Virtual sample based techniques using deep features for SSPP face recognition in unconstrained environment. by Muhammad Tariq Siddique, Ibrahim Venkat, Humera Farooq, Sharul Tajuddin, S H Shah Newaz

“…A deep neural network-based model, FaceNet, was used to extract the features and a support vector machine was used for classification. …”
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CLEAR: Multimodal Human Activity Recognition via Contrastive Learning Based Feature Extraction Refinement by Mingming Cao, Jie Wan, Xiang Gu

“…These strategies are utilized to refine and extract highly discriminative features from various data sources, thereby significantly enhancing the model’s capacity to identify and classify diverse human activities accurately. …”
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Fusion ConvLSTM-Net: Using Spatiotemporal Features to Increase Residential Load Forecast Horizon by Abhishu Oza, Dhaval K. Patel, Bryan J. Ranger

“…We evaluated the model against several benchmark neural network models by: 1) testing different forecast window sizes ranging from 1.5 to 24 hours, 2) assessing model performance across multiple households, and 3) performing large-scale forecasting by aggregating predictions from 100 households. …”
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Artificial Flora Algorithm-Based Feature Selection With Support Vector Machine for Cardiovascular Disease Classification by M. M. Asha, G. Ramya

“…This work proposes a model to identify the presence of Cardiovascular Disease based on various patient features, aiming to enhance prediction accuracy through a powerful feature selection method. …”
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Voice Spoofing Detection Through Residual Network, Max Feature Map, and Depthwise Separable Convolution by Il-Youp Kwak, Sungsu Kwag, Junhee Lee, Youngbae Jeon, Jeonghwan Hwang, Hyo-Jung Choi, Jong-Hoon Yang, So-Yul Han, Jun Ho Huh, Choong-Hoon Lee, Ji Won Yoon

“…The majority of the top-performing solutions from the competition used an ensemble technique that merged numerous sophisticated deep learning models to maximize detection accuracy. …”
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UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism by Shaodan Lin, Deyao Huang, Libin Wu, Zuxin Cheng, Dapeng Ye, Haiyong Weng

“…Results The ConvGAM model, leveraging the ConvNeXt-Large backbone network and the Global Attention Mechanism (GAM), achieves outstanding performance in feature extraction, crucial for detecting small and complex disease patterns. …”
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Clinical and imaging features of co-existent pulmonary tuberculosis and lung cancer: a population-based matching study in China by Fan Zhang, Fei Qi, Yi Han, Hongjie Yang, Yishuo Wang, Guirong Wang, Yujie Dong, Hongxia Li, Yuan Gao, Hongmei Zhang, Tongmei Zhang, Liang Li

“…The pre-diagnostic model exhibited robust performance, achieving area under the curve (AUC) values of 0.864 in the training set and 0.830 in the test set. …”
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Semantic Segmentation Network for Unstructured Rural Roads Based on Improved SPPM and Fused Multiscale Features by Xinyu Cao, Yongqiang Tian, Zhixin Yao, Yunjie Zhao, Taihong Zhang

“…These modules improve the model’s ability to capture both global and local features, addressing the complexity of rural roads. …”
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A Multifunctional Wind Power Conversion System With Sensorless Operation and Power Quality Improvement Features by Abdoalateef Alzhrani, Pranay Krishna Sahay, Sreeram Naveen Chandra, Vedantham Lakshmi Srinivas

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Improving air quality prediction using hybrid BPSO with BWAO for feature selection and hyperparameters optimization by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

“…The Random Forest model achieved the best performance after feature selection with an MSE of 53.93, R² of 0.9710, and reduced fitted time. …”
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Feature Multi-Scale Enhancement and Adaptive Dynamic Fusion Network for Infrared Small Target Detection by Zenghui Xiong, Zhiqiang Sheng, Yao Mao

“…This model is based on a U-Net architecture and incorporates a Residual Multi-Scale Feature Enhancement (RMFE) module and an Adaptive Feature Dynamic Fusion (AFDF) module. …”
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A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition by Hanying Guo, Siying Chen, Yongjiang Zhou, Ting Xu, Yuhao Zhang, Hongliang Ding

“…Compared to similar studies, this model shows superior performance in fatigue driving recognition, which is of significant value for research on fatigue driving detection and prevention.…”
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Detection of Student Engagement via Transformer-Enhanced Feature Pyramid Networks on Channel-Spatial Attention by A. Naveen, I. Jeena Jacob, Ajay Kumar Mandava

“…Additionally, by incorporating the Transformer architecture, the model achieves better overall performance by effectively capturing long-range dependencies and semantic relationships within the input sequences. …”
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Enhanced AlexNet with Gabor and Local Binary Pattern Features for Improved Facial Emotion Recognition by Furkat Safarov, Alpamis Kutlimuratov, Ugiloy Khojamuratova, Akmalbek Abdusalomov, Young-Im Cho

“…Considering the constraints of low hardware specifications often encountered in real-world applications, this study leverages recent advances in deep learning to propose an enhanced model for FER. The model effectively utilizes texture information from faces through Gabor and Local Binary Pattern (LBP) feature extraction techniques. …”
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Application of spectral characteristics of electrocardiogram signals in sleep apnea by Jiayue Hu, Liu Yang, Xintong Zhao, Haicheng Wei, Jing Zhao, Miaomiao Li

“…These features are classified via a random forest machine learning model.ResultsThe femax and IMF7 components of the reconstructed signal exhibited statistically significant differences (p < 0.001) between normal and sleep apnea subjects. …”
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FEL-FRN: fusion ECA long-CLIP feature reconstruction network for few-shot classification by Yuanyuan Wang, Ao Zhang, Jiange Liu, Kexiao Wu, Hauwa Suleiman Abdullahi, Pinrong Lv, Yu Gao, Haiyan Zhang

“…To address this problem, we propose a model called FEL-FRN (fusion ECA Long-CLIP feature reconstruction network). …”
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A Triple-Channel Network for Maritime Radar Targets Detection Based on Multi-Modal Features by Kaiqi Wang, Zeyu Wang

“…Especially when the signal-to-clutter ratio (SCR) is low, it is difficult to achieve high-performance detection. This paper proposes a triple-channel network model for maritime target detection based on the method of multi-modal data fusion. …”
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Design and Efficacy of a Data Lake Architecture for Multimodal Emotion Feature Extraction in Social Media by Yuanyuan Fan, Xifeng Mi

“…Comparative analyses with state-of-the-art models showcase the superior performance of our approach, with an accuracy improvement of 6% on MVSA-Single and 1.6% on MVSA-Multi. …”
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Bearing Lifespan Reliability Prediction Method Based on Multiscale Feature Extraction and Dual Attention Mechanism by Xudong Luo, Minghui Wang

“…Evaluation results based on mean absolute error (MAE) and root mean square error (RMSE) indicated that the dual attention mechanism effectively focused on key features, optimized feature extraction, and improved prediction performance. …”
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PD-YOLO: a novel weed detection method based on multi-scale feature fusion by Shengzhou Li, Zihan Chen, Jialong Xie, Hewei Zhang, Jianwen Guo

“…Building on the YOLOv8n framework, the model introduces a Parallel Focusing Feature Pyramid (PF-FPN), which incorporates two key components: the Feature Filtering and Aggregation Module (FFAM) and the Hierarchical Adaptive Recalibration Fusion Module (HARFM). …”
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