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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Efficacy of a Metal Artifact Reduction Algorithm in CBCT Images of Teeth With Ceramic Brackets With/Without Coated Archwires: An In Vitro Study by Parisa Soltani, Mariangela Cernera, Marzie Kachuie, Amirhossein Moaddabi, Mehran Khoramian, Gianrico Spagnuolo, Niccolò Giuseppe Armogida, Carlo Rengo

“…ABSTRACT Objective This study aimed to assess the efficacy of a metal artifact reduction (MAR) algorithm for cone‐beam computed tomography (CBCT) scans of teeth with ceramic brackets with/without coated archwires. …”
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n-FFT COMPRESSED SENSING ALGORITHM OF SMART DIMENSIONALITY REDUCTION METHOD AND ITS APPLICATION IN FEATURE EXTRACTION AND CLASSIFICATION OF GEAR SYSTEM by CHEN Xiao, HUANG ChuanJin

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Two-Dimensional DOA Estimation for Coprime Planar Arrays: From Array Structure Design to Dimensionality-Reduction Root MUSIC Algorithm by Yunhe Shi, Xiaofei Zhang, Shengxinlai Han

“…The virtualization of the array further increases the available DOFs, while the hole-filling strategy ensures better spatial coverage and continuity. On the algorithmic side, we introduce a dimensionality-reduction root MUSIC algorithm tailored for uniform planar arrays after virtualization. …”
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Metal artifact reduction combined with deep learning image reconstruction algorithm for CT image quality optimization: a phantom study by Huachun Zou, Zonghuo Wang, Mengya Guo, Kun Peng, Jian Zhou, Lili Zhou, Bing Fan

“…Background Aiming to evaluate the effects of the smart metal artifact reduction (MAR) algorithm and combinations of various scanning parameters, including radiation dose levels, tube voltage, and reconstruction algorithms, on metal artifact reduction and overall image quality, to identify the optimal protocol for clinical application. …”
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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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