Uncertainty prediction of wind speed based on improved multi-strategy hybrid models by Xinyi Xu, Shaojuan Ma, Cheng Huang

“…Finally, the NOA-BiTCN-BiGRU model was built to perform wind speed interval prediction. …”
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EVALUATION OF MACHINE LEARNING MODELS FOR BORON PREDICTION IN ANDISOL SOILS OF NARIÑO-COLOMBIA by David Álvarez Sánchez, Xilena López Estrella, Eduar Manso Ordoñez, Laura López Rivera, Jeison Rodriguez Valenzuela

“…That is why this research uses the predictive capacity of the machine learning (ML) approach. …”
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Between Domestic and Commonly Known Bankruptcy Prediction Models, How Differ It Can Be? by Muhamad Fiqri Aripin

“…As such the needs of research and improvement of Bankruptcy Prediction Models as risk assessment tool is a must. …”
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Development and validation of machine learning models for predicting blastocyst yield in IVF cycles by Wen-jie Huo, Fei Peng, Song Quan, Xiao-cong Wang

“…We then stratified predictions and actual yields into three categories (0, 1–2, and ≥ 3 blastocysts) to evaluate the model’s discriminative performance. …”
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Prediction of the volume of shallow landslides due to rainfall using data-driven models by J. Tuganishuri, C.-Y. Yune, G. Kim, S. W. Lee, M. D. Adhikari, S.-G. Yum

“…The objectives of this research are to construct a model using advanced data-driven algorithms (i.e., ordinary least squares or linear regression (OLS), random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), generalized linear model (GLM), decision tree (DT), deep neural network (DNN), <span class="inline-formula"><i>k</i></span>-nearest-neighbor (KNN), and ridge regression (RR) algorithms) for the prediction of the volume of landslides due to rainfall, considering geological, geomorphological, and environmental conditions. …”
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Auditing the fairness of the US COVID-19 forecast hub's case prediction models. by Saad Mohammad Abrar, Naman Awasthi, Daniel Smolyak, Nekabari Sigalo, Vanessa Frias Martinez

“…In this paper, we carry out a comprehensive fairness analysis of the Forecast Hub model predictions and we show statistically significant diverse predictive performance across social determinants, with minority racial and ethnic groups as well as less urbanized areas often associated with higher prediction errors. …”
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Developing new machine-learning intelligent models to predict the excavation-tunnel displacements by Abdollah Tabaroei, Muhand Jawad Jasim, Ali Mohammed Al-Araji, Amir Hossein Vakili

“…Finally in the third part, based on the simulation results two models developed for predict and validate the $${\delta }_{hrm}$$ , $${\delta }_{vm}$$ , $${\delta }_{htm}$$ and $${\delta }_{vtm}$$ values. …”
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Prediction of seepage flow through earthfill dams using machine learning models by Issam Rehamnia, Ahmed Mohammed Sami Al-Janabi, Saad Sh. Sammen, Binh Thai Pham, Indra Prakash

“…Moreover, including the periodicity factors improves prediction accuracy of the machine learning models.…”
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Machine learning models for predicting the risk of depressive symptoms in Chinese college students by Chengfu Yu, Xiangxuan Kong, Weijie Yu, Xingcan Ni, Jing Chen, Xiaoyan Liao

“…Given the limitations of traditional linear models in managing high-dimensional data, this study employed machine learning techniques to predict depressive symptoms.MethodData were collected from 1,635 Chinese college students and included 38 sociodemographic, psychological, and social variables. …”
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COMPARISON OF LEAST SQUARE SPLINE AND ARIMA MODELS FOR PREDICTING INDONESIA COMPOSITE INDEX by Any Tsalasatul Fitriyah, Nur Chamidah, Toha Saifudin

“…The parametric approach in this study uses the ARIMA model. ARIMA is widely used to predict time series data. …”
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Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…This study aimed to determine the critical risk factors associated with suicidal behavior mortality and identify an effective classification model for predicting suicidal behavior outcomes. …”
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Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models by Yafeng Feng, Yong Sun, Chengfang Hang

“…A multilayer perceptron and AdaBoost ensemble are employed for comprehensive feature fusion and state prediction. Experimental results show that our model achieves a significant improvement in classification accuracy, with a 12% increase in emotional state recognition compared to traditional models, and a 15% reduction in prediction error for physiological state estimation. …”
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Prediction of the burst pressure for defective pipelines using different semi-empirical models by S. Budhe, M.D. Banea, S. de Barros

“…The main aim of this work is to predict the theoretical burst pressure of defective pipelines using different semi-empirical models and compare them with the hydrostatic test results. …”
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An Online Interactive Experiment Management System for CMA Numerical Weather Prediction Models by Wang Bin, Hu Jiangkai, Cui Yingjie, Zhou Bin, Sun Jing, Zhao Chunyan, Lou Panxing

Subjects: “…numerical weather prediction model…”
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Effectiveness of three machine learning models for prediction of daily streamflow and uncertainty assessment by Luka Vinokić, Milan Dotlić, Veljko Prodanović, Slobodan Kolaković, Slobodan P. Simonovic, Milan Stojković

“…This study evaluates three Machine Learning (ML) models—Temporal Kolmogorov-Arnold Networks (TKAN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)—focusing on their capabilities to improve prediction accuracy and efficiency in streamflow forecasting. …”
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Calibrated muscle models improve tracking simulations without enhancing gait predictions. by Filippo Maceratesi, Míriam Febrer-Nafría, Josep M Font-Llagunes

“…<h4>Objectives</h4>This study presents two main aims: (i) to assess functionally-calibrated musculoskeletal models (FCMs) in both tracking and predictive simulations of human motion, against non-linearly scaled models (NSMs), and (ii) to examine the effects of three different variations of our baseline functional calibration approach on the results of tracking and predictive simulations.…”
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Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market by Moumita Barua, Teerath Kumar, Kislay Raj, Arunabha M. Roy

“…This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. …”
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Critical Gaps in ARDS Prediction Models: A Call for Clinical Pragmatism [Letter] by Li J

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Improving deceased donor kidney utilization: predicting risk of nonuse with interpretable models by Ruoting Li, Sait Tunç, Osman Y. Özaltın, Matthew J. Ellis

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Prediction and Analysis of Ship Engine Vibration Signals Based on Prompted Language Models by Yunzhou Zhang, Yanghui Tan, Shuai Hao, Hong Zeng, Peisheng Sang, Ya Gao

“…The proposed approach was compared with traditional models, including LSTM, RNN, and SVR, in vibration signal prediction tasks. …”
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