Urban building-level positioning using data-driven algorithms enhanced by spatial variations in sensor features by Die Zhang, Xin Liu, Yong Ge, Yixi Wei, Mengxiao Liu

“…For devices identified as indoors, building identification is performed using a Bayesian inference model that incorporates prior knowledge derived from anonymous crowdsourced data, leveraging spatial heterogeneity in sensor feature distributions across candidate buildings. …”
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A Comprehensive Feature Extraction Network for Deep-Learning-Based Wildfire Detection in Remote Sensing Imagery by Haiyan Pan, Die Luo, Yuewei Zhang

“…Experimental findings demonstrate that this method surpasses traditional models such as DenseNet, SimpleCNN, and Multi-Layer Perceptron (MLP) across multiple performance metrics, including accuracy, recall, and F1 score.…”
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Wi-FiAG: Fine-Grained Abnormal Gait Recognition via CNN-BiGRU with Attention Mechanism from Wi-Fi CSI by Anming Dong, Jiahao Zhang, Wendong Xu, Jia Jia, Shanshan Yun, Jiguo Yu

“…Moreover, the attention mechanism is employed to selectively focus on critical spatiotemporal features for fine-grained abnormal gait detection, enhancing the model’s sensitivity to subtle anomalies. …”
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Application of simulation methods to evaluation of accoustic features and design of adaptation of the concert hall of Cracow Philharmonic Orchestra by A. GOŁAŚ, J. WIERZBICKI

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Azimuth-Guided Feature Embedding Network With Dual Inference Mechanism for Few-Shot SAR Target Recognition by Yan Peng, Xuelian Yu, Haohao Ren, Lei Miao, Lin Zou, Yun Zhou

“…To be specific, the feature extraction model, i.e., AGFEN is composed of the azimuth embedding module (AEM) and the dynamic feature embedding network (DFEN). …”
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DriftShield: Autonomous Fraud Detection via Actor-Critic Reinforcement Learning With Dynamic Feature Reweighting by Jialei Cao, Wenxia Zheng, Yao Ge, Jiyuan Wang

“…Experimental evaluation demonstrates that DriftShield achieves 18% higher fraud detection rates while maintaining lower false positive rates compared to static models. The system demonstrates 57% faster adaptation times, recovering optimal performance within 280 transactions after significant concept drift compared to 650 transactions for the next-best reinforcement learning approach. …”
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Multi-Scale Feature Extraction with 3D Complex-Valued Network for PolSAR Image Classification by Nana Jiang, Wenbo Zhao, Jiao Guo, Qiang Zhao, Jubo Zhu

“…Furthermore, to address robustness degradation from limited labeled samples, we introduced a multi-scale learning strategy that jointly models global and local features. Specifically, global features extract overall semantic information, while local features help the network capture region-specific semantics. …”
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HDF-Net: Hierarchical Dual-Branch Feature Extraction Fusion Network for Infrared and Visible Image Fusion by Yanghang Zhu, Mingsheng Huang, Yaohua Zhu, Jingyu Jiang, Yong Zhang

“…Remarkably, we propose a pin-wheel-convolutional transformer (PCT) module that integrates local convolutional processing with directional attention to improve low-frequency feature extraction, thereby enabling more robust global–local context modeling. …”
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Predicting Parkinson's disease trajectory using clinical and functional MRI features: A reproduction and replication study. by Elodie Germani, Nikhil Bhagwat, Mathieu Dugré, Rémi Gau, Albert A Montillo, Kevin P Nguyen, Andrzej Sokolowski, Madeleine Sharp, Jean-Baptiste Poline, Tristan Glatard

“…We use the Parkinson's Progression Markers Initiative dataset (PPMI, ppmi-info.org), as in (Nguyen et al., 2021) and aim to reproduce the original cohort, imaging features and machine learning models as closely as possible using the information available in the paper and the code. …”
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PolSAR Forest Height Estimation Enhancement With Polarimetric Rotation Domain Features and Multivariate Sensitivity Analysis by Fu-Gen Jiang, Ming-Dian Li, Si-Wei Chen

“…Typical polarimetric features, such as amplitude features and polarimetric decomposition features, are susceptible to the influence of target scattering diversity, often leading to reduced interpretation performance. …”
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TSFANet: Trans-Mamba Hybrid Network with Semantic Feature Alignment for Remote Sensing Salient Object Detection by Jiayuan Li, Zhen Wang, Nan Xu, Chuanlei Zhang

“…These challenges mainly manifest as complex backgrounds, extreme scale variations, and topological irregularities, which severely affect detection performance. However, the deeper underlying issue lies in how to effectively align and integrate local detail features with global semantic information. …”
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GLFFNet: Global–Local Feature Fusion Network for High-Resolution Remote Sensing Image Semantic Segmentation by Saifeng Zhu, Liaoying Zhao, Qingjiang Xiao, Jigang Ding, Xiaorun Li

“…Although hybrid models based on convolutional neural network (CNN) and Transformer can extract features encompassing both global and local information, they still face two challenges in addressing the semantic segmentation task of high-resolution remote sensing (HR²S) images. …”
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MTAD-TF: Multivariate Time Series Anomaly Detection Using the Combination of Temporal Pattern and Feature Pattern by Q. He, Y. J. Zheng, C.L. Zhang, H. Y. Wang

“…In the prediction part, multiscale convolution and graph attention network are mainly used to capture information in temporal pattern with feature pattern. The threshold selection part uses the root mean square error between the predicted value and the actual value to perform extreme value analysis to obtain the threshold. …”
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Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusion by Yice Cao, Chenchen Liu, Zhenhua Wu, Lei Zhang, Lixia Yang

“…CVMH-UNet comprises the following two core modules: the hybrid visual state space block (HVSSBlock) and the multi-frequency multi-scale feature fusion block (MFMSBlock). The HVSSBlock integrates convolutional branches to enhance local feature extraction while employing a cross 2D scanning method (CS2D) to capture global information from multiple directions, enabling the synergistic modeling of global and local features. …”
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MFSM-Net: Multimodal Feature Fusion for the Semantic Segmentation of Urban-Scale Textured 3D Meshes by Xinjie Hao, Jiahui Wang, Wei Leng, Rongting Zhang, Guangyun Zhang

“…Methodologically, the 3D feature extraction branch computes the centroid coordinates and face normals of mesh faces as initial 3D features, followed by a multi-scale Transformer network to extract high-level 3D features. …”
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Improved cancer detection through feature selection using the binary Al Biruni Earth radius algorithm by El-Sayed M. El-Kenawy, Nima Khodadadi, Marwa M. Eid, Ehsaneh Khodadadi, Ehsan Khodadadi, Doaa Sami Khafaga, Amel Ali Alhussan, Abdelhameed Ibrahim, Mohamed Saber

“…However, not all this data is useful, as many features are redundant or irrelevant, which can reduce the accuracy of machine learning models. …”
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Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD by ZHANG Dongxing, YANG Gang, ZHOU Ao, QIN Limu, WEI Yuqian, YAN Lei

“…Firstly, a bearing fault dynamic model based on the rigid-flexible coupling of bearing-vehicle was constructed, and the vibration signal of the axle box under the wheel-rail disturbance and the faulty bearing was extracted. …”
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Evaluating the Effectiveness of Dimensionality Reduction on Machine Learning Algorithms in Time Series Forecasting by Rida Zaheer, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Ramzan Talib

“…The findings reveal that the choice of dimensionality reduction technique significantly influences the performance of these models. Certain methods excel at uncovering underlying patterns and improving predictive accuracy, while others offer computational advantages. …”
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Optimizing Heart Disease Prediction: A Comparative Analysis of Tree-Based Ensembles With Feature Expansion and Selection by K. Aswini, Kriti Arya

“…Three hyperparameter tuning strategies were used to train and tune seven tree-based ensemble models to identify the best-performing approach. The results identified Decision Tree-Based Recursive Feature Elimination (DTRFECV) with AdaBoost optimized for grid search as the most effective model, achieving 98.20% sensitivity, 97.98% F1 score, and 97.75% accuracy. …”
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Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering by Aya Fujiwara, Sunao Nakanowatari, Yohei Cho, Toshiaki Taniike

“…The resulting models are utilized to extract catalyst design rules, revealing key synergistic combinations in high-performing catalysts. …”
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