A Pilot Study: Sleep and Activity Monitoring of Newborn Infants by GRU-Stack-Based Model Using Video Actigraphy and Pulse Rate Variability Features by Ádám Nagy, Zita Lilla Róka, Imre Jánoki, Máté Siket, Péter Földesy, Judit Varga, Miklós Szabó, Ákos Zarándy

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ReScConv-xLSTM: An improved xLSTM model with spatiotemporal feature extraction capability for remaining useful life prediction of Aero-engine by Mingxing Huang, Lanying Yang, Gang Jiang, Xingan Hao, Hong Lu, Hang Luo, Peng Wang, Jinyang Li

“…Although deep learning models based on LSTM and Transformer have achieved significant results in this field, these models typically only extract temporal features, neglecting spatial features, and struggle with parallel computation, leading to a bottleneck in RUL prediction performance. …”
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Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES by Ningyi Cheng, Yukun Chen, Lei Jin, Liangwan Chen

“…Ensemble models were compared for HF classification, with SHAP quantifying feature importance. …”
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A Novel SOH Estimation Method for Lithium-Ion Batteries Based on the PSO–GWO–LSSVM Prediction Model with Multi-Dimensional Health Features Extraction by Xu He, Zhengpu Wu, Jinghan Bai, Junchao Zhu, Lu Lv, Lujun Wang

“…However, the use of individual health features (HFs) and the selection of hyperparameters can increase the data processing burden on the BMS and reduce the accuracy of data-driven models. …”
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MambaMeshSeg-Net: A Large-Scale Urban Mesh Semantic Segmentation Method Using a State Space Model with a Hybrid Scanning Strategy by Wenjie Zi, Hao Chen, Jun Li, Jiangjiang Wu

“…Moreover, our model exhibits faster performance in both inference and pre-processing compared to other mainstream models. …”
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Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification by P. Jeneessha, Vinoth Kumar Balasubramanian

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Prediction model for assessing HER2 status patient with invasive ductal carcinoma based on clinical parameters and ultrasound features: a dual-center study by Lei Zhou, Yingnan Wu, Xin Wen, Xu Guo, Lei Zhang, Tianzhuo Zhao, Weijian Song, Yue Xin, Zehui Su, Litao Sun, Jiawei Tian

“…This study aims to develop a nomogram model that incorporates multimodal ultrasound imaging features alongside clinicopathological characteristics to evaluate HER2 status. …”
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TableStructureFormer: an improved masked-attention mask transformer model with long-distance feature aggregation and deep detail supervision for table structure recognition by Chenglong Yu, Weibin Li, Zixuan Zhu, Wei Li, Jianchao Du, Shiwei Zhang

“…In addition, to address the difficulty of perfectly segmenting the details of rows and columns, we propose the deep detail supervision module to guide the segmentation model to learn the detailed feature maps about objects, thereby further correcting their masks. …”
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Construction of a nomogram prediction model for the pathological complete response after neoadjuvant chemotherapy in breast cancer: a study based on ultrasound and clinicopathologi... by Pingjuan Ni, Yuan Li, Yu Wang, Xiuliang Wei, Wenhui Liu, Mei Wu, Lulu Zhang, Feixue Zhang

“…The ultrasound and clinicopathological features of the training set were compared, and a nomogram prediction model was constructed based on these features. …”
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Stock price prediction based on dual important indicators using ARIMAX: A case study in Vietnam by Wang Pai-Chou, Vo Tram Thi Hoai

“…A dual important features selection approach is proposed to extract key features for the ARIMAX model from a pool of 87 technical indicators. …”
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A dual branch feature extraction network for heart sound signal analysis by Hao Chen, Wenye Gu

“…Furthermore, a squeeze-and-excitation module is integrated into the conventional audio branch to adaptively emphasize key feature channels, which enhances overall model performance. …”
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UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides by Zixin Chen, Chengming Ji, Wenwen Xu, Jianfeng Gao, Ji Huang, Huanliang Xu, Guoliang Qian, Junxian Huang

“…Evaluation results demonstrate superior performance of our proposed model on both balanced benchmark datasets and imbalanced test datasets compared with existing studies. …”
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Lightweight Small Target Detection Algorithm Based on YOLOv8 Network Improvement by Xiaoyi Hao, Ting Li

“…The modules have been designed to optimise feature extraction and improve model efficiency. The paper also discusses the challenges associated with low accuracy in small target detection and high model complexity in UAV applications. …”
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A dissimilarity-adaptive cross-validation method for evaluating geospatial machine learning predictions with clustered samples by Yanwen Wang, Mahdi Khodadadzadeh, Raúl Zurita-Milla

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Improving the prediction of streamflow in large watersheds based on seasonal trend decomposition and vectorized deep learning models by Ningchang Kang, Zhaocai Wang, Anbin Zhang, Hang Chen

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Research on Defect Detection for Overhead Transmission Lines Based on the ABG-YOLOv8n Model by Yang Yu, Hongfang Lv, Wei Chen, Yi Wang

“…Additionally, the ABG-YOLOv8n model demonstrates superior detection performance compared to other enhanced YOLO models.…”
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Visualization of Learning Process in Feature Space by Tomohiro Inoue, Noboru Murata, Taiki Sugiura

“…In machine learning, the structure of feature space is an important factor that determines the performance of a model. …”
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Impact of imbalanced features on large datasets by Waleed Albattah, Rehan Ullah Khan

“…Ideally, each class should have an equal number of instances and features to ensure optimal classifier performance. …”
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Prediction model for spontaneous combustion temperature of coal based on PSO-XGBoost algorithm by Hui Zhuo, Tongren Li, Wei Lu, Qingsong Zhang, Lingyun Ji, Jinliang Li

“…The chosen indicator data were divided into training and testing sets in a 4:1 ratio, the Particle Swarm Optimization (PSO) methodology was applied to optimize the parameters of the XGBoost regressor, and a universal PSO-XGBoost prediction model is proposed. A tenfold cross-validation method was employed to assess performance of PSO-XGBoost, PSO-RF, PSO-SVR, XGBoost, RF, and SVR models separately, the results underscored the superior predictive accuracy, robustness, fault tolerance, and universality of the PSO-XGBoost model.…”
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Construction and validation of a risk stratification model based on Lung-RADS® v2022 and CT features for predicting the invasive pure ground-glass pulmonary nodules in China by Qingcheng Meng, Tong Liu, Hui Peng, Pengrui Gao, Wenda Chen, Mengjia Fang, Wentao Liu, Hong Ge, Renzhi Zhang, Xuejun Chen

“…Abstract Objectives A novel risk stratification model based on Lung-RADS® v2022 and CT features was constructed and validated for predicting invasive pure ground-glass nodules (pGGNs) in China. …”
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