Research on Ship Heave Motion Compensation Control Under Complex Sea State Environment Based on Improved Reinforcement Learning by ZHANG Qin, ZHOU Jingyi, WANG Xingyue, HU Xiong

“…In these diversified test scenarios, the improved TD3 algorithm exhibits remarkable adaptability and stability. …”
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A novel general kernel-based non-negative matrix factorisation approach for face recognition by Wen-Sheng Chen, Xiya Ge, Binbin Pan

“…We assume that the mapped basis images fall within the cone spanned by the mapped training data, allowing us to use arbitrary kernel function in the algorithm. …”
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Prediction of clinical pregnancy after frozen embryo transfer based on ultrasound radiomics: an analysis based on the optimal periendometrial zone by Fangfang Xu, Ying Zhang, Qianqing Ma, Lili Hu, Yu Li, Chuanfen Gao, Peipei Guo, Xianyue Yang, Yi Zhou, Jie Zhang, Heng Wang, Chaoxue Zhang

“…Methods This prospective study had 422 female participants (training set: n = 358, external validation set: n = 64). …”
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Radiomic prediction for durable response to high‐dose methotrexate‐based chemotherapy in primary central nervous system lymphoma by Haoyi Li, Mingming Xiong, Ming Li, Caixia Sun, Dao Zheng, Leilei Yuan, Qian Chen, Song Lin, Zhenyu Liu, Xiaohui Ren

“…Multiple machine‐learning algorithms were utilized for feature selection and classification to build a radiomic signature. …”
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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

“…Nineteen radiomic features were extracted and filtered using Boruta and LASSO algorithms. Seven ML classifiers were evaluated using AUC‐ROC, decision curve analysis (DCA), and SHAP interpretability. …”
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Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy by Paolo Zaffino, Ciro Benito Raggio, Adrian Thummerer, Gabriel Guterres Marmitt, Johannes Albertus Langendijk, Anna Procopio, Carlo Cosentino, Joao Seco, Antje Christin Knopf, Stefan Both, Maria Francesca Spadea

“…The algorithm evaluation revealed an accurate HU prediction (a median absolute prediction deviation equal to 4 HU for CBCT-based synthetic CTs and 6 HU for MR-based synthetic CTs), with discrepancies that do not affect the clinical decisions made on the basis of the proposed estimation. …”
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Predicting p53 Status in IDH‐Mutant Gliomas Using MRI‐Based Radiomic Model by Jiamin Li, Zhihong Lan, Xiao Zhang, Xiaoyun Liang, Hanwei Chen, Xiangrong Yu

“…The predictive performance of the models was evaluated using receiver operating characteristic (ROC) curve analysis. …”
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Simple Yet Powerful: Machine Learning-Based IoT Intrusion System With Smart Preprocessing and Feature Generation Rivals Deep Learning by Kazim Kivanc Eren, Kerem Kucuk, Fatih Ozyurt, Omar H. Alhazmi

“…Here we propose a classical machine learning system, built around a Random Forest classifier paired with a novel feature extraction algorithm adapted from Explainable Boosted Linear Regression (EBLR). …”
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Development and validation of a prediction model for VTE risk in gastric and esophageal cancer patients by Xingyue Zheng, Liuyun Wu, Lian Li, Yin Wang, Qinan Yin, Lizhu Han, Xingwei Wu, Yuan Bian

“…Using nine supervised learning algorithms, 576 prediction models were developed based on 56 available variables. …”
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Preoperative prediction of pituitary neuroendocrine tumor invasion using multiparametric MRI radiomics by Qiuyuan Yang, Tengfei Ke, Jialei Wu, Yubo Wang, Jiageng Li, Yimin He, Jianxian Yang, Nan Xu, Bin Yang

“…Data were divided into training set and testing set according to different field strength equipment. …”
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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

“…Methods We included a total of 356 NSCLC patients at pN0-pN2 stage, divided into training (n = 207), internal test (n = 90), and independent test (n = 59) sets. …”
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Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study by Ting Peng, Rujia Miao, Hao Xiong, Yanhui Lin, Duzhen Fan, Jiayi Ren, Jiangang Wang, Yuan Li, Jianwen Chen

“…The dataset was split into 80% (13,128/16,411) training and 20% (32,83/16,411) testing. A total of 5 machine learning algorithms, namely random forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Machine, and CatBoost were used. …”
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Machine Learning Prognostic Model for Post-Radical Resection Hepatocellular Carcinoma in Hepatitis B Patients by Zhu D, Tulahong A, Abuduhelili A, Liu C, Aierken A, Lin Y, Jiang T, Lin R, Shao Y, Aji T

“…A prognostic model was developed using a machine learning algorithm and evaluated for predictive performance using the concordance index (C-index), calibration curve, decision curve analysis (DCA), and receiver operating characteristic (ROC) curves.Results: Key predictors for constructing the best model included body mass index (BMI), albumin (ALB) levels, surgical resection method (SRM), and the American Joint Committee on Cancer (AJCC) stage. …”
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Preoperative prediction of WHO/ISUP grade of ccRCC using intratumoral and peritumoral habitat imaging: multicenter study by Zhihui Chen, Hongqing Zhu, Hongmin Shu, Jianbo Zhang, Kangchen Gu, Wenjun Yao

“…Methods Data from two hospitals included 513 ccRCC patients, who were divided into training (70%), validation (30%), and an external validation set (testing) of 67 patients. …”
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A machine learning-based depression risk prediction model for healthy middle-aged and older adult people based on data from the China health and aging tracking study by Fang Xia, Jie Ren, Linlin Liu, Yanyin Cui, Yufang He

“…The dataset was randomly split into a training set (70%) and a testing set (30%), and hyperparameters were optimized in the training phase. …”
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Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employin... by Ming-Ya Luo, Yang Qu, Peng Zhang, Reziya Abuduxukuer, Li-Juan Wang, Li-Chong Yang, Zhi-Guo Li, Xiao-Dong Liu, Ce Han, Dan Li, Wei-Jia Wang, Dian-Ping Lv, Ming Liu, Jian Gao, Jing Xu, Yongfei Jiang, Hai-Nan Chen, Fu-Jin Li, Li-Ming Sun, Qi-Dong Sun, Yingbin Qi, Si-Yin Sun, Yu Zhang, Zhen-Ni Guo, Yi Yang

“…Least Absolute Shrinkage and Selection Operator regression was used for feature selection, and six ML algorithms were tested. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F 1-score, calibration curve, and decision curve analysis. …”
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Research on the development of an intelligent prediction model for blood pressure variability during hemodialysis by Zhijian Ren, Minqiao Zhang, Pingping Wang, Kanan Chen, Jing Wang, Lingping Wu, Yue Hong, Yihui Qu, Qun Luo, Kedan Cai

“…The definitions of IDH and IDHTN were clarified, and 10 machine learning algorithms were used to build the models. For model development, the dialysis data were randomly split into a training set (80%) and a testing set (20%). …”
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EMUs Electrical Connector Life Prediction Based on Accelerated Degradation Data by Jie MA, Lide WANG, Chuan YUE, Ping SHEN, Ji QIU

“…Using physical model of life prediction and data processing algorithm and accelerated degradation data modeling process based on degradation distribution, the reliability and life evaluation of EMUs electrical connectors were realized. …”
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Sample Weighting Methods for Compensating Class Imbalance in Elephant Flow Classification by Piotr Jurkiewicz, Robert Wojcik, Jerzy Domzal

“…This paper compares various sample weighting techniques to compensate this imbalance during model training. We evaluate recommended approaches, as well as propose novel methods based on roots, powers, and logarithms of flow size. …”
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A novel canopy water indicator for UAV imaging to monitor winter wheat water status by Meiyan Shu, Zhenghang Ge, Yang Li, Jibo Yue, Wei Guo, Yuanyuan Fu, Ping Dong, Hongbo Qiao, Xiaohe Gu

“…To develop robust estimation models, four machine learning algorithms were implemented across individual and combined growth stages, and their performance was validated using independent ground-measured datasets that were not used during the training process. …”
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