diverse data streams. …”
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Spatiotemporal Land Use Change Detection Through Automated Sampling and Multi-Feature Composite Analysis: A Case Study of the Ebinur Lake Basin by Yi Yang, Liang Zhao, Ya Guo, Shihua Liu, Xiang Qin, Yixiao Li, Xiaoqiong Jiang

“…Feature selection based on the Gini coefficient and Out-Of-Bag Error (OOBE) reduced the original 48 features to 23. …”
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Blackberry Growth Monitoring and Feature Quantification with Unmanned Aerial Vehicle (UAV) Remote Sensing by Akwasi Tagoe, Alexander Silva, Cengiz Koparan, Aurelie Poncet, Dongyi Wang, Donald Johnson, Margaret Worthington

“…Reliable and standardized techniques to record and evaluate crop features using agricultural technology are essential for improving agricultural systems. …”
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Automatic Feature Selection for Imbalanced Echocardiogram Data Using Event-Based Self-Similarity by Huang-Nan Huang, Hong-Min Chen, Wei-Wen Lin, Rita Wiryasaputra, Yung-Cheng Chen, Yu-Huei Wang, Chao-Tung Yang

“…Critical features correlated with disease progression were identified by leveraging self-similarity patterns. …”
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Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning by ShengHui Miao, YiJing Liu, Min Li, Jing Yan

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Pine Wilt Disease Monitoring Using Multimodal Remote Sensing Data and Feature Classification by Xin Ye, Hanwen Yu, Yan Yan, Tieming Liu, Yan Zhang, Taoli Yang

“…Statistical analysis of SAR-derived moisture content reveals significant differences in moisture variation patterns between diseased and healthy trees, enhancing the interpretability of the input features for the neural network. …”
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Molecular classification of hepatocellular carcinoma based on zoned metabolic feature and oncogenic signaling pathway by Tomoko Aoki, Naoshi Nishida, Yutaka Kurebayashi, Kazuko Sakai, Naoto Fujiwara, Masakatsu Tsurusaki, Kohei Hanaoka, Masahiro Morita, Hirokazu Chishina, Masahiro Takita, Satoru Hagiwara, Hiroshi Ida, Kazuomi Ueshima, Yasunori Minami, Atsushi Takebe, Takaaki Murase, Keiko Kamei, Takuya Nakai, Ippei Matsumoto, Kazuto Nishio, Masatoshi Kudo

“…Conversely, the Wnt/β-catenin-high environment (19.9%) features elevated GLUL, APOB and CYP3A4 expression, frequent CTNNB1 (D32–S37) mutations, and an immune-desert/excluded phenotype. …”
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