Limitations of XGBoost in Predicting Material Parameters for Complex Constitutive Models by Prates Pedro, Mitreiro Dário, Andrade-Campos António

“…Machine learning models, particularly Extreme Gradient Boosting, have been explored for predicting material parameters in constitutive models that describe the plastic behaviour of metal sheets. …”
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On Some Limitations of Current Machine Learning Weather Prediction Models by Massimo Bonavita

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Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction by Rusul L. Abduljabbar, Hussein Dia, Pei-Wei Tsai

“…This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. …”
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Reviews on Imaging-based Risk Prediction Models for Ischemic Stroke by Cui Liuping, Liu Ran, Liu Yumei, Zhou Fubo, Tao Yunlu, Xing Yingqi

“…Integrating image-based biomarkers into existing risk-prediction models may enhance risk stratification accuracy. …”
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Predicting volatility of bitcoin returns with ARCH, GARCH and EGARCH models by Hakan Yıldırım, Festus Victor Bekun

“…In this study we seek to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an investment tool in recent times. …”
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Statistical models for predicting the number of under-five mortality in Nepal. by Madhav Kumar Bhusal, Shankar Prasad Khanal

“…<h4>Objective</h4>This study aimed to develop a suitable statistical model using the associated factors to predict the number of under-five mortality a mother in Nepal encountered throughout her lifetime.…”
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LLM-driven semantic explanations for soil moisture prediction models by Bamory Ahmed Toru Koné, Khouloud Boukadi, Rima Grati, Emna Ben Abdallah, Massimo Mecella

“…We propose a framework that leverages large language models (LLMs) to generate textual explanations based on a proposed irrigation and soil moisture ontology, thus making the model's predictions more understandable to farmers. …”
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Prediction of Vapor-Liquid Equilibria Using CEOS /GE Models

“…To predict VLE data in multicomponent symmetric and asymmetric mixtures such as systems that contain light gases (nitrogen, carbon dioxide, etc.) and heavy hydrocarbons, the SRK equation of state has been combined with excess Gibbs energy models. …”
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Development of time to event prediction models using federated learning by Rasmus Rask Kragh Jørgensen, Jonas Faartoft Jensen, Tarec El-Galaly, Martin Bøgsted, Rasmus Froberg Brøndum, Mikkel Runason Simonsen, Lasse Hjort Jakobsen

“…Alternatively, federated learning (FL) can be utilized to train models based on data located at multiple sites. Method We propose two methods for training time-to-event prediction models based on distributed data, relying on FL algorithms, for time-to-event prediction models. …”
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Integrated Machine Learning Models for Bakery Product Defect Prediction by Nataliia Zaiets, Nataliia Lutska, Lidiia Vlasenko

Subjects: “…defect prediction…”
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Satellite-Based Validation of Contrail Prediction Models for Sustainable Aviation by Baddireddi Sree Chandana, Pelleti Nandieswar Reddy, Rithvika Alapati, Sai Aswath Reddy, Radha Doraisamy, Uma Sankari

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Groundwater quality parameters prediction based on data-driven models by Mohammed Falah Allawi, Yasir Al-Ani, Arkan Dhari Jalal, Zainab Malik Ismael, Mohsen Sherif, Ahmed El-Shafie

“…According to the evaluation results, adding more input variables can sometimes increase the efficacy of the proposed models with regard to prediction accuracy. Moreover, the findings show that the PNN model provides a promising performance in predicting the groundwater’s water quality (WQ) matrices, showing superior performance compared to the RBFNN model.…”
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Comparison of Risk Assessment Models for Predicting Postpartum Venous Thromboembolism by Yonghui Xu, Sha Zhu, Ji He, XingSheng Xue, Fei Xiao

“…This study aimed to validate the accuracy of currently used risk assessment models (RAMs) for predicting postpartum VTE. …”
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Guide to evaluating performance of prediction models for recurrent clinical events by Laura J. Bonnett, Thomas Spain, Alexandra Hunt, Jane L. Hutton, Victoria Watson, Anthony G. Marson, John Blakey

“…Therefore, prediction models for outcomes associated with chronic conditions should include all repeated events. …”
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Assimilating Observed Surface Pressure Into ML Weather Prediction Models by L. C. Slivinski, J. S. Whitaker, S. Frolov, T. A. Smith, N. Agarwal

“…Abstract There has been a recent surge in development of accurate machine learning (ML) weather prediction models, but evaluation of these models has mainly been focused on medium‐range forecasts, not their performance in cycling data assimilation (DA) systems. …”
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Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

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Machine learning models for prognosis prediction in regenerative endodontic procedures by Jing Lu, Qianqian Cai, Kaizhi Chen, Bill Kahler, Jun Yao, Yanjun Zhang, Dali Zheng, Youguang Lu

“…Abstract Background This study aimed to establish and validate machine learning (ML) models to predict the prognosis of regenerative endodontic procedures (REPs) clinically, assisting clinicians in decision-making and avoiding treatment failure. …”
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Evaluating Road Crash Severity Prediction with Balanced Ensemble Models by Alexei Roudnitski

“…The model is evaluated based on ROC-AUC score, with a result of 0.68, indicating a moderate level of predictive accuracy. …”
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Research on Customer Churn Prediction Using Machine Learning Models by Jia Xiaolei

“…However, in uncomplex customer churn predictions, the decision tree model gets a high prediction score due to its accuracy rate of 90.8%. …”
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Macroeconomic models for predicting indirect impacts of disasters: A review by Tinger Zhu, Charalampos Avraam, Jack W. Baker

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