Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation by Pijush Samui, Mohamed A. Shahin

“…This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. …”
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Developing a multivariate model for the prediction of concussion recovery in sportspeople: a machine learning approach by Antonio Belli, Vijay Sawlani, David Davies, Kamal Yakoub, Louise C Yates, Elliot Yates, Xuanxuan Li, Yiping Lu

“…Therefore, determining the appropriate recovery time, without unnecessarily delaying return to sport, is paramount at a professional/semi-professional level, yet notoriously difficult to predict.Objectives To use machine learning to develop a multivariate model for the prediction of concussion recovery in sportspeople.Methods Demographics, injury history, Sport Concussion Assessment Tool fifth edition questionnaire and MRI head reports were collected for sportspeople who suffered mTBI and were referred to a tertiary university hospital in the West Midlands over 3 years. …”
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Multivariable modelling based on statistical and machine learning techniques for monthly precipitation forecasting in the eastern Amazon by Renata Gonçalves Tedeschi, Eduardo Costa de Carvalho, Antonio Vasconcelos Nogueira Neto, Claudia Priscila Wanzeler da Costa, Julio Cezar Goncalves de Freitas, Julio Cezar Goncalves de Freitas, Rafael de Lima Rocha, Rafael de Lima Rocha, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Ewerton Cristhian Lima de Oliveira

“…BackgroundAccurate precipitation forecasting is crucial for various sectors, such as agriculture, hydrology, and disaster management. In recent years, machine learning (ML) techniques have proven invaluable in improving the accuracy of rainfall prediction and identifying the complex relationships between precipitation and other meteorological variables.MethodsThis paper presents acomprehensive analysis of the use of multivariable statistical and ML models to predict monthly rainfall at 13 locations in the eastern Amazon. …”
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Integration of Multivariate Beta-based Hidden Markov Models and Support Vector Machines with Medical Applications by Narges Manouchehri, Nizar Bouguila

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Machine learning for base transceiver stations power failure prediction: A multivariate approach by Sofia Ahmed, Tsegamlak Terefe, Dereje Hailemariam

“…This paper proposes a machine-learning-based framework for preemptive BTS power failure prediction using multivariate time-series data from power and environmental monitoring systems. …”
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A Multivariate LSTM Model for Short-Term Water Demand Forecasting by Aly K. Salem, Ahmed A. Abokifa

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A validated multivariable machine learning model to predict cardio-kidney risk in diabetic kidney disease by James L. Jr. Januzzi, Naveed Sattar, Muthiah Vaduganathan, Craig A. Magaret, Rhonda F. Rhyne, Yuxi Liu, Serge Masson, Javed Butler, Michael K. Hansen

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IoT and machine learning models for multivariate very short‐term time series solar power forecasting by Su Kyi, Attaphongse Taparugssanagorn

“…To achieve accurate very short‐term SI predictions, the authors employ machine learning techniques throughout the forecasting process. …”
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Multivariate Machine Learning Model Based on YOLOv8 for Traffic Flow Prediction in Intelligent Transportation Systems by Fukui Wu, Hanzhong Tan, Linfeng Zhang, Shuangbing Wen, Tao Hu

“…Traffic flow prediction plays a crucial role in Intelligent Transportation Systems (ITS), as it substantially enhances traffic management efficiency, alleviates congestion, and improves road safety. Traditional models often face challenges in addressing the dynamic complexity of modern highway traffic, whereas multivariate machine learning models demonstrate superior predictive accuracy by leveraging diverse data sources. …”
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Early Prediction of Massive Transfusion for Patients With Traumatic Hemorrhage: Development of a Multivariable Machine Learning Model by Andrew J. Benjamin, MD, Andrew J. Young, MD, John B. Holcomb, MD, Erin E. Fox, PhD, Charles E. Wade, PhD, Chris Meador, MBA, Jeremy W. Cannon, MD, SM

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Complex multivariate model predictions for coral diversity with climatic change by Tim R. McClanahan, Maxwell K. Azali, Nyawira A. Muthiga, Sean N. Porter, Michael H. Schleyer, Mireille M. M. Guillaume

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A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations by Thanh Hai Nguyen, Phuong Le, Tuyen Thanh Thi Nguyen, Anh Kim Su

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A multivariate analysis of the early dropout using classical machine learning and local interpretable model-agnostic explanations by Thanh Hai Nguyen, Phuong Le, Tuyen Thanh Thi Nguyen, Anh Kim Su

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A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction by Wu Yan, Fu Yu, Li Tan, Li Mengshan, Xie Xiaojun, Zhou Weihong, Sheng Sheng, Wang Jun, Wu Fu-an

“…Results Here, we proposed a hybrid machine learning model based on graph convolutional neural networks (GCN) and bi-directional long short-term memory (Bi-LSTM) with attention mechanism and multidimensional multivariate feature coding for essential gene prediction, called EGP Hybrid-ML. …”
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Predicting vaginal delivery after labor induction using machine learning: Development of a multivariable prediction model by Iolanda Ferreira, Joana Simões, João Correia, Ana Luísa Areia

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Low-Parameter Critic-Based Multivariate WGAN Model for Clogging Detection in Drives by Surówka Artur Dawid, Kosiba Marcin, Mikkelä Teemu, Kavala Asko, Firla Marcin

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Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models by Anderson Targino da Silva Ferreira, Regina Célia de Oliveira, Maria Carolina Hernandez Ribeiro, Pedro Silva de Freitas Sousa, Lucas de Paula Miranda, Saulo de Oliveira Folharini, Eduardo Siegle

“…Using beach face slope (tanβ) and orientation (Aspect) derived from remote sensing images, calibrated by in situ topographic profiles collected through GNSS positioning, and laboratory analyses, six machine learning models Random Forest, Gradient Boosting, Lasso and Ridge regression, Support Vector Regression, and Partial Least Squares regression were tested and evaluated for performance. …”
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Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies by Nuray Vakitbilir, Abrar Islam, Alwyn Gomez, Kevin Y. Stein, Logan Froese, Tobias Bergmann, Amanjyot Singh Sainbhi, Davis McClarty, Rahul Raj, Frederick A. Zeiler

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QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points by Yunendah Nur Fuadah, Muhammad Adnan Pramudito, Lulu Firdaus, Frederique J. Vanheusden, Ki Moo Lim

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Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat by Frank Gyan Okyere, Daniel Kingsley Cudjoe, Nicolas Virlet, March Castle, Andrew Bernard Riche, Latifa Greche, Fady Mohareb, Daniel Simms, Manal Mhada, Malcolm John Hawkesford

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