Learning to solving vehicle routing problems via local–global feature fusion transformer by Wei Li, Bing Tian Dai, Xueming Yan, Junying Zou, Zhijie Liang, Jingwen Li

“…In addition, we design a Proximity-Guided Attention (PGA) mechanism that dynamically fuses local and global contexts based on node proximity, enabling the model to focus on more relevant decision-making information while reducing sensitivity to distributional shifts. …”
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Intelligent Recognition of Rock Mass Discontinuities on the Basis of RGB-Enhanced Point Cloud Features by Honghai Cui, Junqi Chen, Xinyue Wang, Zihan Zhao, Jiali Han, Qi Sun, Wen Zhang

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Mutual information-based radiomic feature selection with SHAP explainability for breast cancer diagnosis by Oladosu Oyebisi Oladimeji, Hamail Ayaz, Ian McLoughlin, Saritha Unnikrishnan

“…SHAP methodology elucidates feature importance, aiding model interpretation. Compared to deep learning methods, MIRFS outperforms both deep learning and existing machine learning approaches, promising advancements in breast cancer diagnosis and treatment.…”
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Intelligent recognition method for urban road grid patterns by fusing mesh and road features by Zhekun Huang, Haizhong Qian, Zhongxiang Cai, Andong Wang, Junwei Wang, Feng Xiong

“…Then, the research focus is refined to the road segment level, where the preliminary results are optimized by extracting the geometric and directional features of the road segments. This method significantly outperformed methods based on single research objects across several evaluation metrics, with a performance improvement ranging from 0.79–44.87%. …”
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CB-MTE: Social Bot Detection via Multi-Source Heterogeneous Feature Fusion by Meng Cheng, Yuzhi Xiao, Tao Huang, Chao Lei, Chuang Zhang

“…Traditional bot detection methods, constrained by single-source features, often fail to capture the complete behavioral and contextual characteristics of social bots, especially their dynamic behavioral evolution and group coordination tactics, resulting in feature incompleteness and reduced detection performance. …”
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DRFW-TQC: Reinforcement Learning for Robotic Strawberry Picking with Dynamic Regularization and Feature Weighting by Anping Zheng, Zirui Fang, Zixuan Li, Hao Dong, Ke Li

“…To address these challenges, this paper proposes an enhanced reinforcement learning framework DRFW-TQC that integrates Dynamic L2 Regularization for adaptive model stabilization and a Group-Wise Feature Weighting Network for discriminative feature representation. …”
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Few-shot object detection for pest insects via features aggregation and contrastive learning by Shuqian He, Shuqian He, Biao Jin, Biao Jin, Xuechao Sun, Wenjuan Jiang, Wenjuan Jiang, Jiaxing Gu, Fenglin Gu

“…Our methodology involves multi-scale feature extraction using a Feature Pyramid Network (FPN), enabling the capture of rich semantic information across various scales. …”
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Bayesian-optimized stacking ensemble with multimodal feature selection for tobacco leaf maturity classification by Yunpeng Lu, Yang Wu, Lu Zhang, Jinguo Huang, Guangwei Sun, Jing Liu

“…Subsequently, we utilize the prediction results of these base models as new features inputted into a meta-model. …”
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MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…On this basis, the model designs a dynamic attention unit that includes both local and global attention to generate comprehensive feature maps and calculates difference weights through linear fusion, further strengthening the feature expression ability to more accurately capture change information. …”
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A Hierarchical Feature Fusion and Dynamic Collaboration Framework for Robust Small Target Detection by Xu Yan, Junliang Du, Xuan Li, Xiaoye Wang, Xiaoxuan Sun, Pochun Li, Hongye Zheng

“…Ablation studies further validate the complementarity of HFA and DCCA in optimizing model performance, confirming the algorithm’s superiority and robustness in complex scenarios. …”
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Multi-Scale Vision Transformer with Optimized Feature Fusion for Mammographic Breast Cancer Classification by Soaad Ahmed, Naira Elazab, Mostafa M. El-Gayar, Mohammed Elmogy, Yasser M. Fouda

“…<b>Methods</b>: Traditional deep learning models often struggle with feature redundancy, suboptimal feature fusion, and inefficient selection of discriminative features, leading to limitations in classification performance. …”
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Features of Combustion of Water-fuel Emulsions with Different Methods of Introducing Water into the Combustion Zone by Kolbasenko O.V., Dymo B.V., Anastasenko S.M., Shostak O.V.

“…To achieve the goal, the experimental study of combustion features was carried out on a combustion chamber model when supplying unwatered fuel oil of the M40 type, fuel oil, and water vapour in the amount equivalent to 30% of the mass of the fuel mixture, and two types of water-fuel emulsion with a water content of 15% and 30%. …”
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Development of brain metastases in non-small-cell lung cancer: high-risk features by Nolan Winslow, Jacqueline Boyle, William Miller, Yanzhi Wang, Francois Geoffroy, Andrew J Tsung

“…Available clinical, radiographic and molecular data were reviewed for association with BM and overall survival. A predictive model for BM development was created for multivariate analysis.Results: Risk factors for new BM development in NSCLC included younger age, larger primary lung tumor, Karnofsky performance score (KPS) <70, pre-existing liver or bone metastases, large cell histology and family history of cancer. …”
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Joint Sparse Local Linear Discriminant Analysis for Feature Dimensionality Reduction of Hyperspectral Images by Cong-Yin Cao, Meng-Ting Li, Yang-Jun Deng, Longfei Ren, Yi Liu, Xing-Hui Zhu

“…Although linear discriminant analysis (LDA)-based subspace learning has been widely applied to hyperspectral image (HSI) classification, the existing LDA-based subspace learning methods exhibit several limitations: (1) They are often sensitive to noise and demonstrate weak robustness; (2) these methods ignore the local information inherent in data; and (3) the number of extracted features is restricted by the number of classes. To address these drawbacks, this paper proposes a novel joint sparse local linear discriminant analysis (JSLLDA) method by integrating embedding regression and locality-preserving regularization into the LDA model for feature dimensionality reduction of HSIs. …”
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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

“…To further optimize performance, the models were combined into a voting ensemble, improving feature selection and predictive accuracy. …”
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A computer-based method for the automatic identification of the dimensional features of human cervical vertebrae by Nicola Cappetti, Luca Di Angelo, Carlotta Fontana, Antonio Marzola

“…This study addresses these limitations by introducing a novel computer-based method for automatically identifying the dimensional features of human cervical vertebrae, leveraging 3D geometric models obtained from CT or 3D scanning. …”
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Distinguishing novel coronavirus influenza A virus pneumonia with CT radiomics and clinical features by Lianyu Sui, Huan Meng, Jianing Wang, Wei Yang, Lulu Yang, Xudan Chen, Liyong Zhuo, Lihong Xing, Yu Zhang, Jingjing Cui, Xiaoping Yin

“…And then combining these features of the two to construct a combined model. …”
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Machine learning techniques for imbalanced multiclass malware classification through adaptive feature selection by Binayak Panda, Sudhanshu Shekhar Bisoyi, Sidhanta Panigrahy, Prithviraj Mohanty

“…Furthermore, this work proposes a novel adaptive feature selection (AFS) technique using the greedy strategy on term frequency and inverse document frequency (TF-IDF) feature weights to address the selection of influential features and ensure better performance metrics in imbalanced multiclass malware classification problems. …”
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Added wave resistance prediction and comparative study based on point cloud feature extraction by Mingfeng WU, Renchuan ZHU, Dekang XU

“…ObjectiveIn order to rapidly forecast added wave resistance in the ship design stage, this paper proposes a neural network based on point cloud feature extraction. MethodsTaking the Series 60 as an example, the corresponding added wave resistance prediction model is set up and compared with the traditional model based on the principal design parameters. …”
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Dual-environment feature fusion-based method for estimating building-scale population distributions by Guangyu Liu, Rui Li, Jing Xia, Zhaohui Liu, Jing Cai, Huayi Wu, Mingjun Peng

“…Finally, a regression model was established using dual-environment features for building-scale population estimation. …”
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