Early Identification of Pediatric Inflammatory Bowel Disease Based on a Noninvasive Multivariable Predictive Model by Wu H, Sun Y, Tang Z, Qin X, Wang Y, Huang Y

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Predictive modeling of the development height of water-conducting fracture zones in mines in Shandong mining area by XU Dongjing, DOU Xuan, LI Ye, XIA Zhicun

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Predictive Model of Granular Fertilizer Spreading Deposition Distribution Based on GA-GRNN Neural Network by Lilian Liu, Guobin Wang, Yubin Lan, Xinyu Xue, Suming Ding, Huizheng Wang, Cancan Song

“…The results show that the prediction accuracy and training effect of the GA-GRNN model are better than those of the GRNN, with a coefficient of determination of 0.839, and that the results of the GA-GRNN model are closer to the actual data when predicting the effective amplitude of the deposition amount, which is more accurate. …”
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The role of the SOX2 gene in cervical cancer: focus on ferroptosis and construction of a predictive model by Shenping Liu, Zhi Wei, Huiqing Ding

“…Objective To delineate the association between SOX2 expression and ferroptosis in cervical cancer and develop a robust, SOX2-centric model for predicting prognosis and enhancing personalized treatment. …”
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Recycling waste materials in construction: Mechanical properties and predictive modeling of Waste-Derived cement substitutes by Moutaman M. Abbas

“…In addition to experimental findings, a neural network model was developed to predict the compressive strength of concrete containing these materials, trained on data collected from the literature. …”
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Hybrid A*-Guided Model Predictive Path Integral Control for Robust Navigation in Rough Terrains by Joonyeol Yang , Minhyeong Kang , Seulchan Lee, Sanghyun Kim

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Predictive modeling and optimization of SI engine performance and emissions with GEM blends using ANN and RSM by Farooq Shaik, D. Vinay Kumar, N. Channa Keshava Naik, G. Radha Krishna, T. M. Yunus Khan, Abdul Saddique Shaik, Abdulrajak Buradi, Addisu Frinjo Emma

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A predictive model for body water and fluid balance using 3D smartphone anthropometry by Austin J. Graybeal, Abby T. Compton, Sydney H. Swafford, Caleb F. Brandner, Molly F. Johnson, Maria G. Kaylor, Hunter Haynes, Jon Stavres

“…Then, LASSO regression was used to develop new TBW and ECF prediction model in a subset of participants (n = 272), which was subsequently tested in the remaining participants (n = 66). …”
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Predictive modeling of ADME properties using M-polynomial based topological indices for biocompatible polysaccharides by W. Eltayeb Ahmed, Muhammad Naeem, Muhammad Kamran Siddiqui, Mohamed Abubakar Fiidow

“…Several models demonstrated excellent predictive strength (e.g., $$R^2 > 0.95$$ ) with statistical significance ( $$p < 0.001$$ ), confirmed through both cross-validation and external validation. …”
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Advanced predictive disease modeling in biomedical IoT using the temporal adaptive neural evolutionary algorithm by Chandragandhi S, Arvind C, Srihari K

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ColdstartCPI: Induced-fit theory-guided DTI predictive model with improved generalization performance by Qichang Zhao, Haochen Zhao, Linyuan Guo, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

“…Abstract Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. …”
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Predictive value of machine learning model based on CT values for urinary tract infection stones by Jiaxin Li, Yao Du, Gaoming Huang, Chiyu Zhang, Zhenfeng Ye, Jinghui Zhong, Xiaoqing Xi, Yawei Huang

“…Taken together, the XGBoost model is the first machine learning model for preoperative prediction of infection stones based on CT values. …”
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Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods by Shiyang Cheng, Qihang Zhang, Hao Min, Wenhui Jiang, Jueting Liu, Chunsheng Liu, Zehua Wang

“…Among the predictive models, the extreme gradient boosting shows the highest prediction accuracy of 81.0%. …”
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An accurate trajectory tracking method for low-speed unmanned vehicles based on model predictive control by Lifen Wang, Sizhong Chen, Hongbin Ren

“…Abstract Trajectory tracking on a low-speed vehicle using the model predictive control (MPC) algorithm usually assumes a simple road terrain. …”
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Fidan: a predictive service demand model for assisting nursing home health-care robots by Feng Zhou, Xin Du, WenLi Li, Zhihui Lu, Shih-Chia Huang

“…The experimental results show that the Fidan model has an accuracy rate of 86.61% in predicting the demand for elderly services.…”
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Predictive modeling of pediatric drug-induced liver injury: Dynamic classifier selection with clustering analysis by Zixin Shi, Linjun Huang, Haolin Wang

“…Results The Clustering-enhanced DCS-MCB framework demonstrated superior performance compared to conventional machine learning models across evaluation metrics. The ensemble learning models consistently outperformed individual classifier models, with the presented study achieving the highest F1-score (0.926), MCC (0.917), G-mean (0.959), demonstrating the strength of this hybrid approach in addressing the complexities of pediatric DILI prediction. …”
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Robust Constrained Model Predictive Control for T-S Fuzzy Uncertain System with Data Loss and Data Quantization by Hongchun Qu, Yu Li, Wei Liu

“…This paper addresses the robust constrained model predictive control (MPC) for Takagi-Sugeno (T-S) fuzzy uncertain quantized system with random data loss. …”
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The future of healthy brain aging will be metabolic: a predictive lesson from data-driven modeling by Luc Pellerin

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Erratum: Small subcortical ischemic infarction and other DWI lesions establish predictive model for MES by Frontiers Production Office

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Predictive modeling of adolescent suicidal behavior using machine learning: Key features and algorithmic insights by Priya Metri, Swetta Kukreja

“…This review highlights existing gaps in cross-cultural generalization and calls for the development of interpretable and hybrid models for improved risk prediction.This review aims to conduct a comprehensive examination of the etiological factors contributing to the development of suicidal thoughts in students, with the goal of enabling early detection through the application of AI and machine learning techniques.This paper aims to review the current state-of-the-art, highlight the limitations, and emphasizes the need to shift toward hybrid and ensemble deep learning models, which have shown early promise but lack extensive analysis in current literature.…”
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