Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage by Lingxu Chen, Xiaochen Wang, Sihui Wang, Xuening Zhao, Ying Yan, Mengyuan Yuan, Shengjun Sun

“…Eight machine learning algorithms were applied to construct radiomics-only and radiomics-clinical fusion nomogram models. …”
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Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting by Sheng Li, Min Wang, Minghang Shi, Jiafeng Wang, Ran Cao

“…These components are integrated into cloud dynamic feature mining units, which concurrently extract spatiotemporal features to strengthen unified spatiotemporal modeling. …”
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Mechanism–Data Collaboration for Characterizing Sea Clutter Properties and Training Sample Selection by Wenhao Chen, Yong Zou, Zhengzhou Li, Shengrong Zhong, Haolin Gan, Aoran Li

“…Based on the characterized clutter properties, a hybrid feature selection strategy is further proposed to construct a diverse and compact training sample set by integrating global density distribution with local gradient variation. …”
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Data-driven non-intrusive reduced order modelling of selective laser melting additive manufacturing process using proper orthogonal decomposition and convolutional autoencoder by Shubham Chaudhry, Azzedine Abdedou, Azzeddine Soulaïmani

“…The findings highlight the potential of integrating reduced-order modeling techniques with machine learning algorithms to enhance the analysis of complex AM processes. …”
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Research on Grouting Dynamic Monitoring Based on Borehole–Tunnel Joint Resistivity Method by Cheng Wang, Lei Zhou, Liangjun Yan, Bofan Li

“…Finite volume method and quasi-Gauss–Newton inversion algorithms were employed to analyze dynamic resistivity variations, enhancing spatial resolution for detailed characterization of grout migration. …”
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YOLOv8n-Al-Dehazing: A Robust Multi-Functional Operation Terminals Detection for Large Crane in Metallurgical Complex Dust Environment by Yifeng Pan, Yonghong Long, Xin Li, Yejing Cai

“…The challenge of real-time multi-form object detection in high-resolution images affected by smoke and dust environments demands detection and dehazing algorithms. To address these issues, we propose the YOLOv8n-Al-Dehazing method, which achieves the precise detection of multi-functional material handling terminals in aluminum electrolysis workshops. …”
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Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review by Olga Adriana Caliman Sturdza, Florin Filip, Monica Terteliu Baitan, Mihai Dimian

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Machine learning based method for forecasting short-term passenger flow in urban rail stations by HU Mingwei, SHI Xiaolong, WU Wenlin, HE Guoqing

“…Based on passenger flow characteristics as collected, we build the short-term passenger flow forecast models, which include light gradient boosting machine (LightGBM) model, long short-term memory (LSTM) model, and LightGBM-LSTM fusion model. Besides, we construct the autoregressive integrated moving average (ARIMA) model and extreme gradient boosting (XGBoost) model for experiment comparison. …”
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Development and validation of a machine learning-based prediction model for hepatorenal syndrome in liver cirrhosis patients using MIMIC-IV and eICU databases by Fengwei Yao, Ji Luo, Qian Zhou, Luhua Wang, Zhijun He

“…The AUC in the external test set was 0.8212 (95% CI 0.7784–0.864). By integrating the MIMIC-IV database and machine learning algorithms, we developed an effective predictive model for HRS in liver cirrhosis patients, providing a robust tool for early clinical intervention.…”
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A Random Degradation Aggregation Network With Temporal-Spatial Attention for Satellite Video Super-Resolution by Lu Li, Mi Wang, Yingdong Pi

“…Moreover, current VSR algorithms are primarily designed for natural scene videos and fail to account for the unique characteristics of satellite videos, leading to suboptimal reconstruction performance when directly applied to them. …”
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Comprehensive analysis of phagocytosis regulatory genes in bladder cancer: implications for prognosis and immunotherapy by Xueming Ma, Xueming Ma, Xueming Ma, Dongnuan Yao, Dongnuan Yao, Dongnuan Yao, Weitao Yu, Weitao Yu, Weitao Yu, Gongping Wu, Gongping Wu, Gongping Wu, Chengwei Fan, Chengwei Fan, Chengwei Fan, Junqiang Tian, Junqiang Tian, Junqiang Tian

“…A prognostic model was constructed and verified, and the risk score was calculated. …”
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A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space by Zheng Luo, Jiahao Mai, Caihong Feng, Deyao Kong, Jingyu Liu, Yunhong Ding, Bo Qi, Zhanbo Zhu

“…Nevertheless, machine learning algorithms possess significant advantages in handling data complexity and nonlinearity. …”
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Supervised Machine Learning Models for Predicting SS304H Welding Properties Using TIG, Autogenous TIG, and A-TIG by Subhodwip Saha, Barun Haldar, Hillol Joardar, Santanu Das, Subrata Mondal, Srinivas Tadepalli

“…The machine learning (ML) models were constructed based on the data gathered from 50 experimental runs, considering eight key input variables: gas flow rate, torch angle, filler material, welding pass, flux application, root gap, arc gap, and heat input. …”
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Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis by Chengyuan Xu, Chengyuan Xu, Siqi Zhang, Bin Sun, Zicheng Yu, Hailong Liu

“…However, the metabolic and prognostic regulators governing this process remain largely undefined.MethodsWe constructed a macrophage polarization gene signature (MPGS) by integrating weighted gene co-expression network analysis (WGCNA) with multiple machine learning algorithms across two independent cohorts: 363 rectal cancer samples from GSE87211 and 177 samples from The Cancer Genome Atlas (TCGA). …”
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Hyperspectral Imaging and Machine Learning for Diagnosing Rice Bacterial Blight Symptoms Caused by <i>Xanthomonas oryzae</i> pv. <i>oryzae</i>, <i>Pantoea ananatis</i> and <i>Enter... by Meng Zhang, Shuqi Tang, Chenjie Lin, Zichao Lin, Liping Zhang, Wei Dong, Nan Zhong

“…One-dimensional convolutional neural networks (1DCNNs) were employed to construct a classification model, integrating various spectral preprocessing techniques and feature selection algorithms for comparison. …”
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Computed tomography-based radiomics model for predicting station 4 lymph node metastasis in non-small cell lung cancer by Yanru Kang, Mei Li, Xizi Xing, Kaixuan Qian, Hongxia Liu, Yafei Qi, Yanguo Liu, Yi Cui, Hua Zhang

“…Four machine learning algorithms—decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM)—were employed to construct radiomics models. …”
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Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study by Mingwei Zhang, Ming Zhong, Yunzhang Cheng, Tianyi Zhang

“…Three linear parameters (mean, SD, and endpoint value) were calculated to construct the prediction model using multiple ML algorithms. …”
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Identification and evaluation of metabolic mRNAs and key miRNAs in colorectal cancer liver metastasis by Guanxuan Chen, Shiwen Wang, Meng Zhang, Wenna Shi, Ruoyu Wang, Wanqi Zhu

“…Subsequently, we employed Weighted Gene Co-expression Network Analysis (WGCNA) to construct an integrated co-expression network of mRNAs and miRNAs, facilitating the identification of pivotal mRNAs and miRNAs. …”
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Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth by Rui Zhou, Ziqian Liu, Tongtong Wu, Xianwei Pan, Tongtong Li, Kaiting Miao, Yuru Li, Xiaohui Hu, Haigang Wu, Andrew M. Hemmings, Beier Jiang, Zhenzhen Zhang, Ning Liu

“…Methods An integrated machine learning approach, integrated by Bayesian inference, was employed to screen a commercial dataset of 70,413 molecules, identifying candidates that selectively and efficiently bind with EGFR harboring T790M mutation. …”
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Unveiling tumor-infiltrating immune cell-driven immune-mediated drug resistance in clear cell renal cell carcinoma: prognostic insights and therapeutic strategies by Jifeng Yang, Yixuan Xing, Jiusong Luan, Wenbo Yang, Xin Zhang, Yanhua Tian, Haisong Zhang

“…The constructed prognostic model was validated across independent datasets. …”
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