A New Mathematical Model for Predicting the Surface Vibration Velocity on the Step Topography by Xu Wu, Qifeng Guo, Yunpeng Zhang

“…The regression analysis results show that the fitting coefficient of determination of the new prediction model is 0.8152 in horizontal and 0.8902 in vertical, respectively, and the prediction error is less than 20%, which is much better than other formulas. …”
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Model for predicting metabolic activity in athletes based on biochemical blood test analysis by Victoria A. Zaborova, Evgenii I. Balakin, Ksenia A. Yurku, Olga E. Aprishko, Vasiliy I. Pustovoyt

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Machine Learning Models for Predicting Mortality in Hemodialysis Patients: A Systematic Review by Alexandru Catalin Motofelea, Adelina Mihaescu, Nicu Olariu, Luciana Marc, Lazar Chisavu, Gheorghe Nicusor Pop, Andreea Crintea, Ana Maria Cristina Jura, Viviana Mihaela Ivan, Adrian Apostol, Adalbert Schiller

“…This systematic review and meta-analysis aimed to evaluate and compare the performance of various machine learning (ML) models in predicting mortality among HD patients. …”
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Predicting EFL Learners’ Self-Regulated Learning through Technology Acceptance Model by Yusup Supriyono, Francisca Maria Ivone, Dedi Heryadi, Lorna Beduya, Luis Luigi Eugenio A. Valencia

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Construction of a Column Chart Model for Predicting TCRP Recurrence in Gravid Women by Xuqing Chen, Jing Li, Hui Liang, Nanxiang Lei

“…Conclusion: The nomogram model constructed in this study is conducive to predicting the recurrence of women of childbearing age after TCRP, and may be helpful for preventing and treating polyp recurrence.…”
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Predicting Dilution in Underground Mines with Stacking Artificial Intelligence Models and Genetic Algorithms by Jorge L. V. Mariz, Tertius S. G. Ferraz, Marinésio P. Lima, Ricardo M. A. Silva, Hyongdoo Jang

“…Furthermore, four stacking models were constructed by aggregating the top-performing base learners, giving rise to ensemble metamodels applied, for the first time, to the task of dilution prediction in underground mining.…”
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Transport models for predicting spontaneous ignition of pressurized hydrogen released into a tube by Gang Luo, Wenping Zhang, Zixuan Yang, Lijing Zhang, Jinfeng Zhang

“…In this paper, OpenFOAM is employed to study three different transport models for predicting spontaneous ignition of pressurized hydrogen released into a tube. …”
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Performance of deep learning models in predicting the nugent score to diagnose bacterial vaginosis by Naoki Watanabe, Tomohisa Watari, Kenji Akamatsu, Isao Miyatsuka, Yoshihito Otsuka

“…This study aimed to evaluate the performance of deep learning models in predicting the Nugent score to improve diagnostic consistency and accuracy. …”
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LLM-Prop: predicting the properties of crystalline materials using large language models by Andre Niyongabo Rubungo, Craig Arnold, Barry P. Rand, Adji Bousso Dieng

“…Current methods for predicting crystal properties focus on modeling crystal structures using graph neural networks (GNNs). …”
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Predicting Soft Soil Settlement with a FAGSO-BP Neural Network Model by Binhui Ma, Yarui Xiao, Tian Lan, Chao Zhang, Zengliang Wang, Zeshi Xiang, Yuqi Li, Zijing Zhao

“…The FAGSO-BP neural network forecasting model is used to predict the soft foundation settlement of Hunan Wuyi Expressway Project. …”
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Predicting Response to Transarterial Chemoembolization in Hepatocellular Carcinoma Using Machine Learning Models by Niharika Dutta, Pankaj Gupta

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Light source classification and colour change modelling for understanding and predicting pigments discolouration by Panagiotis Siozos, Letizia Monico, Aldo Romani, Costanza Miliani, Brenda Doherty, Irina Crina Anca Sandu, Hartmut Kutzke, Ingrid M T Flåte, Petros Stavroulakis, Sophia Sotiropoulou

“…This model is experimentally validated by artificial ageing tests on two sets of model samples made of historical pigments (strontium yellow and Prussian blue mixed with lead white) using three white light sources (two WLEDs and a xenon light source). …”
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Proposing a machine learning-based model for predicting nonreassuring fetal heart by Nasibeh Roozbeh, Farideh Montazeri, Mohammadsadegh Vahidi Farashah, Vahid Mehrnoush, Fatemeh Darsareh

“…Although this study found that the classification tree models performed well in predicting NFH, more research is needed to make a better conclusion on the performance of ML models in predicting NFH.…”
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Predicting DNA Reactions with a Quantum Chemistry‐Based Deep Learning Model by Likun Wang, Na Li, Mengyao Cao, Yun Zhu, Xiewei Xiong, Li Li, Tong Zhu, Hao Pei

“…Abstract In this study, a deep learning model based on quantum chemistry is introduced to enhance the accuracy and efficiency of predicting DNA reaction parameters. …”
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Predicting the potential distribution of Taxus cuspidata in northeastern China based on the ensemble model by Baoliang Chang, Chen Huang, Bingming Chen, Ziwen Wang, Xingyuan He, Wei Chen, Yanqing Huang, Yue Zhang, Shuai Yu

“…In this study, a combined model was employed to predict potentially suitable habitats for T. cuspidata based on extant data of T. cuspidata distributions in northeastern China. …”
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Hybrid TCN-transformer model for predicting sustainable food supply and ensuring resilience by Ibrahim Alrashdi, Rasha M. Abd El-Aziz, Ahmed I. Taloba, Mohammed Farsi

“…Hybrid design enables faster training, increased interpretability, and better prediction accuracy than current methods. Results from experiments have revealed that the suggested model surpasses the performance of the stand-alone TCN, ARIMA, LSTM, and GRU models in terms of accuracy of predictions, efficiency of computations, and adaptability. …”
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Construction of a risk prediction model for occupational noise-induced hearing loss using routine blood and biochemical indicators in Shenzhen, China: a predictive modelling study by Wenting Feng, Wen Zhang, Yan Guo, Naixing Zhang, Liang Zhou, Dafeng Lin, Linlin Chen, Caiping Li, Liuwei Shi, Xiangli Yang, Peimao Li, Dianpeng Wang

“…Routine blood and biochemical indicators were extracted from the case data, and a range of machine learning algorithms including extreme gradient boosting (XGBoost) were employed to construct predictive models. The model underwent refinement to identify the most representative variables, and decision curve analysis was conducted to evaluate the net benefit of the model across various threshold levels.Primary outcome measures Model creation data set and validation data sets: ONIHL.Results The prediction model, developed using XGBoost, demonstrated exceptional performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.942, a sensitivity of 0.875 and a specificity of 0.936 on the validation data set. …”
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Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study by Eric Kamana, Jijun Zhao, Di Bai

“…The LSTMSeq2Seq model achieved an average prediction accuracy of 87.3%.Conclusions The LSTMSeq2Seq model significantly improved the prediction of malaria re-emergence based on the influence of climatic factors. …”
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Predictive Modeling for Cardiovascular Disease in Patients Based on Demographic and Biometric Data by Abayomi Danlami Babalola, Kayode Francis Akingbade, Daniel Olakunle

“…The results demonstrate that all three models achieve accuracy performance in predicting CVD events, with AUC values ranging from 0.85 to 0.92. …”
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Flexible Model Predictive Control for Bounded Gait Generation in Humanoid Robots by Tianbo Yang, Yuchuang Tong, Zhengtao Zhang

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