Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control by Rongjun Cheng, Haoli Lou, Qi Wei

“…The connected and automated vehicles (CAV) smoothing mixed traffic flow has gained attention, and a thorough assessment of these control algorithms is necessary. Our previous research proposed the time-varying model predictive control (TV-MPC) strategy, which considers the time-varying driving style of human driven vehicles (HDV), performing better than current baseline models. …”
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Risk factors and predictive model construction for lower extremity arterial disease in diabetic patients. by Yingjie Kuang, Zhixin Cheng, Jun Zhang, Chunxu Yang, Yue Zhang

“…Logistic regression analysis was employed to identify related factors, and machine learning algorithms were used to construct the risk prediction model.…”
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Optimizing maize germination forecasts with random forest and data fusion techniques by Lili Wu, Yuqing Xing, Kaiwen Yang, Wenqiang Li, Guangyue Ren, Debang Zhang, Huiping Fan

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A Multi-granularity Heterogeneous Ensemble Model for Point and Interval Forecasting of Carbon Prices by Di Sha, Xianyi Zeng, Kim-Phuc Tran, Lin Xia, Ruolin Wang

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A Discrete Grey Seasonal Model with Fractional Order Accumulation and Its Application in Forecasting the Groundwater Depth by Kai Zhang, Lifeng Wu, Kedong Yin, Wendong Yang, Chong Huang

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SELECTION OF RECIPIENTS FOR HEART TRANSPLANTATION BASED ON URGENCY STATUS by O. A. Sujayeva, M. I. Davidovitch, S. G. Sudzhaeva, Y. P. Оstrovsky

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Research on Tracking Control of Unmanned Mine Trucks Based on Adaptive Preview by HUANG Yaoran, LIU Zhicong, KANG Yuanrong

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Applying PageRank to Team Ranking in Single-Elimination Tournaments: Evidence from Taiwan’s High School Baseball by Yu-Chia Hsu, Wen-Jie Zhang

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An extension of the Spiegelhalter-Knill-Jones method for continuous covariates in clinical decision making by Bart K. M. Jacobs, Tafadzwa Maseko, Lutgarde Lynen, Aquiles Rodrigo Henriquez-Trujillo, Jozefien Buyze

Subjects: “…Prediction…”
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Development and validation of a nomogram to predict bacterial blood stream infection by Yu Huan Jiang, Rui Zhao, Yun Xue Bai, Hui Ming Li, Jun Liu, Shi Xuan Wang, Xing Xie, Yang Liu, Qiang Chen

“…The calibration curve indicated satisfactory calibration ability of the predictive model. Decision curve analysis revealed that the nomogram model had good clinical utility in predicting bacterial BSI. …”
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AI optimization algorithms enhance higher education management and personalized teaching through empirical analysis by XiWen Xu

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Retracted: International Chinese Education Expert System Based on Artificial Intelligence and Machine Learning Algorithms by Journal of Mathematics

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Construction of disability risk prediction model for the elderly based on machine learning by Jing Chen, Yifei Ren, Jie Ding, Qingqing Hu, Jiajia Xu, Jun Luo, Zhaowen Wu, Ting Chu

“…Abstract The study aimed to develop a predictive model using machine learning algorithms, providing healthcare professionals with a novel tool for assessing disability risk in older adults. …”
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Coronary Heart Disease Risk Prediction Model Based on Machine Learning by YUE Haitao, HE Chanchan, CHENG Yuyou, ZHANG Sencheng, WU You, MA Jing

“…However, the issue of data imbalance in these studies is often overlooked, despite its crucial role in enhancing the accuracy of CHD risk identification within classification algorithms. Objective To investigate the factors influencing CHD and to establish predictive models for CHD risk using two data balancing methods based on five algorithms, comparing the predictive value of these models for CHD risk. …”
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Constructing a predictive model for acute mastitis in lactating women based on machine learning by Liujing Zhu, Zuyan Huang, Yan Chen, Guangqiu Li, Liwen Liu

“…By using machine learning (ML) algorithms (Logistic Regression (LR), Naive Bayes (NB), XGBoost, Multilayer Perceptron (MLP)) to train and validate the above data, it aimed to construct a predictive model of the risk factors for the occurrence of acute mastitis in lactating women, and simultaneously analyzed the other influences and effects of these factors on acute mastitis. …”
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Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods by Sebastian-Camilo Vanegas-Ayala, Julio Barón-Velandia, Daniel-David Leal-Lara

“…The implementation of a Mamdani-type fuzzy inference system, optimized by a hybrid method combining genetic and interior point algorithms, allowed to predict the relative humidity in greenhouses with high interpretability and precision, with an effectiveness percentage of 90.97% and MSE (mean square error) of 8.2e − 3.…”
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Machine Learning Models for Predicting Thermal Properties of Radiative Cooling Aerogels by Chengce Yuan, Yimin Shi, Zhichen Ba, Daxin Liang, Jing Wang, Xiaorui Liu, Yabei Xu, Junreng Liu, Hongbo Xu

“…The model integrated multiple parameters, including the material composition (matrix material type and proportions), modification design (modifier type and content), optical properties (solar reflectance and infrared emissivity), and environmental factors (solar irradiance and ambient temperature) to achieve accurate cooling performance predictions. A comparative analysis of various machine learning algorithms revealed that an optimized XGBoost model demonstrated superior predictive performance, achieving an R² value of 0.943 and an RMSE of 1.423 for the test dataset. …”
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Development and validation of web-based, interpretable predictive models for sepsis and mortality in extensive burns by Shi-Qi Wang, Shi-Qi Wang, Kan Qiu, Qi-Rui Zheng, Bing-Jie Zhou, Ming-Yu Li, Hai-Yan Zhong, Yong Chen, Si-Ming Yuan, Si-Ming Yuan

“…We applied ten machine learning algorithms, including random forest, gradient boosting tree (GBT), and logistic regression, to predict sepsis and mortality. …”
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From predictive analytics to emotional recognition–The evolving landscape of cognitive computing in animal welfare by Suresh Neethirajan

“…The focus is on the strategic application of digital imaging and artificial intelligence to discern subtle behavioral patterns and micro-expressions in livestock, offering a predictive window into their emotional states. The significance of acoustic vocalization analysis in interpreting complex communicative signals and emotional subtleties is highlighted. …”
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Review on Sound-Based Industrial Predictive Maintenance: From Feature Engineering to Deep Learning by Tongzhou Ye, Tianhao Peng, Lidong Yang

“…Sound-based predictive maintenance (PdM) is a critical enabler for ensuring operational continuity and productivity in industrial systems. …”
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