A comprehensive investigation of PRMT5 in the prognosis and ion channel features of lung cancer by Yan Wang, Daifang Chu, Haichao Li, Jiangjiang Fan, Ximing Zhu, Yulong Ma, Zhongping Gu, Nianlin Xie, Pengyu Jing

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Multi-feature fusion-based consumer perceived risk prediction and its interpretability study. by Lin Qi, Yunjie Xie, Qianqian Zhang, Jian Zhang, Yanhong Ma

“…Based on a dataset containing 262,752 online reviews, we employ the KeyBERT-TextCNN model to extract thematic features from the review content. …”
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Vehicle re-identification with multiple discriminative features based on non-local-attention block by Lu Bai, Leilei Rong

“…Comprehensive experiments implemented on challenging vehicle evaluation datasets (including VeRi-776, VRIC, and VehicleID) show that our model robustly achieves state-of-the-art performances. …”
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Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection by Mohammad Abrar, Abdu Salam, Ahmed Albugmi, Fahad Al-otaibi, Farhan Amin, Isabel de la Torre, Thania Candelaria Chio Montero, Perla Araceli Arroyo Gala

“…The preprocessed data passed to the next stage. In the feature extraction stage, feature selection is performed using CAOA. …”
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SFEF-Net: Scattering Feature Extraction and Fusion Network for Aircraft Detection in SAR Images by Qiang Zhou, Zongxu Pan, Ben Niu

“…Secondly, we developed the global information fusion and distribution module (GIFD) to fuse feature maps of different levels and scales. GIFD possesses the capability for global modeling, enabling the comprehensive fusion of multi-scale features and the utilization of contextual information. …”
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Thermographic Data Processing and Feature Extraction Approaches for Machine Learning-Based Defect Detection by Alexey Moskovchenko, Michal Svantner

“…Data preparation and feature extraction are crucial factors affecting ML model results, especially in thermographic data analysis. …”
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Improving Image Spam Detection Using a New Image Texture Features Selection by Azam Shekari Shahrak, Seyed javad Mirabedini, Nasser Mikaeilvand, Seyed Hamid Haj Seyed Javadi

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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

“…While deep learning models like Vision Transformer (ViT) capture broader temporal features, they struggle with varying fault causes and time dependencies inherent in industrial data, where adding encoder layers may even hinder performance. …”
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Comparison of Feature Extraction in Support Vector Machine (SVM) Based Sentiment Analysis System by Imam Fahrur Rozi, Irma Maulidia, Mamluatul Hani’ah, Rakhmat Arianto, Dika Rizky Yunianto, Ahmadi Yuli Ananta

“…Our findings indicate that SVM performs effectively with all three feature extraction methods, with TF-IDF yielding the highest accuracy at 0.79. …”
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THz image recognition of moldy wheat based on multi-scale context and feature pyramid by Yuying Jiang, Yuying Jiang, Yuying Jiang, Xinyu Chen, Xinyu Chen, Xinyu Chen, Hongyi Ge, Hongyi Ge, Hongyi Ge, Xixi Wen, Xixi Wen, Xixi Wen, Mengdie Jiang, Mengdie Jiang, Mengdie Jiang, Yuan Zhang, Yuan Zhang, Yuan Zhang

“…Moreover, a bidirectional feature pyramid network is embedded into the baseline model, so that certain coarse-grained features and fine-grained features are retained in the processed output features at the same time to improve the network recognition accuracy. …”
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DSNet enables feature fusion and detail restoration for accurate object detection in foggy conditions by Zhiyong Jing, Zhaobing Chen, Yucheng Shi, Lei Shi, Lin Wei, Yufei Gao

“…Abstract In real-world scenarios, adverse weather conditions can significantly degrade the performance of deep learning-based object detection models. …”
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Benchmarking Variants of Recursive Feature Elimination: Insights from Predictive Tasks in Education and Healthcare by Okan Bulut, Bin Tan, Elisabetta Mazzullo, Ali Syed

“…For example, while RFE wrapped with tree-based models such as Random Forest and Extreme Gradient Boosting (XGBoost) yields strong predictive performance, these methods tend to retain large feature sets and incur high computational costs. …”
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A method for feature division of Soccer Foul actions based on salience image semantics. by Jianming Wang, Lifeng Li

“…Therefore, a Deep Learning-Based Saliency Prediction Model (DLSPM) is proposed. DLSPM combines the improved DeepPlaBV 3+architecture for salient region detection, Graph Convolutional Networks (GCN) for feature extraction and Deep Neural Network (DNN) for classification. …”
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A supervised machine learning approach with feature selection for sex-specific biomarker prediction by Luke Meyer, Danielle Mulder, Joshua Wallace

“…For predictions within 10% error, the top performing models were waist circumference, albuminuria, BMI, blood glucose and systolic blood pressure, with males scoring higher than females, followed by the combined data set containing sex as an input feature and the combined data without sex as an input feature performing the poorest. …”
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Effective Feature Selection on Transfer Deep Learning Algorithm for Thyroid Nodules Ultrasound Detection by Ghufran Basim Alghanimi, Hadeel Aljobouri, Khaleel Akeash Alshimmari, Rasha Massoud

“…ResNet50 was the first model used to extract deep features from US images. …”
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A Multi-Semantic Feature Fusion Method for Complex Address Matching of Chinese Addresses by Pengpeng Li, Qing Zhu, Jiping Liu, Tao Liu, Ping Du, Shuangtong Liu, Yuting Zhang

“…Finally, the Enhanced Sequential Inference Model (ESIM) is used to perform both local inference and inference composition on the multi-semantic features of the addresses to achieve accurate matching of addresses. …”
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Radiomics features from the peritumoral region can be associated with the epilepsy status of glioblastoma patients by Yeong Chul Yun, Yeong Chul Yun, Yeong Chul Yun, Johann M. E. Jende, Johann M. E. Jende, Katharina Holz, Katharina Holz, Sabine Wolf, Sabine Wolf, Freya Garhöfer, Freya Garhöfer, Anja Hohmann, Philipp Vollmuth, Martin Bendszus, Heinz-Peter Schlemmer, Felix Sahm, Felix Sahm, Sabine Heiland, Wolfgang Wick, Varun Venkataramani, Varun Venkataramani, Felix T. Kurz, Felix T. Kurz, Felix T. Kurz

“…The training cohort was used for feature selection with ElasticNet and model optimization. …”
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Spectroscopic detection of cotton Verticillium wilt by spectral feature selection and machine learning methods by Weinan Li, Weinan Li, Weinan Li, Lisen Liu, Jianing Li, Weiguang Yang, Weiguang Yang, Yang Guo, Yang Guo, Longyu Huang, Longyu Huang, Zhaoen Yang, Jun Peng, Jun Peng, Xiuliang Jin, Xiuliang Jin, Yubin Lan, Yubin Lan

“…At the canopy scale, UAV-based hyperspectral data achieved a remarkable classification accuracy of 93.0% for disease incidence detection.DiscussionThis study highlights the significant impact of feature selection on enhancing the performance of hyperspectral-based remote sensing models for cotton wilt monitoring. …”
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Remote sensing inversion of nitrogen content in silage maize plants based on feature selection by Kejing Cheng, Kejing Cheng, Jixuan Yan, Jixuan Yan, Guang Li, Guang Li, Weiwei Ma, Weiwei Ma, Zichen Guo, Zichen Guo, Wenning Wang, Wenning Wang, Haolin Li, Qihong Da, Qihong Da, Xuchun Li, Xuchun Li, Yadong Yao, Yadong Yao

“…The results reveal that there is a degree of redundancy in the information contained in various spectral indices. Feature selection effectively eliminates correlated and redundant spectral information, thereby improving modeling efficiency. …”
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Neural Networks for Operational SYM‐H Forecasting Using Attention and SWICS Plasma Features by Armando Collado‐Villaverde, Pablo Muñoz, Consuelo Cid

“…To overcome that issue, we use ACE's Solar Wind Ion Composition Spectrometer (SWICS) data to fill the missing plasma features. To validate this technique, we compare the results of our forecasting model trained using plasma features in two ways: only using SWEPAM and performing linear interpolation and using SWICS to fill the missing values in SWEPAM. …”
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