A hybrid clustering and boosting tree feature selection (CBTFS) method for credit risk assessment with high-dimensionality by Jianxin Zhu, Xiong Wu, Lean Yu, Xiaoming Zhang

“… To solve the high-dimensional issue in credit risk assessment, a hybrid clustering and boosting tree feature selection method is proposed. In the hybrid methodology, an  improved minimum spanning tree model is first used to remove redundant and irrelevant  features. …”
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Classification of Wetlands in the Liaohe Estuary Based on MRMR-RF-CV Feature Preference of Multisource Remote Sensing Images by Lina Ke, Shilin Zhang, Yao Lu, Nan Lei, Changkun Yin, Qin Tan, Lei Wang, Daqi Liu, Quanming Wang

“…Results revealed that: 1) The MRMR-RF optimized 40 features, ranked by importance as Sentinel-2 spectral &gt; Sentinel-1 index &gt; Sentinel-1 radar &gt; topographic &gt; Sentinel-1 texture; 2) Six sets of comparison schemes were established, and the classification scheme based on the MRMR-RF model achieved the best classification performance, with an overall accuracy of 90.89% and a Kappa coefficient of 0.9; 3) The Liaohekou Estuary wetland was predominantly composed of <italic>Phragmites australis (P.australis),</italic> shallow sea, and tidal flat, followed by cropland, rivers, breeding pools, <italic>Suaeda salsa</italic> (<italic>S.salsa)</italic>, reservoirs, puddles, bare soil, and building sites as secondary components; and 4) Between 2000 and 2023, the wetland area of different types in the study area changed significantly, with the changes mainly concentrated in the coastal aquaculture areas, tidal flat areas, and <italic>S.salsa</italic> growth areas.…”
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EMHANet: Lightweight Salient Object Detection for Remote Sensing Images via Edge-Aware Multiscale Feature Fusion by Qian Tang, Zhen Wang, Xuqi Wang, Shan-Wen Zhang

“…To address these issues, we propose EMHANet, a lightweight network that integrates edge texture detail extraction, multi-scale feature fusion, and hybrid attention mechanism. EMHANet consists of MobileNetV3 for feature extraction, an Edge Feature Integration Module (EFIM) for low-level edge details, a Multi-scale Contextual Information Enhancement Module (MCIEM) for high-level feature refinement, and a lightweight decoder for saliency prediction. …”
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Accurate Localization Method Combining Optimized Hybrid Neural Networks for Geomagnetic Localization with Multi-Feature Dead Reckoning by Suqing Yan, Baihui Luo, Xiyan Sun, Jianming Xiao, Yuanfa Ji, Kamarul Hawari bin Ghazali

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Deep TPS-PSO: Hybrid Deep Feature Extraction and Global Optimization for Precise 3D MRI Registration by Gayathri Ramasamy, Tripty Singh, Xiaohui Yuan, Ganesh R Naik

“…The method combines a 3D ResNet encoder to extract volumetric features, a Thin Plate Spline (TPS) model to capture smooth anatomical deformations, and Particle Swarm Optimization (PSO) to estimate transformation parameters efficiently without relying on gradients. …”
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INFLUENCE OF LINING THERMAL PERFORMANCE IN ELECTRIC-ARC FURNACES ON POWER CONSUMPTION by S.. V. Korneev

“…The paper presents an analysis of specific features of lining thermal performance in electric-arc furnaces at various technological periods. …”
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MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition by Khan Abrar Shams, Md. Rafid Reaz, Mohammad Ryan Ur Rafi, Sanjida Islam, Md. Shahriar Rahman, Rafeed Rahman, Md. Tanzim Reza, Mohammad Zavid Parvez, Subrata Chakraborty, Biswajeet Pradhan, Abdullah Alamri

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BeliN: A novel corpus for Bengali religious news headline generation using contextual feature fusion by Md Osama, Ashim Dey, Kawsar Ahmed, Muhammad Ashad Kabir

“…Existing approaches to headline generation typically rely solely on the article content, overlooking crucial contextual features such as sentiment, category, and aspect. This limitation significantly hinders their effectiveness and overall performance. …”
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Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction by Azeddine Mjahad, Alfredo Rosado-Muñoz

“…Experimental results demonstrate that combining Convolutional Neural Network (CNN)-based feature extraction with OCSVM significantly improves anomaly detection performance compared with simpler handcrafted approaches. …”
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LMSFA-YOLO: A lightweight target detection network in Remote sensing images based on Multiscale feature fusion by Yuanbo Chu, Jiahao Wang, Longhui Ma, Chenxing Wu

“…Subsequently, the mixed local channel attention (MLCA) is combined to create an effective mixed channel attention spatial pyramid pooling (EMCASPP), aiming to simultaneously integrate local and channel space information to enhance the feature fusion ability of the model. To further improve the precision of feature extraction and preserve detailed information, a high-resolution shallow feature layer is applied. …”
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SAFH-Net: A Hybrid Network With Shuffle Attention and Adaptive Feature Fusion for Enhanced Retinal Vessel Segmentation by Yang Zhou Ling Ou, Joon Huang Chuah, Hua Nong Ting, Shier Nee Saw, Jun Zhao

“…A series of experimental results demonstrates that the proposed model significantly outperforms other advanced methods in segmentation performance.…”
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Computer-aided diagnosis of hepatic cystic echinococcosis based on deep transfer learning features from ultrasound images by Miao Wu, Chuanbo Yan, Gan Sen

“…The proposed CAD system adopts the concept of deep transfer learning and uses a pre-trained convolutional neural network (CNN) named VGG19 to extract deep CNN features from the ultrasound images. The proven classifier models, k - nearest neighbor (KNN) and support vecter machine (SVM) models, are integrated to classify the extracted deep CNN features. 3 distinct experiments with the same deep CNN features but different classifier models (softmax, KNN, SVM) are performed. …”
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The fecal microbiota of Holstein cows is heritable and genetically correlated to dairy performances by L. Brulin, S. Ducrocq, J. Estellé, G. Even, S. Martel, S. Merlin, C. Audebert, P. Croiseau, M.-P. Sanchez

“…Genetic parameters were calculated using either univariate or bivariate animal models. Heritabilities estimates, ranging from 0.08 to 0.31 for taxa abundances and β-diversity indices, highlight the influence of the host genetics on the composition of the fecal microbiota. …”
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Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy by Wenlong Liao, Jiannong Fang, Birgitte Bak-Jensen, Guangchun Ruan, Zhe Yang, Fernando Porté-Agel

“…Besides, it outperforms most benchmark models and exhibits comparable performance to the best-performing neural networks. …”
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Multimodal Raga Classification from Vocal Performances with Disentanglement and Contrastive Loss by Sujoy Roychowdhury, Preeti Rao

“…Using an available dataset of Hindustani raga performances by 11 singers, we extract features from audio and video (gesture) and apply deep learning models to classify the raga from short excerpts. …”
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GM2FFNet: Grouped Multiscale Multiangle Feature Fusion Network With Center Attention for Hyperspectral Image Classification by Junding Sun, Haoxiang Dong, Yanlong Gao, Xiaosheng Wu, Jianlong Wang, Yudong Zhang

“…Convolutional neural networks and transformers have been extensively utilized in hyperspectral image classification due to their exceptional feature learning capabilities. However, many existing patch-based classification methods often neglect the fusion of multiscale and multiangle features and cannot fully capture the relationships between the central pixel and its neighboring pixels, which is likely to compromise the classification performance. …”
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A visual positioning method for tunnel boring machines in underground coal mines based on anchor net features by Xuhui ZHANG, Yunkai CHI, Yuyang DU, Junying JIANG, Wenjuan YANG, Youjun ZHAO, Jicheng WAN, Yanqun WANG, Chenhui TIAN

“…A pose estimation model with minimized reprojection errors of line features was constructed. …”
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GNSS-Based Multi-Target RDM Simulation and Detection Performance Analysis by Jinxing Li, Qi Wang, Meng Wang, Youcheng Wang, Min Zhang

“…This paper proposes a novel Global Navigation Satellite System (GNSS)-based remote sensing method for simulating Radar Doppler Map (RDM) features through joint electromagnetic scattering modeling and signal processing, enabling characteristic parameter extraction for both point and ship targets in multi-satellite scenarios. …”
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Landslide mapping with deep learning: the role of pre-/post-event SAR features and multi-sensor data fusion by Aiym Orynbaikyzy, Frauke Albrecht, Wei Yao, Mahdi Motagh, Wandi Wang, Sandro Martinis, Simon Plank

“…Additionally, we assess the impact of increasing the number of pre-/post-event SAR observations on classification performance. The U-Net models are trained and tested using globally distributed and limited reference data (563 unique patches). …”
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YOLOFIV: Object Detection Algorithm for Around-the-Clock Aerial Remote Sensing Images by Fusing Infrared and Visible Features by Huiying Wang, Chunping Wang, Qiang Fu, Binqiang Si, Dongdong Zhang, Renke Kou, Ying Yu, Changfeng Feng

“…We evaluate the proposed method YOLOFIV on the widely used drone vehicle dataset, YOLOFIV achieves an accuracy of 64.71&#x0025; (in terms of <inline-formula><tex-math notation="LaTeX">$\text{mean average precision}_{0.5}$</tex-math></inline-formula>), accuracy improvement of 8.32&#x0025; over baseline bimodal model, similar performance to UACMD designed for ARSI object detection but with 92.35&#x0025; reduction in parameter count, and 17.87 times speedup. …”
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