A risk stratification model based on ultrasound radiologic features for cervical metastatic lymph nodes in papillary thyroid cancer by Hai-Long Tan, Sai-Li Duan, Qiao He, Zhe-Jia Zhang, Peng Huang, Shi Chang

“…Multivariate analysis indicated that the absence of fatty hilum, cystic components, round shape (SD/LD ≥ 0.5), abundant vascularity, hyperechogenicity (including hyper and hypo-echogenicity, and hyper-echogenicity), and calcifications (include microcalcification, and macrocalcification) were independent risk US features associated with malignant LNs. A risk stratification model for cervical metastatic LNs was developed based on these suspicious US features and showed well-predicted performance (C-index 0.840; 95% CI: 0.840–0.923). …”
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Power Wavelet Cepstral Coefficients (PWCC): An Accurate Auditory Model-Based Feature Extraction Method for Robust Speaker Recognition by Youssef Zouhir, Mohamed Zarka, Kais Ouni, Lilia El Amraoui

“…To bridge this gap, researchers investigate the human auditory system to support machine learning algorithm performance. The paper introduces a novel feature extraction method, named “Power Wavelet Cepstral Coefficients” (PWCC), for enhancing SR accuracy. …”
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Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground‐Glass Nodules Based on AI‐Extracted Radiomic Features by Guozhen Yang, Yuanheng Huang, Huiguo Chen, Weibin Wu, Yonghui Wu, Kai Zhang, Xiaojun Li, Jiannan Xu, Jian Zhang

“…Conclusion This study presents a simplified ML model using AI‐extracted radiomic features, which has strong predictive performance and biological interpretability for preoperative risk stratification of GGNs. …”
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3M: Multi-style image caption generation using Multi-modality features under Multi-UPDOWN model by Chengxi Li, Brent Harrison

“…In this paper, we build a multi-style generative model for stylish image captioning which uses multi-modality image features, ResNeXt features, and text features generated by DenseCap. …”
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Feature Fusion Based Audio-Visual Speaker Identification Using Hidden Markov Model under Different Lighting Variations by Md. Rabiul Islam, Md. Abdus Sobhan

“…Among the different fusion strategies, feature level fusion has been used for the proposed AVSI system where Hidden Markov Model (HMM) is used for learning and classification. …”
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Construction of a prediction model for peripheral lymph node metastasis in patients with colorectal cancer based on enhanced CT texture features by Feng Tong, Longfei Zhang, Xiaobin Jiang, Zhenyu Wu

“…Abstract Background To investigate the analysis of peripheral lymph node metastasis prediction model construction for patients with colorectal cancer based on enhanced CT texture features. …”
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Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack. by Prashant Kumar, Chitra Kushwaha, Dimple Sethi, Debjani Ghosh, Punit Gupta, Ankit Vidyarthi

“…Here, a benchmark dataset named CICDDoS2019 is used that contains 88 features from which a handful (22) convenient features are extracted further deep learning models are applied. …”
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End-to-End Information Extraction from Courier Order Images Using a Neural Network Model with Feature Enhancement by Wei Shen, Han Li, Youbo Jin, Chase Q. Wu

“…Comparative experiments were also performed with other models on publicly available datasets CORD and SROIE. …”
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Deciphering Customer Satisfaction: A Machine Learning-Oriented Method Using Agglomerative Clustering for Predictive Modeling and Feature Selection by Rezki Nisrine, Mansouri Mohamed, Oucheikh Rachid

“…Performance metrics such as accuracy, recall, precision and F1-score are employed for model evaluation, ensuring robustness and reliability in the predictive process. …”
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SFSIN: A Lightweight Model for Remote Sensing Image Super-Resolution with Strip-like Feature Superpixel Interaction Network by Yanxia Lyu, Yuhang Liu, Qianqian Zhao, Ziwen Hao, Xin Song

“…In addition to traditional methods that rely solely on direct upsampling for reconstruction, our model uses the convolutional block attention module with upsampling convolution (CBAMUpConv), which integrates deep features from spatial and channel dimensions to improve reconstruction performance. …”
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Classification Model of Clock Drawing Test Based on Contrastive Learning Using Multi-Channel Features With Channel-Spatial Attention by Changsu Kang, Bohyun Wang, J. S. Lim

“…As far as our knowledge extends, this study represents the first instance of utilizing supervised contrastive learning, acquiring features from multiple channels, for classifying CDT images, and we achieved superior performance compared to other models. …”
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A Comprehensive Method for Calculating Maritime Radar Identification Probability Using 3D Marine Geographical Feature Models by Hao Meng, Li-Hua Zhang, Hai Hu, Shi-Jun Rao, Bao-Hui Gao

“…To overcome the limitations of existing maritime radar identification analysis methods, which are only applicable to sea-skimming aircraft and fail to quantitatively calculate the probability of radar correctly identifying the target under electromagnetic influence from marine geographical features (MGFs), an advanced method is proposed for calculating the radar identification probability in marine areas using 3D MGF models. …”
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Just-in-time updated DBN BOF steel-making soft sensor model based on dense connectivity of key features by Lu Zongxu, Liu Hui, Chen FuGang, Li Heng, Xue XiaoJun

“…In order to enable deep learning to cope with these problems and maintain good prediction performance, this chapter proposes a Deep Belief Network (DBN) feature extraction model based on dense connectivity of key features. …”
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Boosting Urban Openspace Mapping with the Enhancement Feature Fusion of Object Geometry Prior Information from Vision Foundation Model by Zijian Xu, Jiajun Chen, Hongyang Niu, Runyu Fan, Dingkun Lu, Ruyi Feng

“…However, the challenges of high inter-class similarity, complex environmental surroundings, and scale variations often result in suboptimal performance in UO mapping. To address these issues, this paper proposes UOSAM, a novel approach that leverages the Segment Anything Model (SAM) for efficient UO mapping using high-resolution remote sensing images. …”
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Research on computer multi feature fusion SVM model based on remote sensing image recognition and low energy system by Yangming Wu, Hao Wu, Xin Tang, Jianwei Lv, Rufei Zhang

“…Therefore, this paper aims to explore a low-energy multi-feature fusion support vector machine (SVM) model based on remote sensing image recognition. …”
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An enhanced BERT model with improved local feature extraction and long-range dependency capture in promoter prediction for hearing loss by Jing Sun, Yangfan Huang, Jiale Fu, Li Teng, Xiao Liu, Xiaohua Luo

“…The CNN module is able to capture local regulatory features, while the BiLSTM module can effectively model long-distance dependencies, enabling efficient integration of global and local features of promoter sequences. …”
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DuSAFNet: A Multi-Path Feature Fusion and Spectral–Temporal Attention-Based Model for Bird Audio Classification by Zhengyang Lu, Huan Li, Min Liu, Yibin Lin, Yao Qin, Xuanyu Wu, Nanbo Xu, Haibo Pu

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Diagnosis of non-puerperal mastitis based on “whole tongue” features: non-invasive biomarker mining and diagnostic model construction by Siyuan Tu, Yulian Yin, Lina Ma, Hongfeng Chen, Meina Ye

“…Based on clinical, imaging, and microbial features, three machine learning models—logistic regression (LR), support vector machine (SVM), and gradient boosting decision tree (GBDT)—were trained to distinguish NPM.ResultsThe GBDT model achieved a superior diagnostic performance (AUROC = 0.98, accuracy = 0.95, and specificity = 0.95), outperforming the LR (AUROC = 0.98, accuracy = 0.95, and specificity = 0.90) and SVM models (AUROC = 0.87, accuracy = 0.80, and specificity = 0.75). …”
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Learning behavior aware features across spaces for improved 3D human motion prediction by Ruiya Ji, Chengjie Lu, Zhao Huang, Jianqi Zhong

“…However, most existing works model human motion dependencies exclusively in Euclidean space, neglecting the human motion representation in Euclidean space leads to distortions and loss of information when representation dimensions increase. …”
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Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient by Yanxiang Niu, Xin Chen, Jianqi Fan, Chunli Liu, Menghao Fang, Ziquan Liu, Xiangyan Meng, Yanqing Liu, Lu Lu, Haojun Fan

“…After rigorous preprocessing and feature engineering, machine learning models (Logistic Regression, SVM, Random Forest, and Gradient Boosting) were developed. …”
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