Estimating Coastal Dyke Leakage Flow Using Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS) Model by Adel Asakereh, Farhad Choobi, mohammad bagherzadeh, reza mirzaee

“…The best support vector machine model is checked using a multivariate adaptive regression spline model (MARS) for prediction. …”
Get full text

Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors by Deepa Tilwani, Deepa Tilwani, Deepa Tilwani, Deepa Tilwani, Christian O'Reilly, Christian O'Reilly, Christian O'Reilly, Christian O'Reilly, Nicholas Riccardi, Valerie L. Shalin, Valerie L. Shalin, Dirk-Bart den Ouden, Julius Fridriksson, Svetlana V. Shinkareva, Svetlana V. Shinkareva, Amit P. Sheth, Amit P. Sheth, Amit P. Sheth, Rutvik H. Desai, Rutvik H. Desai

Get full text

Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…Finally, the integrated prediction combining fluctuation and random terms under condition 5 yielded R2 of 0.87 and 0.93 for the overall prediction at Ankang and Baihe stations, respectively, demonstrating excellent model performance. [Conclusions](1) The MVMD decomposition method can control the number of decomposition layers, ensuring complete signal feature extraction without overfitting while improving processing speed.(2) Pearson correlation coefficient method enhances prediction accuracy through decomposed data classification.(3) The MEA-BP can improve signal-to-noise ratio, adapt to complex environments, enhance learning efficiency and generalization ability, and reduce computational complexity.(4) The GWO-ELM algorithm integrates grey wolf optimizer with extreme learning machine, providing a fast and adaptive solution for time-series prediction with reduced overfitting and improved efficiency.(5) The overall combined model can efficiently and stably process large amount of data while ensuring high accuracy.…”
Get full text

AI enhanced model predictive control for optimizing LPG recovery through integrated computational modeling design of experiments and multivariate regression by Basma Abd El Hakim, Mahmoud Abdel-Halim Abdel-Goad, M. E. Awad, Abeer M. Shoaib

Get full text

Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities by Mahdieh Shokrollahi Barough, Mohammad Darzi, Masoud Yunesian, Danesh Amini Panah, Yekta Ghane, Sam Mottahedan, Sohrab Sakinehpour, Tahereh Kowsarirad, Zahra Hosseini-Farjam, Mohammad Reza Amirzargar, Samaneh Dehghani, Fahimeh Shahriyary, Mohammad Mahdi Kabiri, Marzieh Nojomi, Neda Saraygord-Afshari, Seyedeh Ghazal Mostofi, Zeynab Yassin, Nazanin Mojtabavi

Get full text

Random Ensemble MARS: Model Selection in Multivariate Adaptive Regression Splines Using Random Forest Approach by Mehmet Ali Cengiz, Dilek Sabancı

Subjects: Get full text

Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models by Sughashini Murugesu, Kristofer Linton-Reid, Emily Braun, Jennifer Barcroft, Nina Cooper, Margaret Pikovsky, Alex Novak, Nina Parker, Catriona Stalder, Maya Al-Memar, Srdjan Saso, Eric O. Aboagye, Tom Bourne

Get full text

RNN and GNN based prediction of agricultural prices with multivariate time series and its short-term fluctuations smoothing effect by Youngho Min, Young Rock Kim, YunKyong Hyon, Taeyoung Ha, Sunju Lee, Jinwoo Hyun, Mi Ra Lee

Subjects: “…Multivariate time series…”
Get full text

Prediction of successful weaning from renal replacement therapy in critically ill patients based on machine learning by Qiqiang Liang, Xin Xu, Shuo Ding, Jin Wu, Man Huang

Subjects: Get full text

Thermal Characteristics Testing and Analysis of Dry Gear Hobbing Machine on Multivariate Information Fusion by Peng Shengdi, Yang Xiao, Li Benjie, He Lang, Zhang Zhili

Get full text

Thermal characteristics testing and analysis of dry gear hobbing machines on multivariate information fusion by PENG Shengdi, YANG Xiao, LI Benjie, HE Lang, ZHANG Zhili

Get full text

A novel multivariate decomposition-based hybrid model for interpretable multi-step-ahead daily reference evapotranspiration forecasting by Ali Matoog Obaid Lebawi, Mahnoosh Moghaddasi, Mehdi Mohammadi Ghaleni, Mansour Moradi

“…Study focus: This research study develops a complementary expert system for accurately forecasting reference evapotranspiration (ET0) over one, three, and seven-day horizons by integrating Machine Learning (ML) models with a novel multivariate decomposition technique. …”
Get full text

Statistical modeling and application of machine learning for antibiotic degradation using UV/persulfate-peroxide based advanced oxidation process by Musfekur Rahman Dihan, Md. Ashraful Alam, Surya Akter, Md. Abdul Gafur, Md. Shahinoor Islam

“…Pearson correlation and statistical multivariate linear regression (MLR) were applied to model the removal% and pHfinal of both antibiotics, along with the three machine learning algorithms, Artificial neural network (ANN), support vector machine (SVM), and Random Forest (RF), to make the same predictions. …”
Get full text

Risk prediction and clinical utility analysis of postoperative pancreatic fistula: a comparative study of multivariable logistic regression and random forest models by Kaixuan Zhang, Kunlun Chen

“…ObjectiveCompare the performance of the Multivariable logistic regression (LR) model based on traditional statistical methods and the Random Forest (RF) model in machine learning for predicting clinically relevant postoperative pancreatic fistula (CR-POPF) after pancreatoduodenectomy (PD).BackgroundCR-POPF is a common and severe complication following PD. …”
Get full text

Investigating Stress and Coping Behaviors in African Green Monkeys (<i>Chlorocebus aethiops sabaeus</i>) Through Machine Learning and Multivariate Generalized Linear Mixed Models by Brittany Roman, Christa Gallagher, Amy Beierschmitt, Sarah Hooper

“…The statistical methodology utilized machine learning and multivariate generalized linear mixed models to find associations between behaviors and fluctuations of cortisol, lysozyme, and β-endorphin concentrations. …”
Get full text

Early detection of feline chronic kidney disease via 3-hydroxykynurenine and machine learning by Ellen Vanden Broecke, Laurens Van Mulders, Ellen De Paepe, Dominique Paepe, Sylvie Daminet, Lynn Vanhaecke

Subjects: Get full text

Parameterized quantum circuits as universal generative models for continuous multivariate distributions by Alice Barthe, Michele Grossi, Sofia Vallecorsa, Jordi Tura, Vedran Dunjko

“…Abstract Parameterized quantum circuits are a key component of quantum machine learning models for regression, classification, and generative tasks. …”
Get full text

Hybrid machine learning-enabled multivariate bridge-specific seismic vulnerability and resilience assessment of UHPC bridges by Tadesse G. Wakjira, M. Shahria Alam

“…Thus, this study proposes a hybrid machine learning (ML)-enabled multivariate bridge-specific seismic vulnerability and resilience assessment framework for UHPC bridges. …”
Get full text

Pre-Filtering SCADA Data for Enhanced Machine Learning-Based Multivariate Power Estimation in Wind Turbines by Bubin Wang, Bin Zhou, Denghao Zhu, Mingheng Zou, Haoxuan Luo

“…Finally, the performance of the power estimation model is validated using two wind turbine datasets and two machine learning algorithms, with results compared with and without filtering. …”
Get full text

Multivariate forecasting of dengue infection in Bangladesh: evaluating the influence of data downscaling on machine learning predictive accuracy by Mahadee Al Mobin

“…This study introduces a rigorous multivariate time series analysis, integrating meteorological factors with state-of-the-art machine learning (ML) models, to predict DENV case trends across different temporal scales. …”
Get full text