Feature representation ‎via‎ graph-regularized ‎entropy-‎weighted nonnegative matrix factorization by Hazhir Sohrabi, Shahrokh Esmaeili, Parham Moradi

“…Feature extraction plays a crucial role in dimensionality reduction in machine learning applications. …”
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Airport Clearance Detection Based on Vision Transformer and Multi-Scale Feature Fusion by Yutong Chen, Yufen Liu, Zhixiong Guo, Qiang Gao

“…To overcome the defects in detection, this paper proposes an airport clearance detection algorithm based on Vision Transformer and multi-scale feature fusion to address the problems of poor real-time performance, low accuracy, and large parameter quantity in existing airport clearance detection systems. …”
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Chinese Character Features Facilitate Working Memory Updating: Evidence From the EEG by Hongli Li, Decai Ren, Yihang Ouyang

“…ABSTRACT Introduction According to the multicomponent model of working memory (WM), the phonological loop serves to protect WM representations from interference through its phonological storage and rehearsal mechanisms, thereby enhancing performance on WM tasks. …”
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Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation by Luis Miguel Moreno Haro, Adaiton Oliveira-Filho, Bruno Agard, Antoine Tahan

“…This paper presents a novel approach for detecting sensor failures using image-based feature representation and the Convolutional Variational Autoencoder (CVAE) model. …”
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HGLFNet: Hybrid Global Semantic and Local Detail Feature Network for Lane Detection by Lei Ding, Chunhui Tang, Yi Fang

“…Furthermore, we develop a Semantic Feature Fusion Module (SFFM) to effectively bridge the semantic gap between global semantic features and local detailed features. …”
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PFVnet, a feature enhancement network for low recognition coal and rock images by Cai Han, Zhenwen Liu, Shenglei Zhao, Yubo Li, Yanwei Duan, Xinzhou Yang, Chuanbo Hao

“…The simulation experiment results show that illumination, dust, and fog can reduce the distinguishability of coal-rock images, which seriously affects the recognition performance of the network. Based on this, the convolution operation was combined with the Vision Transformer network and the deep convolution algorithm was applied to design a parallel hybrid vision network model, PFVnet. …”
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Multi-granularity feature intersection learning for visible-infrared person re-identification by Sixian Chan, Jie Wang, Jiaao Cui, Jie Hu, Zhuorong Li, Jiafa Mao

“…Previous methods ignore the potential loss of details during representation extraction and the presence of data bias in the metric function, limiting further improvements in retrieval performance. Meanwhile, the discrepancy regarding how to calculate the loss for representation learning and metric learning also affects the model’s training. …”
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SDA-Mask R-CNN: An Advanced Seabed Feature Extraction Network for UUV by Yao Xiao, Dongchen Dai, Hongjian Wang, Chengfeng Li, Shaozheng Song

“…Second, a Depth-Weighted Hierarchical Fusion Network (DWHF-Net) incorporates depthwise separable convolution to minimize computational complexity while preserving model performance, which is particularly effective for high-resolution SSS image processing. …”
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AI-enhanced automation of building energy optimization using a hybrid stacked model and genetic algorithms: Experiments with seven machine learning techniques and a deep neural net... by Mohammad H. Mehraban, Samad ME Sepasgozar, Alireza Ghomimoghadam, Behrouz Zafari

“…It was further evaluated on unseen data from diverse UK cities without retraining, confirming its predictive power across varying climatic conditions. Feature importance analysis revealed that occupant behaviour and infiltration play the most significant role in energy performance, surpassing structural and building envelope characteristics.This paper’s contributions lie in the workflow designed for automating selected tasks, the AI-driven optimization framework, and the robust hybrid modeling approach, offering novel tools for energy-efficient building design and retrofitting. …”
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TDFNet: twice decoding V-Mamba-CNN Fusion features for building extraction by Wenlong Wang, Peng Yu, Mengmeng Li, Xiaojing Zhong, Yuanrong He, Hua Su, Yunxuan Zhou

“…During the decoding process, we propose an Encoder-Decoder Fusion Module (EDFM) to initially merge features from different stages of the encoder and decoder, thereby diminishing the model’s susceptibility to non-building elements with features similar to those of buildings, and consequently reducing the incidence of erroneous extractions. …”
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Integrating Correlation-Based Feature Selection and Clustering for Improved Cardiovascular Disease Diagnosis by Agnieszka Wosiak, Danuta Zakrzewska

“…Clustering of patient instances allows finding out groups for which statistical models can be built more efficiently. However, the performance of such an approach depends on the features used as clustering attributes. …”
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Comparison of MRI imaging features to differentiate degenerating fibroids from uterine leiomyosarcomas by William W Loughborough, Andrea G Rockall, Tanja T Gagliardi, Laura Satchwell, Emily Greenlay, Piers Osborne, Nishat Bharwani, Thomas Ind, Ayoma Attygalle, Dione Lother, Georgina Hopkinson, Robin Jones, Charlotte Benson, Aisha Miah, Aslam Sohaib, Christina Messiou

“…Objectives: The aim of this study was to construct a diagnostic model from MRI features to distinguish complex leiomyomas/degenerating fibroids (DF) from leiomyosarcoma (LMS). …”
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Multiscale Feature Fusion for Salient Object Detection of Strip Steel Surface Defects by Li Zhang, Xirui Li, Yange Sun, Yan Feng, Huaping Guo

“…This paper proposes a novel multiscale feature fusion (MFF) method for salient object detection of strip steel surface defects, fusing multiscale features through the following three steps: 1) generating rough multiscale features using upsampling/downsampling or convolution operations with sampling techniques, 2) applying self-attention operations to each feature to generate a refined representation, and 3) fusing the multiscale features from the previous two steps for salient object detection. …”
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A Registration Method for Historical Maps Based on Self-Supervised Feature Matching by Zikang Qin, Yumin Feng, Gang Wu, Qing Dong, Tianxin Han

“…Additionally, we proposed a self-supervised fine-tuning feature extraction algorithm and a Transformer-based architecture utilizing graph attention mechanisms to refine feature descriptors and enhance feature matching performance. …”
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Research on adversarial examples defense method based on multi-modal feature fusion by WEI Xuanxuan, LIU Wanping, LU Ling

“…Subsequently, a pre-trained TF-IDF model was employed to extract feature matrices from the textual descriptions, and a ResNet50 model was used to extract image features. …”
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Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG by Daojun Xu, Bin Zhou, Jiaqi Zhang, Chenyu Li, Chen Guan, Yuxuan Liu, Lin Li, Haina Li, Li Cui, Lingyu Xu, Hang Liu, Li Zhen, Yan Xu

“…Different machine learning models (LR, SVM, CNN, XGB, Adaboost) were built for dichotomous prediction of hyperkalemia by analyzing 48 features of chest leads V2-V5. …”
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Enhancing Traffic Accident Severity Prediction: Feature Identification Using Explainable AI by Jamal Alotaibi

“…The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. …”
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Multi-dimensional feature extraction of EEG signal and its application in stroke classification by Teng Wang, Wenhui Jia, Fenglian Li, Xirui Liu, Xueying Zhang, Fengyun Hu

“…However, the quality of the extracted features directly affects the classification performance. …”
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Enhancing cerebral infarct classification by automatically extracting relevant fMRI features by Vitaly I. Dobromyslin, Wenjin Zhou, for the Alzheimer’s Disease Neuroimaging Initiative

“…Surface-based registration methods were applied to minimize partial-volume effects typically associated with lower resolution fMRI data. We evaluated the performance of 7 previously known fMRI biomarkers alongside 107 new auto-generated fMRI biomarkers across 33 different classification models. …”
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Improving Voice Spoofing Detection Through Extensive Analysis of Multicepstral Feature Reduction by Leonardo Mendes de Souza, Rodrigo Capobianco Guido, Rodrigo Colnago Contreras, Monique Simplicio Viana, Marcelo Adriano dos Santos Bongarti

“…Our framework involves extracting multicepstral features followed by the application of diverse dimensionality reduction methods, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (SVD), statistical feature selection (ANOVA F-value, Mutual Information), Recursive Feature Elimination (RFE), regularization-based LASSO selection, Random Forest feature importance, and Permutation Importance techniques. …”
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