Patient perspective on predictive models in healthcare: translation into practice, ethical implications and limitations? by Sarah Markham

“…In this perspective article, we consider the use of predictive models in healthcare and associated challenges. …”
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Analysis of the factors that influence the quality of rapeseed and sunflower seeds and development of predictive models☆ by El Msayryb Abdellatif, Champolivier Luc, Debaeke Philippe, Flénet Francis

“…The model achieved good predictive accuracy, with over 84% of well-predicted values falling within acceptable ranges for rapeseed seed quality (oil and protein concentrations), and the same for sunflower protein concentration. …”
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Evaluation of Prediction Performances of Deep Learning Models for the Aerodynamic Characteristics of Flettner Rotors by Seo Janghoon, Park Jung Yoon, Ma Juhwan, Kim Young Bu, Park Dong-Woo

“…This study investigates the prediction of the aerodynamic characteristics of Flettner rotors through three deep learning models. …”
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Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction by Raja Ayu Mahessya, Dian Eka Putra, Rostam Ahmad Efendi, Rayendra, Rozi Meri, Riyan Ikhbal Salam, Dedi Mardianto, Ikhsan, Ismael, Arif Rizki Marsa

“…Post-operative kyphosis represents a significant complication following pediatric spinal corrective surgery, necessitating sophisticated prediction methods to identify high-risk patients. This study developed and evaluated machine learning models for kyphosis prediction using a dataset of 81 pediatric patients by comparing the logistic regression and decision tree approaches. …”
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Enhancing Load Prediction Accuracy using Optimized Support Vector Regression Models by Abdulsemiu Olawuyi, Titus Ajewole, Muyideen Lawal, Tolulope Awofolaju

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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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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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Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis by Jonathan Hourmozdi, MD, MSAI, MA, Nicholas Easton, MSAI, Simon Benigeri, MSAI, James D. Thomas, MD, Akhil Narang, MD, David Ouyang, MD, Grant Duffy, BS, Ross Upton, PhD, Will Hawkes, PhD, Ashley Akerman, PhD, Ike Okwuosa, MD, Adrienne Kline, MD, PhD, Abel N. Kho, MD, Yuan Luo, PhD, Sanjiv J. Shah, MD, Faraz S. Ahmad, MD, MS

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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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Letter: the limitations of gene set-based predictive models: a critical assessment by Lijing Yang, Wenbo Zhao, Min Jiang, Xianqiang Liu

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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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