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

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Application of a Nomogram Model in Predicting Postoperative Delirium Following Percutaneous Coronary Intervention by Yaxin Xiong, Ze Meng, Jiuyue Sun, Yucheng Qi, Kuo Wang, Ping Huang, Qiuyue Yang, Renliang Fan, Jiaman Guan, Mingyan Zhao, Xianglin Meng

“…The clinical value of the prediction model was tested using decision curve analysis (DCA). …”
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Linear and Machine Learning modelling for spatiotemporal disease predictions: Force-of-Infection of Chagas disease. by Julia Ledien, Zulma M Cucunubá, Gabriel Parra-Henao, Eliana Rodríguez-Monguí, Andrew P Dobson, Susana B Adamo, María-Gloria Basáñez, Pierre Nouvellet

“…<h4>Conclusions/significance</h4>The choice of FoI predictive models will depend on the objectives of the analysis. …”
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DeepAT: A Deep Learning Wheat Phenotype Prediction Model Based on Genotype Data by Jiale Li, Zikang He, Guomin Zhou, Shen Yan, Jianhua Zhang

“…Deep learning has become an important tool for phenotype prediction due to its abilities in automatic feature learning, nonlinear modeling, and high-dimensional data processing. …”
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Prediction of Income of Rural Residents Based on Markov Model under the Background of Rural Revitalization by Xiaorong Li, Qianli Xing

“…And the results show that the prediction accuracy of the model is higher. It shows that the results of using the grey Markov model to predict the income of rural residents in China from 2021 to 2025 are reliable. …”
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Wind Power Prediction Based on a Hybrid Model of ICEEMDAN and ModernTCN-Informer by Jun He, Zijian Cheng, Zijie Zhong, Lizhuo Liang, Jianhui Ye

“…The multi-step prediction accuracy is superior to that of the comparison models, verifying the effectiveness of the proposed model.…”
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Winter Wheat Yield Prediction Based on the ASTGNN Model Coupled with Multi-Source Data by Zhicheng Ye, Xu Zhai, Tianlong She, Xiaoyan Liu, Yuanyuan Hong, Lihui Wang, Lili Zhang, Qiang Wang

“…Furthermore, the ASTGNN model provided better prediction performance than two traditional crop yield prediction models (R² = 0.70, RMSE = 0.21 t/ha, MAE = 0.17 t/ha). …”
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Quantitative imaging outperforms No-reflow in predicting functional outcomes in a translational stroke model by Justine Debatisse, Lucie Chalet, Omer Faruk Eker, Tae-Hee Cho, Guillaume Becker, Océane Wateau, Marlène Wiart, Nicolas Costes, Inés Mérida, Christelle Léon, Jean-Baptiste Langlois, Sophie Lancelot, François Lux, Timothé Boutelier, Norbert Nighoghossian, Laura Mechtouff, Emmanuelle Canet-Soulas

“…We used quantitative PET-MRI imaging to characterize early microvascular damages in a preclinical non-human primate model mimicking endovascular mechanical thrombectomy (EVT). …”
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Prediction of Remaining Useful Life of Operating Mechanism Driven by Deep Feature in Classification Model by Xilei Dong, Fuping Zhang, Yunyang Ye, Huan Zhang

“…To address the drawbacks of high computational complexity and slow convergence speed in existing data-driven regression models, in this research, based on deep feature linear fitting of classification models, a remaining useful life prediction method for operating mechanism is proposed. …”
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Deep learning model for precise and rapid prediction of tomato maturity based on image recognition by Muhammad Waseem, Muhammad Muzzammil Sajjad, Laraib Haider Naqvi, Yaqoob Majeed, Tanzeel Ur Rehman, Tayyaba Nadeem

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A method for filling traffic data based on feature-based combination prediction model by Haicheng Xiao, Xueyan Shen, Jianglin Li, Xiujian Yang

“…Therefore, this study proposes a feature generation model based on LightGBM-GRU, combined with a SARIMA-GRU prediction model, to more thoroughly capture and enrich the data characteristics. …”
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Development of hybrid computational model for simulation of heat transfer and temperature prediction in chemical reactors by Kamal Y. Thajudeen, Mohammed Muqtader Ahmed, Saad Ali Alshehri

“…The Deep Neural Network model, showcasing exceptional predictive accuracy, obtained an outstanding R2 score of 0.99147. …”
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Development and validation of prognostic prediction model for submandibular gland cancer based on the SEER database by Junkun He, Feng Zhao, Jiangmiao Li, Qiyun Li, Fangyu Wei, Jiping Su

“…Results: The established prognostic prediction model demonstrated good accuracy and reliability. …”
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Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response by Qiqing Zhang, Haidong He, Yi Wei, Guoping Li, Lu Shou

“…The prediction model not only reflects the strong correlation between gene expression and LUSC prognosis, but also provides clinicians with an effective tool to predict patients' survival prospects. …”
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Research on Mode Shift Control of Multimode Hybrid Systems Based on Hybrid Model Prediction by Xinxin Zhao, Jiadian Liu, Bing Li

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Memory-Efficient Imagification for Light-Weight Prediction Model of Multivariate Time-Series Data by Seungwoo Kang, Ohyun Jo

“…Compared to benchmark imagification methods (Recurrence Plot, Gramian Angular Field) and a conventional multivariate LSTM model, the proposed approach achieves competitive prediction accuracy with significantly reduced training time. …”
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AHP-Guided Stacked Ensemble Modeling for Student Engagement Level Prediction in Online Education by Jingjing Fu, Linjie Luo, Zhifeng Zhong, Jiaming Qin

“…The main goal is to improve the performance of prediction models using an innovative hybrid approach. …”
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Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…Finally, the predicted results were fused, and evaluation coefficients were derived using evaluation indicators, demonstrating the accuracy and stability of the model. …”
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Flow Field Prediction of Gas-Solid Fluidized Bed Based on POD and Surrogate Model by Jialei FENG, Xiaohui ZHAO, Nan TU, Jiabin FANG, Ruihe HU, Yibo XU

“…Moreover, the reduced-order model improves the computational efficiency by nearly 400 times while maintaining high reconstruction and prediction accuracy.…”
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Machine learning model for predicting corneal stiffness and identifying keratoconus based on ocular structures by Longhui Li, Yifan Xiang, Xi Chen, Duoru Lin, Lanqin Zhao, Jun Xiao, Zhenzhe Lin, Jianyu Pang, Xiaotong Han, Lixue Liu, Yuxuan Wu, Zhenzhen Liu, Jingjing Chen, Jing Zhuang, Keming Yu, Haotian Lin

“…Conclusion: Our models predicted SP-A1 and cCBI relatively accurately in keratoconus and normal corneas. …”
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