Molecular feature-based classification of retroperitoneal liposarcoma: a prospective cohort study by Mengmeng Xiao, Xiangji Li, Fanqin Bu, Shixiang Ma, Xiaohan Yang, Jun Chen, Yu Zhao, Ferdinando Cananzi, Chenghua Luo, Li Min

“…Methods: RNA sequencing was performed on a training cohort of 88 RPLS patients to identify dysregulated genes and pathways using clusterProfiler. …”
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Footwork recognition and trajectory tracking in track and field based on image processing by Jiaju Zhu, Zhong Zhang, Runnan Liu, Junyi Liu

“…Accurate footwork can effectively improve the performance of professional athletes, and for ordinary trainers, it can reduce the probability of training injuries. To solve the problem that traditional footwork is inaccurate and not well accepted by people, this paper has used an image processing method based on support vector machine (SVM) algorithm to identify and track the footwork. …”
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Quality Judgment of 3D Face Point Cloud Based on Feature Fusion by Gong Gao, Hong Liu, Hongyu Yang

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Neural network model for dependency parsing incorporating global vector feature by Hengjun WANG, Nianwen SI, Yulong SONG, Yidong SHAN

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Grading Related Feature Extraction of Chinese Mitten Crab Based on Machine Vision by Sun Dawei, Li Jiangtao, Li Zhuo, Zhou Chengquan, Lu Yingfeng, Ye Hongbao

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Decoding ’Eligibility Unknown’: transparent classification and feature-based reclassification in CAFV analysis by Muhammad Amir Khan, Mona A. Alkhattabi, Sheikh Muhammad Saqib, Tehseen Mazhar, Tariq Shahzad, Asim Seedahmed Ali Osman, Abdul Khader Jilani Saudagar, Habib Hamam

“…Robustness tests under perturbed conditions and DiCE-based counterfactual analysis highlight that approximately 68% of the Eligibility unknown cases can be reclassified under realistic feature adjustments.This framework supports regulatory transparency, informed policymaking, and integration with energy systems through improved EV classification, aiding adoption forecasting, load modeling, and vehicle-to-grid (V2G) planning.…”
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Research on Urban Rainfall Runoff Pollution Prediction Model Based on Feature Fusion by Junping Yao, Tianle Sun

“…The neural network algorithm is optimized and trained according to the sample data to obtain the sample features; the sample data are predicted according to the extracted sample features, and the prediction model is generated by using the feature fusion technology for two groups of prediction results to generate the prediction model and realize the water drop prediction. …”
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Transformer-Based Multi-Scale Feature Remote Sensing Image Classification Model by Ting Sun, Jun Li, Xiangrui Zhou, Zan Chen

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Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation by Luis Miguel Moreno Haro, Adaiton Oliveira-Filho, Bruno Agard, Antoine Tahan

“…This paper presents a novel approach for detecting sensor failures using image-based feature representation and the Convolutional Variational Autoencoder (CVAE) model. …”
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Photovoltaic output prediction based on VMD disturbance feature extraction and WaveNet by ShouSheng Zhao, Xiaofeng Yang, Kangyi Li, Xijuan Li, Weiwen Qi, Xingxing Huang

“…To address this, this paper proposes a PV output forecasting method based on Variational Mode Decomposition (VMD) disturbance feature extraction and the WaveNet model. …”
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MRI-Based Meningioma Firmness Classification Using an Adversarial Feature Learning Approach by Miada Murad, Ameur Touir, Mohamed Maher Ben Ismail

“…Moreover, the proposed pre-trained BiGAN encoder-based model outperformed relevant state-of-the-art methods in meningioma firmness classification. …”
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An effective brain stroke diagnosis strategy based on feature extraction and hybrid classifier by Maha Samir Elsayed, Gehad Ahmed Saleh, Ahmed I. Saleh, Abeer Twakol Khalil

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Feature Generation-Based Fingerprint Liveness Detection: A Novel Multimodal Approach by B. R. Rajakumar, S. Amala Shanthi

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Deep joint learning diagnosis of Alzheimer’s disease based on multimodal feature fusion by Jingru Wang, Shipeng Wen, Wenjie Liu, Xianglian Meng, Zhuqing Jiao

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Graph-Based Radiomics Feature Extraction From 2D Retina Images by Ofelio Jorreia, Nuno Goncalves, Rui Cortesao

“…Based on predicted bifurcation points and blood vessel segments, we use the Graph-Based Radiomics Feature Extraction Algorithm (Graph-BRFExtract) to extract the adjacency matrix. …”
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Soil Porosity Detection Method Based on Ultrasound and Multi-Scale Feature Extraction by Hang Xing, Zeyang Zhong, Wenhao Zhang, Yu Jiang, Xinyu Jiang, Xiuli Yang, Weizi Cai, Shuanglong Wu, Long Qi

“…Since the collected ultrasonic signals belong to long-time series data and there are different frequency and sequence features, this study constructs a multi-scale CNN-LSTM deep neural network model using large convolution kernels based on the idea of multi-scale feature extraction, which uses multiple large convolution kernels of different sizes to downsize the collected ultra-long time series data and extract local features in the sequences, and combining the ability of LSTM to capture global and long-term dependent features enhances the feature expression ability of the model. …”
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Deep Learned Feature Technique for Human Action Recognition in the Military using Neural Network Classifier by Adeola O Kolawole, Martins E Irhebhude, Philip O Odion

“…The dataset used was captured locally during military trainees’ obstacle-crossing exercises at a military training institution to achieve the objective. Images were segmented into background and foreground using a Grabcut-based segmentation algorithm. …”
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Nomogram construction for overall survival in breast angiosarcoma based on clinicopathological features: a population-based cohort study by Peikai Ding, Luxiao Zhang, Shengbin Pei, Zheng Qu, Xiangyi Kong, Zhongzhao Wang, Jing Wang, Yi Fang

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A range spread target detection algorithm based on polarimetric features and SVDD by Qiang LI, Yuanxin YAO, Xiangqi KONG

“…Multi-polarization range high resolution radar is an important mean for ground target detection.In the echo formed by it, the target occupies multiple range cells and becomes an extended target.The traditional spread target detection method relies on energy, and the detection performance decreases when the signal-to-clutter ratio decreases.A spread target detection algorithm based on polarization decomposition features was proposed, which improved the detection performance under low signal-to-clutter ratio by using the difference of polarization scattering characteristics between target and clutter.Specifically, 16 kinds of polarization decomposition features were extracted to form feature vectors as detection statistics, and then support vector data description (SVDD) was used to obtain the detection threshold.When training the detection threshold, the polarization decomposition features of clutter were extracted as training data.In order to ensure the false alarm probability, two penalty parameters were introduced into the objective function of SVDD.The experimental results show that the proposed method requires a signal-to-clutter ratio of about 12.6 dB in the case of Gobi background, false alarm probability of 10^-4 and detection probability of 90%, which is about 1.7 dB lower than the energy-based methods.…”
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Early Prediction of Epilepsy after Encephalitis in Childhood Based on EEG and Clinical Features by Xiaojuan Sun, Jinhua Zhao, Chunyun Guo, Xiaoxiao Zhu

“…The present study was designed to establish and evaluate an early prediction model of epilepsy after encephalitis in childhood based on electroencephalogram (ECG) and clinical features. …”
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