Predicting the Future Failures of Urban Water Systems: Integrating Climate Change and Machine Learning Prediction Models by Melica Khashei, Fatemeh Boloukasli ahmadgourabi, Rebecca Dziedzic

“…However, the integration of these climate-related factors into watermain failure prediction models, with a specific focus on climate change impacts, remains insufficiently explored. …”
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Clinical Prediction Models Based on Traditional Methods and Machine Learning for Predicting First Stroke: Status and Prospects by ZHANG Zijiao, DING Shunjing, ZHAO Di, LIANG Jun, LEI Jianbo

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Prediction, Explanation, and Control: The Use of Mental Models in Dynamic Environments by Roman Tikhonov, Simon DeDeo

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Risk prediction models for breast cancer: a systematic review by Jiang Li, He Li, Jie He, Ni Li, Yadi Zheng, Zheng Wu, Maomao Cao

“…Objectives To systematically review and critically appraise published studies of risk prediction models for breast cancer in the general population without breast cancer, and provide evidence for future research in the field.Design Systematic review using the Prediction model study Risk Of Bias Assessment Tool (PROBAST) framework.Data sources PubMed, the Cochrane Library and Embase were searched from inception to 16 December 2021.Eligibility criteria We included studies reporting multivariable models to estimate the individualised risk of developing female breast cancer among different ethnic groups. …”
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Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory by Yaoqi Peng, Yudong Zheng, Zengwei Zheng, Yong He

“…By incorporating crop yield data, a comparative analysis of 28 prediction models was performed, assessing performance metrics such as MSE, RMSE, MAE, MAPE, R², prediction speed, training time, and model size. …”
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Liner Wear Prediction Using Bayesian Regression Models and Clustering by Jacob Van Den Broek, Melinda Hodkiewicz, Adriano Polpo

“…Notably, Model 2 predicts remaining useful life within 95% credible intervals and identifies anomalous sensor performance. …”
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Reasoning language models for more transparent prediction of suicide risk by Roy H Perlis, Thomas H McCoy

“…The corresponding c-statistic was 0.64 (0.63–0.65), modestly poorer than the GPT4o model (0.67 (0.66–0.68)). In chain-of-thought reasoning, topics including Substance Abuse, Surgical Procedure, and Age-related Comorbidities were associated with correct predictions, while Fall-related Injury was associated with incorrect prediction.Conclusions Application of a reasoning model using local, consumer-grade hardware only modestly diminished performance in stratifying suicide risk.Clinical implications Smaller models can yield more secure, scalable and transparent risk prediction.…”
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Risk prediction models for diabetic retinopathy: a systematic review by Hui Huang, Yingmin Wu, Hejiang Ye, Jiaoyang Li, Ling Chen, Xuan Huang

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Regression models for productivity prediction in cactus pear cv. Gigante by Bruno V. C. Guimarães, Sérgio L. R. Donato, Ignacio Aspiazú, Alcinei M. Azevedo, Abner J. de Carvalho

“…This study aimed to adjust, through simple and multiple regression analysis, models for predicting the yield of cactus pear cv. …”
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Benchmarking shoreline prediction models over multi-decadal timescales by Yongjing Mao, Giovanni Coco, Sean Vitousek, Jose A. A. Antolinez, Georgios Azorakos, Masayuki Banno, Clément Bouvier, Karin R. Bryan, Laura Cagigal, Kit Calcraft, Bruno Castelle, Xinyu Chen, Maurizio D’Anna, Lucas de Freitas Pereira, Iñaki de Santiago, Aditya N. Deshmukh, Bixuan Dong, Ahmed Elghandour, Amirmahdi Gohari, Eduardo Gomez-de la Peña, Mitchell D. Harley, Michael Ibrahim, Déborah Idier, Camilo Jaramillo Cardona, Changbin Lim, Ivana Mingo, Julian O’Grady, Daniel Pais, Oxana Repina, Arthur Robinet, Dano Roelvink, Joshua Simmons, Erdinc Sogut, Katie Wilson, Kristen D. Splinter

“…The outcomes from this collaborative benchmarking competition critically review the present state-of-the-art in shoreline change prediction as well as reveal model limitations, facilitate improvements, and offer insights for advancing shoreline-prediction capabilities.…”
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Development of novel hybrid models for the prediction of Covid-19 in Kuwait by Ahmad Aldousari, Maria Qurban, Ijaz Hussain, Maha Al-Hajeri

“…There is a need to estimate a prediction model for the estimation of this epidemic size. …”
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Optimizing Deep Learning Models with Improved BWO for TEC Prediction by Yi Chen, Haijun Liu, Weifeng Shan, Yuan Yao, Lili Xing, Haoran Wang, Kunpeng Zhang

Subjects: “…TEC prediction…”
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Stock price prediction using combined GARCH-AI models by John Kamwele Mutinda, Amos Kipkorir Langat

“…The non-linear and non-stationary nature of financial time series data poses significant challenges for standalone statistical and neural network methods. While predictive modeling in finance often focuses on volatility, there is a notable lack of research on predicting actual stock prices, particularly in the African market. …”
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Calibrating AI Models for Wireless Communications via Conformal Prediction by Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone, Shlomo Shamai Shitz

“…This paper investigates the application of conformal prediction as a general framework to obtain AI models that produce decisions with formal calibration guarantees. …”
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Application of Thermodynamic Models to Predict the Solubility of Biologically Active Substances by K. S. Stepanov, G. N. Turmanidze, V. V. Sorokin, A. D. Sakharov

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Connectome-based prediction of functional impairment in experimental stroke models. by Oliver Schmitt, Peter Eipert, Yonggang Wang, Atsushi Kanoke, Gratianne Rabiller, Jialing Liu

“…Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. …”
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Numerical Permeation Models to Predict the Annulus Composition of Flexible Pipes by João Marcos B. Vieira, José Renato M. de Sousa

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Human genotype-to-phenotype predictions: Boosting accuracy with nonlinear models. by Aleksandr Medvedev, Satyarth Mishra Sharma, Evgenii Tsatsorin, Elena Nabieva, Dmitry Yarotsky

“…First, we ask if state-of-the-art nonlinear predictive models, such as boosted decision trees, can be more efficient for phenotype prediction than conventional linear models. …”
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Predictive models of sarcopenia based on inflammation and pyroptosis-related genes by Xiaoqing Li, Cheng Wu, Xiang Lu, Li Wang

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A study on the effectiveness of machine learning models for hepatitis prediction by Popy Khatun, Shafeel Umam, Rubaiya Binte Razzak, Iffat Binta Shamsuddin, Nahid Salma

“…We employed one traditional predictive model, logistic regression, alongside six machine learning models: support vector machine (SVM), K-nearest neighbors (KNN), artificial neural network (ANN), random forest (RF), AdaBoost, and XGBoost. …”
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