Prediction of the strength characteristics of basalt fibre reinforced concrete using explainable machine learning models by B. Y. Wickramasuriya, Yasitha Alahakoon, B. R. G. A. Krishantha, Janaka Alawatugoda, I. U. Ekanayake

“…This study applies machine learning (ML) models, paired with explainable artificial intelligence (XAI), to predict the compressive, flexural, and tensile strengths of BFRC efficiently and transparently. …”
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Optimized Prediction of Weapon Effectiveness in BVR Air Combat Scenarios Using Enhanced Regression Models by Andre R. Kuroswiski, Annie S. Wu, Angelo Passaro

“…This study investigates high-performance models for predicting the Weapon Engagement Zone (WEZ) in beyond-visual-range (BVR) air combat scenarios. …”
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A comparative analysis of deep learning models for accurate spatio-temporal soil moisture prediction by Litao Zhu, Wen Dai, Jiru Huang, Zicong Luo

“…All models demonstrated increased prediction errors as SM values exceed 75%, suggesting limitations in the models’ ability to predict extreme events. …”
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Optimizing pore pressure prediction in earth dams through the integration of panel data and intelligent models by Behrang Beiranvand, Taher Rajaee, Mehdi Komasi

“…This study presents an innovative spatiotemporal modeling approach that leverages data from properly functioning instruments to reconstruct missing measurements through panel data analysis. …”
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Trajectory prediction of human-driven vehicles on the basis of risk field theory and interaction multiple models by Zhaojie Wang, Guangquan Lu, Jinghua Wang, Haitian Tan, Renjing Tang

Subjects: “…trajectory prediction…”
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A comparative approach of machine learning models to predict attrition in a diabetes management program. by Samantha Kanny, Grisha Post, Patricia Carbajales-Dale, William Cummings, Janet Evatt, Windsor Westbrook Sherrill

“…These findings underscore the difficulty for models to accurately predict health behavior outcomes, highlighting the need for future research to improve predictive modeling to better support patient engagement and retention.…”
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Spatio-Temporal Generalization of VIS-NIR-SWIR Spectral Models for Nitrogen Prediction in Sugarcane Leaves by Carlos Augusto Alves Cardoso Silva, Rodnei Rizzo, Marcelo Andrade da Silva, Matheus Luís Caron, Peterson Ricardo Fiorio

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Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic by Hayley B Gershengorn, Monisha C Bhatia, Prem Rajendra Warde, Samira S Patel, Tanira D Ferreira, Dipen J Parekh, Kymberlee J Manni, Bhavarth S Shukla

“…Objectives We describe a hospital’s implementation of predictive models to optimise emergency response to the COVID-19 pandemic.Methods We were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. …”
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Characterization and Prediction of the Ghana Stock Exchange Composite Index Utilizing Bayesian Stochastic Volatility Models by Osei K. Tweneboah, Kwesi A. Ohene-Obeng, Maria C. Mariani

“…The paper then advances to predictive modeling, employing an innovative approach with four variations of Stochastic Volatility (SV) models: SV with linear regressors, SV with Student’s <i>t</i> errors, SV with leverage effects, and a hybrid model combining Student’s <i>t</i> errors with leverage. …”
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Machine Learning for Chinese Corporate Fraud Prediction: Segmented Models Based on Optimal Training Windows by Chang Chuan Goh, Yue Yang, Anthony Bellotti, Xiuping Hua

“…We propose a comprehensive and practical framework for Chinese corporate fraud prediction which incorporates classifiers, class imbalance, population drift, segmented models, and model evaluation using machine learning algorithms. …”
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Improving generalization in slope movement prediction using sequential models and hierarchical transformer predictor autoencoder by Praveen Kumar, Priyanka Priyanka, K. V. Uday, Varun Dutt

“…Machine Learning (ML) models can help in the prediction of landslide hazards. …”
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Prediction Models for Risk of Cardiorespiratory Morbidity/Mortality and Fracture Among Young Adults With Cerebral Palsy by Daniel G. Whitney, Edward A. Hurvitz

“…ABSTRACT Background There is a dearth of screening tools for cardiorespiratory disease and fracture risk, such as risk prediction models, for adults with cerebral palsy (CP). …”
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Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures by Yiqun Chen, Mingxuan Ma, Dandan Qu, Chunxiang Xu

“…XGBoost and Transformer models have better performance (AUC 0.866 VS 0.946, F1 0.807 VS 0.889), and both models have better substantial prediction ability for the occurrence of postoperative pneumonia. …”
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Enhancing Engineering and STEM Education With Vision and Multimodal Large Language Models to Predict Student Attention by Luis Marquez-Carpintero, Diego Viejo, Miguel Cazorla

Subjects: “…Attention prediction…”
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Prognostic models for survival predictions in advanced cancer patients: a systematic review and meta-analysis by Mong Yung Fung, Yuen Lung Wong, Ka Man Cheung, King Hei Kelvin Bao, Winnie Wing Yan Sung

“…Prediction model study Risk Of Bias Assessment Tool (PROBAST) was adopted for risk of bias assessment. …”
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Machine learning approach for solar irradiance estimation on tilted surfaces in comparison with sky models prediction by Mbah O. M., Madueke C. I., Umunakwe R., Okafor C. O.

“…The measured global horizontal solar radiation and the time and day number were used as input for the prediction process. Python computational software was used for model prediction, and the performance of each model was assessed using statistical methods such as mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE) (RMSE). …”
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Predicting the Energy Consumption in Chillers: A Comparative Study of Supervised Machine Learning Regression Models by Mohamed Salah Benkhalfallah, Sofia Kouah, Saad Harous

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From Pixels to Prognosis: Artificial Intelligence and Machine Learning Models in Brain Tumour Mutation Prediction by Quratulain Tariq, Eisha Abid Ali, Saad bin Anis, Irfan Yousaf, Ahmer Nasir Baig, Muhammad Shahzad Shamim

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Predicting mortality in critically ill patients with hypertension using machine learning and deep learning models by Ziyang Zhang, Jiancheng Ye

“…The SHAP analysis revealed that these predictors had a substantial influence on model predictions, underscoring their importance in assessing mortality risk in this patient population.ConclusionDeep learning models, particularly the 1D CNN, demonstrated superior predictive accuracy compared to traditional ML models in predicting mortality among critically ill patients with hypertension. …”
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Investigating the performance of multivariate LSTM models to predict the occurrence of Distributed Denial of Service (DDoS) attack. by Prashant Kumar, Chitra Kushwaha, Dimple Sethi, Debjani Ghosh, Punit Gupta, Ankit Vidyarthi

“…In this paper basically conversed about some deep learning models that will hand over a descent accuracy in prediction of DDoS attacks. …”
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