A Hybrid Sequential Feature Selection Approach for Identifying New Potential mRNA Biomarkers for Usher Syndrome Using Machine Learning by Rama Krishna Thelagathoti, Wesley A. Tom, Dinesh S. Chandel, Chao Jiang, Gary Krzyzanowski, Appolinaire Olou, M. Rohan Fernando

“…The selected biomarkers were further validated using multiple machine learning models, including Logistic Regression, Random Forest, and Support Vector Machines, demonstrating robust classification performance. …”
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6D Pose Estimation Algorithm Based on Improved YOLOv5 With Asymptotic Feature Pyramid Network and Attention Mechanism by Yan Zhang, Hanyu Ye

“…Additionally, the neck component is enhanced by replacing the BiFPN (Bi-directional Feature Pyramid Network) with the AFPN (Asymptotic Feature Pyramid Network), and the EMA module is integrated into the three C2F modules to boost multi-scale feature fusion and model stability. …”
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Ghost-Attention-YOLOv8: Enhancing Rice Leaf Disease Detection with Lightweight Feature Extraction and Advanced Attention Mechanisms by Thanh Dang Bui, Tra My Do Le

“…The Ghost model optimizes feature extraction by reducing computational complexity, while the attention modules enable the model to focus on relevant regions, improving detection performance. …”
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Transformer fault diagnosis using machine learning: a method combining SHAP feature selection and intelligent optimization of LGBM by Cheng Liu, Weiming Yang

“…Subsequently, the Shapley Additive Explanations (SHAP) method is employed to evaluate feature importance and select a subset that significantly influences model predictions, thereby simplifying the model and enhancing its interpretability. …”
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Enhance health evidence quality in classification tasks: A triangulation approach utilizing case-based reasoning and process features by Ruihua Guo, Ross Smith, Qifan Chen, Angus Ritchie, Simon Poon

“…The proportion of cases misclassified by any of the six models decreased by 47% after incorporating process features (from 5.29% to 2.91%) and further decreased to 0.0% after applying the QCA solutions. …”
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Numerical Analysis of Fluid‒solid Interactions in a 3D Piezoelectric Micropump Featuring a Passive Check Valve by Y. F. Zhang, B. Zhou, Z. L. Deng

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Nomogram prediction of molecular characteristics in WHO grade 3–4 diffuse gliomas based on fractal analysis and VASARI features by Changyou Long, Dan Xu, Wenbo Sun, Weiqiang Liang, Jie Zhou, Shen Gui, Huan Li, Haibo Xu

“…This study aims to develop a nomogram model using fractal analysis and Visually AcceSAble Rembrandt Images (VASARI) features to predict the molecular characteristics of WHO Grade 3–4 diffuse gliomas. …”
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Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer by Liangrui Pan, Qingchun Liang, Wenwu Zeng, Yijun Peng, Zhenyu Zhao, Yiyi Liang, Jiadi Luo, Xiang Wang, Shaoliang Peng

“…VERN captures spatial topological features with feature sharing and skip connections to enhance model training. …”
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An object-based spectral and elevation feature fusion framework for landslide mapping using time-series Landsat-8 imagery by Tsung-Han Wen, Tee-Ann Teo

“…The methodology employs multiresolution segmentation with a fusion of spectral and topographic features, enabling the model to capture complex patterns and improve segmentation quality. …”
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Prediction of MGMT methylation status in glioblastoma patients based on radiomics feature extracted from intratumoral and peritumoral MRI imaging by Wang-Sheng Chen, Fang-Xiong Fu, Qin-Lei Cai, Fei Wang, Xue-Hua Wang, Lan Hong, Li Su

“…This study aimed to develop a radiomic model using MRI-derived features from both regions. …”
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Untangling RFID Privacy Models by Iwen Coisel, Tania Martin

“…Secondly, these models are grouped according to their features (e.g., tag corruption ability). …”
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Enhanced Prediction of Immune Checkpoint Blockade Response in Melanoma Using Ensemble Learning and a Hybrid Feature Selection Technique by Basant Badwy, Nagia M. Ghanem, Karim Banawan, Nour Eldin Ismail

“…We evaluated the performance of the model across five datasets of melanoma patients who received ICB treatment. …”
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Estimating Winter Canola Aboveground Biomass from Hyperspectral Images Using Narrowband Spectra-Texture Features and Machine Learning by Xia Liu, Ruiqi Du, Youzhen Xiang, Junying Chen, Fucang Zhang, Hongzhao Shi, Zijun Tang, Xin Wang

“…Subsequently, machine learning algorithms were applied to develop estimation models for winter canola biomass. The results indicate: (1) For spectra features, narrow-bands at 450~510 nm, 680~738 nm, 910~940 nm wavelength, as well as vegetation indices containing red-edge narrow-bands, showed outstanding performance with correlation coefficients ranging from 0.49 to 0.65; For texture features, narrow-band texture parameters CON, DIS, ENT, ASM, and vegetation index texture parameter COR demonstrated significant performance, with correlation coefficients between 0.65 and 0.72; (2) The Adaboost model using the spectra-texture feature scheme exhibited the best performance in estimating winter canola biomass (R² = 0.91; RMSE = 1710.79 kg/ha; NRMSE = 19.88%); (3) The combined use of narrowband spectra and texture feature significantly improved the estimation accuracy of winter canola biomass. …”
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A Crowd Counting and Localization Network Based on Adaptive Feature Fusion and Multi-Scale Global Attention Up Sampling by Min Wang, Li Huang, Jingke Yan, Jin Huang, Tao Yang

“…By reducing redundant features inside and outside the separable branch, the model achieves global fusion of shallow features during the decoding process. …”
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The value of radiomics features of white matter hyperintensities in diagnosing cognitive frailty: a study based on T2-FLAIR imaging by Qinmei Liao, Xihao Hu, Zhiqiong Jiang, Xiaoyun Huang, Jiacheng Guo, Yuanzhong Zhu, Wenjing He

“…Three machine learning algorithms—K-Nearest Neighbors (KNN), Logistic Regression (LR), and Support Vector Machine (SVM)—were used to construct radiomic models, clinical models, and combined models. The performance of each model in diagnosing CF was evaluated using metrics including the area under the curve (AUC), area under the net benefit curve (AUNBC), and Brier score. …”
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Feature-Driven Density Prediction of Maraging Steel Additively Manufactured Samples Using Pyrometer Sensor and Supervised Machine Learning by Rajesh Kumar Balaraman, Shaista Hussain, John Kgee Ong, Qing Yang Tan, Nagarajan Raghavan

“…The performance of these models was enhanced through three hyperparameter optimization (HPO) techniques: Random Search (RS), Grid Search (GS), and Bayes Search (BS), alongside a feature selection (FS) method to refine the input feature dimensions. …”
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YOLORemote: Advancing Remote Sensing Object Detection by Integrating YOLOv8 With the CE-WA-CS Feature Fusion Approach by Ruihan Bai, Guanghan Song, Qiang Wang

“…This research rigorously tests the proposed feature fusion method across various YOLO models, substantiating its versatility and effectiveness in enhancing RSOD. …”
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Forecasting Stock Market Volatility Using Housing Market Indicators: A Reinforcement Learning-Based Feature Selection Approach by Pourya Zareeihemat, Samira Mohamadi, Jamal Valipour, Seyed Vahid Moravvej

“…Current EWS methods often face significant hurdles, including model generalization, feature selection, and hyperparameter optimization challenges. …”
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Improving machine learning algorithm for risk of early pressure injury prediction in admission patients using probability feature aggregation by Shu-Chen Chang, Shu-Mei Lai, Mei-Wen Wu, Shou-Chuan Sun, Mei-Chu Chen, Chiao-Min Chen

“…Conclusion The ML-based approach, coupled with feature aggregation, enhances predictive performance, aiding clinical teams in understanding crucial features and the model's decision-making process.…”
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SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification by Chunyang Wang, Chao Zhan, Bibo Lu, Wei Yang, Yingjie Zhang, Gaige Wang, Zongze Zhao

“…The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). …”
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