Application of Three Neural Network Models in the Prediction ofStratospheric Wind Field by YUAN Junjie, LUO Rubin, LIAO Jun, YANG Zechuan, WANG Ning, LI Jun

“…Wind field forecast is of great significance for aerostat trajectory prediction. Traditional theoretical models can only predict wind speed in the next few hours, while BP neural network models can predict wind speeds in next few days. …”
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Artificial intelligence prediction models for acute respiratory distress syndrome：progress and challenges by MENG Xianglin*，XIONG Yaxin，HAN Ci，GE Xin，ZHAO Mingyan

“…Compared with traditional scoring systems，AI models perform well in predicting mortality and optimizing clinical decision ⁃ making，especially through multimodal data fusion，which can significantly improve the prediction accuracy of the models. …”
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The impact of CNN MHAM-enhanced WRF and BPNN models for user behavior prediction by Kaixin Zheng, Zhensen Liang

“…Abstract To address the challenge of user behavior prediction on artificial intelligence (AI)-based online education platforms, this study proposes a novel ensemble model. …”
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Improved epigenetic age prediction models by combining sex chromosome and autosomal markers by Zhong Wan, Peter Henneman, Huub C. J. Hoefsloot, Ate D. Kloosterman, Pernette J. Verschure

“…Abstract Background Alterations in epigenetic DNA methylation (DNAm) can be used as an accurate and robust method for biological age prediction. We assessed the feasibility of incorporating sex chromosomal DNAm markers into a six autosomal DNAm CpG marker-based age prediction model, since DNAm-based prediction modeling has predominantly relied on analyzing DNAm patterns on autosomes. …”
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Performance Comparison of Basic Pesticide Residue Prediction Models in Domestic Agricultural Environments by Hyun-Geoun Park, Sang-Ik Suh, Bum-Seok Lee, Hyo-Sub Lee, Seo Jin Ki

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Deterioration Models and Service Life Prediction of Vertical Assets of Urban Water Systems by M. Cabral, D. Loureiro, C. Amado, D. Covas

Subjects: “…deterioration models…”
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Nonequilibrium Molecular Velocity Distribution Functions Predicted by Macroscopic Gas Dynamic Models by Maksim Timokhin, Yevgeniy Bondar

“…In the present study, abilities of various macroscopic models (Navier–Stokes–Fourier, Burnett, original and regularized Grad’s 13-moment equations) in predicting the nonequilibrium molecular velocity distribution are examined. …”
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Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds by Wu Qiao, Tong Xie, Jing Lu, Tinghan Jia

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Traffic Flow Prediction Based on Large Language Models and Future Development Directions by Zhang Muhua, Zhao Wenzheng

“…This paper references a responsible and reliable traffic flow prediction model (R2T-LLM) based on large language models (LLMs). …”
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Applicability of machine learning models for drought prediction using SPI in Kalahandi, Odisha by AMIT PRASAD, R.K. SINGH, K V RAMANA RAO, C. K. SAXENA

“…Overall, machine learning models, particularly ANN and SVM, proved to be superior for predicting both long-term (SPI-12) and short-term (SPI-6) precipitation indices, highlighting their effectiveness for accurate drought forecasting. …”
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Prediction of influenza peaks in Russian cities: Comparing the accuracy of two SEIR models by Vasiliy N. Leonenko, Sergey V. Ivanov

Subjects: “…mathematical modeling…”
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Leveraging Frozen Foundation Models and Multimodal Fusion for BEV Segmentation and Occupancy Prediction by Seamie Hayes, Ganesh Sistu, Ciaran Eising

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Assessment and prediction models for the quantitative and qualitative reserve of the ovary using machine learning by Hiroshi Koike, Miyuki Harada, Kaname Yoshida, Katsuhiko Noda, Chihiro Tsuchida, Toshihiro Fujiwara, Akari Kusamoto, Zixin Xu, Tsurugi Tanaka, Nanoka Sakaguchi, Chisato Kunitomi, Nozomi Takahashi, Yoko Urata, Kenbun Sone, Osamu Wada-Hiraike, Yasushi Hirota, Yutaka Osuga

“…Our aim is to produce a model for prediction of the ovarian reserve that contributes to preconception care and precision medicine. …”
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Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites by Feng Bin, Shahab Hosseini, Jie Chen, Pijush Samui, Hadi Fattahi, Danial Jahed Armaghani

“…Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. …”
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Prediction of Horizontal in Situ Stress in Shale Reservoirs Based on Machine Learning Models by Wenxuan Yu, Xizhe Li, Wei Guo, Hongming Zhan, Xuefeng Yang, Yongyang Liu, Xiangyang Pei, Weikang He, Longyi Wang, Yaoqiang Lin

“…To address the limitations of traditional methods in modeling complex nonlinear relationships in horizontal in situ stress prediction for shale reservoirs, this study proposes an integrated framework that combines well logging interpretation with machine learning to accurately predict horizontal in situ stress in shale reservoirs. …”
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Prediction Models for Gestational Diabetes Mellitus: Diagnostic Utility of Clinical and Biochemical Markers by Asha Dinakaran, Ramachandran Thiruvengadam, Abu R. Srinivasan, Manikandan, Sunil K. Nanda, Mary Daniel, Reeta Rajagambeeram

Subjects: “…gdm prediction models…”
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Prediction of Propellant Electrostatic Sensitivity Based on Small-Sample Machine Learning Models by Fei Wang, Kai Cui, Jinxiang Liu, Wenhai He, Qiuyu Zhang, Weihai Zhang, Tianshuai Wang

“…A dataset comprising 18 experimental formulations was employed to train and evaluate six machine learning models. Among them, the Random Forest (RF) model achieved the highest predictive accuracy (R² = 0.9681), demonstrating a strong generalization capability through leave-one-out cross-validation. …”
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A review of a priori regression models for warfarin maintenance dose prediction. by Ben Francis, Steven Lane, Munir Pirmohamed, Andrea Jorgensen

“…Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. …”
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Performance of five dynamic models in predicting tuberculosis incidence in three prisons in Thailand. by Nithinan Mahawan, Thanapoom Rattananupong, Puchong Sri-Uam, Wiroj Jiamjarasrangsi

“…This study examined the ability of the following five dynamic models for predicting pulmonary tuberculosis (PTB) incidence in a prison setting: the Wells-Riley equation, two Rudnick & Milton-proposed models based on air changes per hour and liters per second per person, the Issarow et al. model, and the applied susceptible-exposed-infected-recovered (SEIR) tuberculosis (TB) transmission model. …”
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Comparison of spatial prediction models from Machine Learning of cholangiocarcinoma incidence in Thailand by Oraya Sahat, Supot Kamsa-ard, Apiradee Lim, Siriporn Kamsa-ard, Matias Garcia-Constantino, Idongesit Ekerete

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