Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation by Yu Guo, Ziyi Cheng, Yuanlong Zhang, Gaoxuan Wang, Jundong Zhang

“…The proposed approach addresses the critical challenge of generalizing classification models across diverse datasets, demonstrating significant improvements in performance. …”
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Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition by Hao Chao, Liang Dong, Yongli Liu, Baoyun Lu

“…Finally, a deep learning model designed with two principal component analysis convolutional layers and a nonlinear transformation operation extracted the spatial characteristics and global interchannel synchronization features from the constructed feature images, which were then input to support vector machines to perform the emotion recognition tasks. …”
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User-Independent Activity Recognition via Three-Stage GA-Based Feature Selection by Theresia Ratih Dewi Saputri, Adil Mehmood Khan, Seok-Won Lee

“…Secondly, different activities performed by the same person could have different underlying models. …”
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Identifying Influential Nodes in Complex Networks via Transformer with Multi-Scale Feature Fusion by Tingshuai Jiang, Yirun Ruan, Tianyuan Yu, Liang Bai, Yifei Yuan

“…Through the transformer module, node information is effectively aggregated, thereby improving the model’s ability to recognize key nodes. We perform evaluations using six real-world and three synthetic network datasets, comparing our method against multiple baselines using the SIR model to validate its effectiveness. …”
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Dental bur detection system based on asymmetric double convolution and adaptive feature fusion by HongLing Hou, Ao Yang, Xiangyao Li, Kangkai Zhu, Yandi Zhao, Zhiqiang Wu

“…A Lightweight Asymmetric Dual Convolution module (LADC) was devised to diminish the detrimental effects of extraneous features on the model’s precision, thereby enhancing the feature extraction network. …”
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Unveiling key pathomic features for automated diagnosis and Gleason grade estimation in prostate cancer by Valentina Brancato, Mario Verdicchio, Carlo Cavaliere, Francesco Isgrò, Marco Salvatore, Marco Aiello

“…Focusing on the problem of prostate cancer (PCa) diagnosis and grading, this study aims to detect which are the most discriminant features for distinguishing malignant from non-malignant tissue and Gleason patterns, leaving the evaluation of models’ classification performances as a secondary goal. …”
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Detecting sand gradation based on the two-dimensional sand particle features in sand images by Chuanyun Xu, Heng Wang, Yang Zhang, Song Sun, Gang Li

“…Existing machine learning-based automated detection methods have limited accuracy, and deep learning is hindered by difficulties in image data annotation and the lack of high-quality datasets, which affect its application effectiveness.To address this, this paper proposes a detection method based on the two-dimensional sand particle features in sand images. By extracting these sand particle features to train a network model and combining a threshold division strategy, sand gradation detection is performed. …”
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Forecasting dengue in Bangladesh using meteorological variables with a novel feature selection approach by Mahadee Al Mobin

“…A novel wrapper feature selection algorithm employing a custom objective function is proposed in this study, which significantly improves model accuracy by 12.63% and reduces the mean absolute percentage error by 70.82%. …”
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An Assessment of the Map-Style Influence on Generalization with CycleGAN: Taking Line Features as an Example by Heng Yu, Haoxuan Chen, Ling Zhang

“…This study explores the impact of different map symbolization configurations on generative models, specifically using CycleGAN for line feature generalization. …”
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Tree-Based Algorithms and Incremental Feature Optimization for Fault Detection and Diagnosis in Photovoltaic Systems by Khaled Chahine

“…A comprehensive dataset analysis is conducted to improve the dataset quality and uncover intricate relationships between features and the target variable. By introducing novel feature importance averaging techniques, a two-phase fault detection and diagnosis framework employing tree-based models is proposed to identify faults from normal cases and diagnose the fault type. …”
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Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning by Ickbum Kim, Sandeep Singh

“…Deep learning architectures have previously been explored to perform low-dimensional localization with limited success. …”
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Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features by Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, Jonas Honer, Wibke Deisting, Jonas Anlahr, Anja Hoenig, Julia Czwikla, Eva Sienz-Widmann, Doris Rau, Austin J. Rice, Timothy P. Riley, Danqing Li, Hannah B. Catterall, Christine E. Tinberg, Jeffrey J. Gray, Kathy Y. Wei

“…On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task ([Formula: see text] 0.52). …”
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Urban object detection algorithm based on feature enhancement and progressive dynamic aggregation strategy by Luxuan Bian, Zijun Gao, Jue Wang, Bo Li

“…First, the dual-gated feature mechanism enables selective suppression or enhancement of multilevel features, thereby reducing the interference of complex environmental noise in remote sensing on feature fusion. …”
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A quantitative benchmark of neural network feature selection methods for detecting nonlinear signals by Antoine Passemiers, Pietro Folco, Daniele Raimondi, Giovanni Birolo, Yves Moreau, Piero Fariselli

“…Recently, a proliferation of Deep Learning (DL) models has emerged to tackle both FS and prediction at the same time, allowing non-linear modeling of the selected features. …”
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A Methodology to Extract Knowledge from Datasets Using ML by Ricardo Sánchez-de-Madariaga, Mario Pascual Carrasco, Adolfo Muñoz Carrero

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Comparison of clinical features and inflammatory factors between patients with bipolar depression and unipolar depression by Tianwei Zhang, Changjun Ji, Jiayu Zhu, Xiaoxiao Wang, Chengjia Shen, Fei Liang, Yajun Hou, Yan Sun, Chongze Wang, Peijuan Wang, Guoqiang Lu, Xiaohui Wang, Qinyu Lv, Zhenghui Yi

“…Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive value of clinical features and biological markers for bipolar depression. …”
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Visual SLAM algorithm for underground robots in coal mines based on point-line features by Li WANG, Tianxiang ZANG, Bo SU

“…The reprojection error model of point features and line features is constructed, the angle constraints are added to the line feature residual model, the Jacobian matrix of the pose increment of point features and line features is derived, the unified cost function of the reprojection error of point features and line features is established, the local mapping thread uses the ORB-SLAM3 classic local optimization method to adjust the pose of points, line features and keyframes, and performs loop correction, subgraph fusion and global BA(Bundle Adjustment) in the back-end thread. …”
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FEMNet: A Feature-Enriched Mamba Network for Cloud Detection in Remote Sensing Imagery by Weixing Liu, Bin Luo, Jun Liu, Han Nie, Xin Su

“…In this study, we propose FEMNet, a lightweight dual-branch network that combines state space modeling with convolutional encoding for multi-class cloud segmentation. …”
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Multipath Suppression and High-precision Angle Measurement Method Based on Feature Game Preprocessing by Houhong XIANG, Yongliang WANG, Yuxi LI, Yufeng CHEN, Fengyu WANG, Xiaolu ZENG

“…Compared to existing supervised learning models, the proposed algorithm offers better generalization to unknown signal parameters and multipath distribution models.…”
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Empirical studies on the impact of filter‐based ranking feature selection on security vulnerability prediction by Xiang Chen, Zhidan Yuan, Zhanqi Cui, Dun Zhang, Xiaolin Ju

“…Then we consider seven methods from three FRFS categories for FRFS and use a random forest classifier to construct SVP models. Final results show that given the similar code inspection cost, using FRFS can improve the performance of SVP when compared with state‐of‐the‐art baselines. …”
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