Recent development of risk-prediction models for incident hypertension: An updated systematic review. by Dongdong Sun, Jielin Liu, Lei Xiao, Ya Liu, Zuoguang Wang, Chuang Li, Yongxin Jin, Qiong Zhao, Shaojun Wen

“…<h4>Conclusions</h4>The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. …”
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Establishment and validation of a predictive model for coronary artery lesions in children with KDSS by Zhihui Zhao, Yue Yuan, Lu Gao, Hongxia Li, Qirui Li, Zhen Zhen, Shunying Zhao, Yanyan Xiao

Subjects: “…Coronary artery lesions prediction model…”
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Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model by Shaojiang Dong, Shirong Yin, Baoping Tang, Lili Chen, Tianhong Luo

“…After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy. …”
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PREDICTION STAGES OF THE DIFFICULT INERTIA SYSTEM BEHAVIOR WITH THE USE OF THE DEVELOPED SYSTEM MODEL by Vasyl V. Bychenkov

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The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies. by Viviane Naimy, Omar Haddad, Gema Fernández-Avilés, Rim El Khoury

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Prediction of power grid fault repair time based on multi-model fusion by Jianyue PAN, Yizhen WU, Hanlin XU

“…There are many types of power grid faults,and the reasons are complicated.The prediction of fault repair time is difficult.Due to the rise of new technologies such as deep learning,it is feasible to accurately mine the faulty worksheet and accurately predict the fault repair time.Taking the historical grid fault repair worksheet as the research object,the multi-model fusion prediction method was proposed,and the prediction results of LightGBM,XGBoost and LSTM were weighted and fused.The experimental results show that the multi-model fusion prediction method can accurately estimate the fault repair time and provide better support for the automation and intelligence of grid fault repair.…”
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Large language model trained on clinical oncology data predicts cancer progression by Menglei Zhu, Hui Lin, Jue Jiang, Abbas J. Jinia, Justin Jee, Karl Pichotta, Michele Waters, Doori Rose, Nikolaus Schultz, Sulov Chalise, Lohit Valleru, Olivier Morin, Jean Moran, Joseph O. Deasy, Shirin Pilai, Chelsea Nichols, Gregory Riely, Lior Z. Braunstein, Anyi Li

“…Abstract Subspecialty knowledge barriers have limited the adoption of large language models (LLMs) in oncology. We introduce Woollie, an open-source, oncology-specific LLM trained on real-world data from Memorial Sloan Kettering Cancer Center (MSK) across lung, breast, prostate, pancreatic, and colorectal cancers, with external validation using University of California, San Francisco (UCSF) data. …”
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Distributed Model Predictive Cross-Dimensional Formation Control for Heterogeneous Multiagent Systems by Haitao Liu, Zhaoxin Li, Qingqun Mai, Haochun Liu, Xuehong Tian

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AI Knows You: Deep Learning Model for Prediction of Extroversion Personality Trait by Anam Naz, Hikmat Ullah Khan, Sami Alesawi, Omar Ibrahim Abouola, Ali Daud, Muhammad Ramzan

“…The state-of-the-art shallow machine learning, ensemble modelling and deep learning models are applied. The main novelty is the exploration of latest sentence embeddings which captures semantic information of content in a better manner. …”
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Penalized Logistic Regression Models for Phenotype Prediction Based on Single Nucleotide Polymorphisms by seyedeh rezwan Hosseini, Farnaz Ghassemi, Mohammad Hasan Moradi

“…Some SNPs alone and some by interacting with others, play an important role in any phenotype or specific disease. Various models, including the regression models, are designed and implemented for the prediction of these diseases. …”
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LMI-Based Model Predictive Control for Underactuated Surface Vessels with Input Constraints by Lutao Liu, Zhilin Liu, Jun Zhang

“…A nonlinear model predictive control (MPC) is proposed for underactuated surface vessel (USV) with constrained inputs. …”
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PREDICT LEARNERS’ PERFORMANCE USING AN ONTOLOGICAL-BASED MODEL ON AN E-LEARNING PLATFORM by Safa Ridha Albo Abdullah

“…In learning analytics and educational data mining, a prominent challenge is posed by the lack of portability and transferability of predictive models across different courses. A novel ontology-based decision tree model is introduced in this study, which significantly enhances portability by incorporating semantic features. …”
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Thermal Performance of Lightweight Earth: From Prediction to Optimization through Multiscale Modeling by Séverine Rosa Latapie, Vincent Sabathier, Ariane Abou-Chakra

“…Mean-field homogenization techniques, including the Mori-Tanaka as well as double inclusion models, are used to develop predictive tools for thermal behavior, using rigorously selected experimental data. …”
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Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side by Ali Sharida, Abdullah Berkay Bayindir, Sertac Bayhan, Haitham Abu-Rub

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Fast and effective assessment for individuals with special needs form optimization and prediction models by Bilal Baris Alkan, Muhammet Kumartas, Serafettin Kuzucuk, Nesrin Alkan

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Research on Robust Adaptive Model Predictive Control Based on Vehicle State Uncertainty by Yinping Li, Li Liu

“…To address the performance degradation in model predictive control (MPC) under vehicle state uncertainties caused by external disturbances (e.g., crosswinds and tire cornering stiffness variations) and rigid constraint conflicts, we propose a robust MPC framework with adaptive weight adjustment and dynamic constraint relaxation. …”
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Towards Predictive Communication: The Fusion of Large Language Models and Brain–Computer Interface by Andrea Carìa

“…First, I will review the evolution of language models—from early rule-based systems to contemporary deep learning architectures—and their role in enhancing predictive writing. …”
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COWAVE: A labelled COVID-19 wave dataset for building predictive models. by Melpakkam Pradeep, Karthik Raman

“…We also use a simple eXtreme Gradient Boosting (XGBoost) model to provide a minimum standard for future classifiers trained on this dataset and demonstrate the utility of our dataset for the prediction of (future) waves. …”
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Prediction models for intraventricular hemorrhage in very preterm infants: a systematic review by Ping Xiong, Yonggang Wei, Lei Li, Houxin Kang, Zhangbin Yu, Hong Tang, Yuanlin Pu

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Construction of a disease risk prediction model for postherpetic pruritus by machine learning by Zheng Lin, Yuan Dou, Ru-yi Ju, Ping Lin, Yi Cao

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