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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PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…These methods involve the analysis of various types of data, including clinical assessments, imaging scans, and genetic markers, to develop accurate predictive models. Even in the initial stages of the conditions, machine learning techniques can discriminate between patients who have and do not have PD by identifying minor variations and traits from such multivariate data. …”
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Beyond analytic bounds: Re-evaluating predictive power in risky decision models by Or David Agassi, Ori Plonsky

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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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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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Post–Liver Transplant Outcomes: A Comparative Study of 6 Predictive Models by Christof Kaltenmeier, MD, Eishan Ashwat, BA, Hao Liu, MD, PhD, Charbel Elias, MD, Amaan Rahman, BA, Jason Mail-Anthony, BA, Isabel Neckermann, BA, Stalin Dharmayan, MD, Andrew Crane, MD, Godwin Packiaraj, MD, Subhashini Ayloo, MD, MPH, Armando Ganoza, MD, MBA, Vikraman Gunabushanam, MD, Michele Molinari, MD, MHS

“…Conclusions. None of the predictive models demonstrated sufficient precision to reliably identify LT recipients who died within 90 d and 1 y after LT. …”
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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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Evaluation of predictive accuracy of gene expression in pigs using machine learning models by Tianle ZHOU, Jinyan TENG, Zhiting XU, Zhe ZHANG

“…Subsequently, we evaluated the prediction accuracy of each model.ResultThere was a positive correlation between the prediction accuracy of machine learning models and the cis-h2 of genes. …”
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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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Bitcoin Trend Prediction with Attention-Based Deep Learning Models and Technical Indicators by Ming-Che Lee

“…This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. …”
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Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques by Oumoulylte Mariame, El Allaoui Ahmad, Farhaoui Yousef, Boughrous Ali Ait

“…To tackle these issues, it's crucial to set up prediction systems allowing officials to act before high pollution levels occur. …”
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Improving the Predictability of the Madden‐Julian Oscillation at Subseasonal Scales With Gaussian Process Models by Haoyuan Chen, Emil Constantinescu, Vishwas Rao, Cristiana Stan

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Deep learning models for hepatitis E incidence prediction leveraging Baidu index by Yanhui Guo, Li Zhang, Shengnan Pang, Xiya Cui, Xuechen Zhao, Yi Feng

“…Baidu index proves to be valuable for predicting hepatitis E incidence. Furthermore, stack layers and KAN can also improve the representational ability of LSTM models.…”
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Understanding Software Defect Prediction Through eXplainable Neural Additive Models by Ruiqi He, Yong Li, Chi Sun

Subjects: “…Software defect prediction…”
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