Physical Health Portrait and Intervention Strategy of College Students Based on Multivariate Cluster Analysis and Machine Learning by Rong Guo, Rou Dong, Ni Lu, Lin Yu, Chaoxian Chen, Yonglin Che, Jiajin Zhang, Jianke Yang

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Machine learning-based mathematical equations for dengue positivity detection using elementary laboratory parameters by Shirin Dasgupta, Shuvankar Das, Debarghya Chakraborty

“…Materials and Methods: As a replacement, two machine learning (ML)-based prediction models, specifically Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN), are utilized to predict dengue infection. …”
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A data-driven approach to forest health assessment through multivariate analysis and machine learning techniques by Raja Waqar Ahmed Khan, Hamayun Shaheen, Muhammad Ejaz Ul Islam Dar, Tariq Habib, Muhammad Manzoor, Syed Waseem Gillani, Abeer Al-Andal, John Oluwafemi Ayoola, Muhammad Waheed

“…K-means clustering was used to group forests into three distinct classes based on ecological characteristics, due to its efficiency in identifying natural patterns within multivariate data. ML models, including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM) were trained and validated using an 80:20 train-test split and 5-fold cross-validation. …”
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Predicting infections with multidrug-resistant organisms (MDROs) in neurocritical care patients with hospital-acquired pneumonia (HAP): development of a novel multivariate predicti... by Lin Yang, Guangyu Lu, Haiqing Diao, Yang Zhang, Zhiyao Wang, Xiaoguang Liu, Qiang Ma, Hailong Yu, Yuping Li

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Advancing in creep index of soil prediction: A groundbreaking machine learning approach with Multivariate Adaptive Regression Splines by Mohammed E. Seno, Husein Ali Zeini, Hamza Imran, Mohammed Noori, Sadiq N. Henedy, Nouby M. Ghazaly

“…Finally, the model's performance was compared to previously developed machine learning models and empirical equations across the entire dataset. …”
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ADTime: Adaptive Multivariate Time Series Forecasting Using LLMs by Jinglei Pei, Yang Zhang, Ting Liu, Jingbin Yang, Qinghua Wu, Kang Qin

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Density matrix emulation of quantum recurrent neural networks for multivariate time series prediction by J D Viqueira, D Faílde, M M Juane, A Gómez, D Mera

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The RICO dataset: A multivariate HVAC indoors and outdoors time-series datasetZenodo by Zachari Thiry, Massimiliano Ruocco, Alessandro Nocente, Odne Andreas Oksavik

“…Acquiring high-quality transitory regime data for training Machine Learning models is challenging due to the scarcity of publicly available dataset. …”
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APPLICATION AND PERFORMANCE COMPARISON OF MULTI-OUTPUT MACHINE LEARNING FOR NUMERICAL-NUMERICAL AND NUMERICAL-CATEGORICAL OUTPUTS by Karin Joan, Robyn Irawan, Benny Yong

“…These benefits will significantly impact cost savings for industries utilizing Big Data. The models used in this research include Multivariate Regression Tree, Multivariate Random Forest, and Multi-Output Neural Network. …”
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A New XGBoost Algorithm Based Prediction Model for Fetal Growth Restriction in Patients with Preeclampsia by Haijuan Li, Sumei Wang, Chunyu Zhan

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A Pipeline for Multivariate Time Series Forecasting of Gas Consumption in Pelletization Process by Thadeu Pezzin Melo, Jefferson Andrade, Karin Satie Komati

“…Although AutoML did not outperform the statistical model in terms of RMSE values, regarding training time, AutoML models were significantly more efficient than the statistical approach, optimizing computational resource usage and enabling faster model adjustments. …”
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Power Assessment and Performance Comparison of Wind Turbines Driven by Multivariate Environmental Factors by Bubin Wang, Bin Zhou, Denghao Zhu, Mingheng Zou, Zhao Rao, Haoxuan Luo, Weihao Ji

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Relationship between the human development index and the behavior of PM2.5 in USA, using multivariate statistics and machine learning by Paul Sanmartin Mendoza, Margarita Castillo Ramirez, Ste`phanie Galvis Castro, Vera Santiago Martinez, Alexander Elias Parody Muñoz

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Outlier Detection and Removal in Multivariate Time Series for a More Robust Machine Learning–based Solar Flare Prediction by Junzhi Wen, Azim Ahmadzadeh, Manolis K. Georgoulis, Viacheslav M. Sadykov, Rafal A. Angryk

“…Furthermore, we explore a novel approach by treating our outliers as if they belong to flaring-class data in the training phase of our machine learning, resulting in further enhancements to our models’ performance.…”
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1-Hexadecyl-3-methylimidazolium tetrachloroindate ionic liquid as corrosion inhibitor for mild steel: Insight from experimental, computational, multivariate statistics and multi-quadratic regression based machine learning model by Ndidiamaka Martina Amadi, Joseph Okechukwu Ezeugo, Chukwunonso Chukwuzuluoke Okoye, John Ifeanyi Obibuenyi, Maduabuchi Arinzechukwu Chidiebere, Dominic Okechukwu Onukwuli, Valentine Chikaodili Anadebe

“…Various advanced methods were employed in this research, such as potentiodynamic polarization (PDP), quantum chemical computations, molecular dynamics simulations, weight loss assessments, electrochemical impedance spectroscopy (EIS) and multivariate statistics via machine learning models. …”
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Application of laser light backscattering for qualitative and quantitative assessment of dilution of clear and cloudy apple juices by Hoa Xuan Mac, Nga Thi Thanh Ha, László Friedrich, Lien Le Phuong Nguyen, László Baranyai

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Data-Adaptive Dynamic Time Warping-Based Multivariate Time Series Fuzzy Clustering by Qinglin Cai, Leiying Chen, Jian Shao, Ling Chen

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Machine learning−derived multivariable predictors of postcardiotomy cardiogenic shock in high-risk cardiac surgery patientsCentral MessagePerspective by Edward G. Soltesz, MD, MPH, Randi J. Parks, PhD, Elise M. Jortberg, MS, Eugene H. Blackstone, MD

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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

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Automated Detection of Poor-Quality Scintigraphic Images Using Machine Learning by Anil K. Pandey, Akshima Sharma, Param D. Sharma, Chandra S. Bal, Rakesh Kumar

“…Conclusion Machine learning algorithms can be used to classify poor- and good-quality images with good accuracy (86.88%) using 32 PCs as the feature vector and MARS as the classification model.…”
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