Ensemble Voting Method for Phonocardiogram Heart Signal Classification Using FFT Features by Adisaputra Zidha Noorizki, Heri Pratikno, Weny Indah Kusumawati

“…Ensemble learning with soft voting was also applied to combine the strengths of each model. Although the ensemble model showed strong performance with 92% accuracy and ROC AUC of 0.9636, it did not provide significant improvement over the base model. …”
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Efficient Multi-Task Training with Adaptive Feature Alignment for Universal Image Segmentation by Yipeng Qu, Joohee Kim

“…We evaluate our model performance on the ADE20K and Cityscapes datasets. …”
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Optimizing Solar Radiation Prediction with ANN and Explainable AI-Based Feature Selection by Ibrahim Al-Shourbaji, Abdalla Alameen

“…This paper presents an Artificial Neural Network (ANN) model optimized using feature selection techniques based on Explainable AI (XAI) methods to enhance SR prediction performance. …”
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Underwater vessel sound recognition based on multi-layer feature and attention mechanism by Wei Wei, Jing Li, Yucheng Han, Lili Zhang, Ning Cui, Pei Yu, Hongxin Tan, Xudong Yang, Kang Yang

“…The model adjusts the weights of the feature map dynamically by learning the correlation between dimensions through Squeeze and Excitation Block (SE-Block), which enables the model to capture the contextual information, thus the model performance is improved. …”
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TEDformer: Temporal Feature Enhanced Decomposed Transformer for Long-Term Series Forecasting by Jiayi Fan, Bingyao Wang, Dong Bian

“…To address these issues, we combined the excellent performance of the time convolutional neural network (TCN) on time series data and the advantages of the STL inner-outer loop decomposition to design the TEDformer, a Transformer prediction model enhanced with global and local temporal features. …”
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Sparse Linear Discriminant Analysis With Constant Between-Class Distance for Feature Selection by Shuangle Guo, Yongxia Li, Jianguang Zhang, Yue Liu, Tian Tian, Mengchen Guo

“…Since the SLDA-CBD model is rooted in TR-LDA, it ensures the discriminative performance of the selected features. …”
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Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features by Qiumeng Xi, Juanni Gong, Jianfeng Wang, Xiaojuan Guo, Yuanhua Yang, Xiuzhang lv, Suqiao Yang, Yidan Li

“…SHapley Additive exPlanations (SHAP) values were applied to interpret feature importance of the predictive model. Results A total of 135 patients were included to construct models. 6 features were selected from 49 variables for model training. …”
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Feature Screening via Mutual Information Learning Based on Nonparametric Density Estimation by Shengbin Zhou, Tao Wang, Yejin Huang

“…Firstly, the proposed procedure is model-free without specifying any relationship between the predictors and the response and is valid under a wide range of model settings including parametric and nonparametric models. …”
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Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion by Yuhang Yang, Yuanqing Luo, Yingyu Yang, Shuang Kang

“…Experimental evaluations reveal that on our self-curated KRHO dataset, all performance metrics approach 1.00, and the overall classification accuracy reaches an impressive 99.33%, surpassing existing mainstream models. …”
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A Review on Machine Learning Deployment Patterns and Key Features in the Prediction of Preeclampsia by Louise Pedersen, Magdalena Mazur-Milecka, Jacek Ruminski, Stefan Wagner

“…However, they have not addressed the intended deployment of these models throughout pregnancy, nor have they detailed feature performance. …”
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Multiview Multimodal Feature Fusion for Breast Cancer Classification Using Deep Learning by Sadam Hussain, Mansoor Ali Teevno, Usman Naseem, Daly Betzabeth Avendano Avalos, Servando Cardona-Huerta, Jose Gerardo Tamez-Pena

“…However, most DL methods have relied on unimodal features, which may limit the performance of diagnostic models. …”
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Does Social Media Advertising Features Matter An Investigation of Consumer Purchase Intention by Hafiz Wajahat Ali Shah, Imran Khan, Abdul Hameed

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Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement by Jing Wang, Yanru Wang, Dan Yuan, Yuxiang Que, Weichao Huang, Yuan Wei

“…To overcome this problem, this paper constructs a semantic enhancement model, which utilizes multi-layer feature representations extracted from deep networks, and correlates and fuses shallow features with deep features by using cross-attention. …”
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Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities by Yongsheng Zhao, Yuxing Gao, Xu Yang, Luyang Yang

“…Secondly, the Attention-Enhanced Feature Fusion Framework (AEFF) is introduced to optimize both cross-modal and intra-modal feature representations by employing an attention mechanism, effectively boosting model performance. …”
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Multi-channel Convolutional Neural Network Feature Extraction for Session Based Recommendation by Zhenyan Ji, Mengdan Wu, Yumin Feng, José Enrique Armendáriz Íñigo

“…Existing session-based recommendation systems usually model a session into a sequence and extract sequence features through recurrent neural network. …”
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Multi-Scale Feature Pyramid Network With Camera Artifact Augmentation for Pedestrian Detection by Ankit Shrivastava, Shanmugam Poonkuntran

“…This paper presents a novel hybrid model termed as multi-scale feature pyramid network with camera artifact augmentation (MSFPN-CAA) that integrates Feature Pyramid Networks (FPN) with a fine-tuned YOLOv10 model for enhanced pedestrian detection. …”
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Liver segmentation network based on detail enhancement and multi-scale feature fusion by Lu Tinglan, Qin Jun, Qin Guihe, Shi Weili, Zhang Wentao

“…Furthermore, to enable the model to better learn liver features at different scales, a Multi-Scale Feature Fusion module (MSFF) is added to the skip connections in the model. …”
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Stacked Ensemble Learning for Classification of Parkinson’s Disease Using Telemonitoring Vocal Features by Bolaji A. Omodunbi, David B. Olawade, Omosigho F. Awe, Afeez A. Soladoye, Nicholas Aderinto, Saak V. Ovsepian, Stergios Boussios

“…Feature selection results showed that using the top 10 features ranked by gain ratio provided optimal balance between performance and clinical interpretability. …”
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The impact of feature selection techniques on effort‐aware defect prediction: An empirical study by Fuyang Li, Wanpeng Lu, Jacky Wai Keung, Xiao Yu, Lina Gong, Juan Li

“…Previous studies indicated that some feature selection methods could improve the performance of Classification‐Based Defect Prediction (CBDP) models, and the Correlation‐based feature subset selection method with the Best First strategy (CorBF) performed the best. …”
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A New Hybrid PSO-HHO Wrapper Based Optimization for Feature Selection by Sumbul Azeem, Shazia Javed, Iftikhar Naseer, Oualid Ali, Taher M. Ghazal

“…These attributes hinder the performance of predictive models. Therefore, an effective preprocessing feature selection procedure is essential to identify the relevant features and eliminate unnecessary ones. …”
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