A Robust Multivariate Time Series Classification Approach Based on Topological Data Analysis for Channel Fault Tolerance by Seong-Yeon Jeung, Jang-Woo Kwon

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Integrating Dynamic Red Blood Cell Distribution Width Monitoring and β-Blocker Therapy for Mortality Prediction in Intensive Care Unit Cardiomyopathy Patients: A Bayesian Multivariate Joint Model and Machine Learning Study by Si Chen, Rui Nie, Yi Wang, Haoran Guo, Yan Wang, Haixia Luan, Xiaoli Zeng, Hui Yuan

“…Repeated-measures ANOVA was used to analyze RDW trends and β-blocker associations. A Bayesian multivariate joint model (BMJM) integrated RDW dynamics and β-blocker therapy, incorporating repeated measures and survival outcomes. …”
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Price Forecast of Treasury Bond Market Yield: Optimize Method Based on Deep Learning Model by Weiying Ping, Yuwen Hu, Liangqing Luo

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Designing a Multivariate Belt Conveyor Idler Stall Detection and Identification System with Scalability Analysis by Kyeong Su Shin, Younho Nam, Young-Joo Suh

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Surface-Enhanced Raman Scattering Combined with Machine Learning for Rapid and Sensitive Detection of Anti-SARS-CoV-2 IgG by Thais de Andrade Silva, Gabriel Fernandes Souza dos Santos, Adilson Ribeiro Prado, Daniel Cruz Cavalieri, Arnaldo Gomes Leal Junior, Flávio Garcia Pereira, Camilo A. R. Díaz, Marco Cesar Cunegundes Guimarães, Servio Túlio Alves Cassini, Jairo Pinto de Oliveira

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Characterization and feature selection of volatile metabolites in Yangxian pigmented rice varieties through GC-MS and machine learning algorithms by Kaiqi Cheng, Ruonan Dong, Fei Pan, Wen Su, Lingjie Xi, Meng Zhang, Jingzhang Geng, Ruichang Gao, Ruichang Gao, Wengang Jin, A. M. Abd El-Aty, A. M. Abd El-Aty

“…Based on differential metabolites with great multicollinearity above 0.8 and the chi-square test (20% feature numbers), only 7 metabolites were found to represent the overall metabolites among the several colored rice varieties. Four machine learning models were further used for the classification of various colored rice varieties, and random forest model was the optimum for predicting classification, with an accuracy of 0.97. …”
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Determinant of adoption of agricultural machine renting in West Gojjam zone, Ethiopia by Selam Tilahun, Berhanu Kuma, Amsalu Bedemo

“…To describe and analyze the data descriptive statistics and econometric model were used. The descriptive statistics show among agricultural machine renting households about 25.3% households were rented tractor, 22.3% combine harvester 28.54% maize Sheller and other farmers use two or all three types of machines simultaneously. …”
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DYNAMICS AND MODELING OF TRANSPORT TECHNOLOGICAL MACHINES FOR AGRICULTURE by Viktor P. Zharov

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Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition by Marjan Kordani, Mohsen Bagheritabar, Iman Ahmadianfar, Arvin Samadi-Koucheksaraee

“…The light gradient boosting machine model (LGBM) is also implemented to select the influential input variables. …”
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Vector visibility graph for rare event classification in complex system multivariate time series data by Bo Peng, Shan Gao

“…Experimental results demonstrated that the VVG-GAT framework significantly outperformed traditional models across several metrics. The study further highlights the potential of incorporating VVG-derived network statistics as additional features for machine learning and deep learning models. …”
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Is there a competitive advantage to using multivariate statistical or machine learning methods over the Bross formula in the hdPS framework for bias and variance estimation? by Mohammad Ehsanul Karim, Yang Lei

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Using Artificial Intelligence to Develop a Multivariate Model with a Machine Learning Model to Predict Complications in Mexican Diabetic Patients without Arterial Hypertension (National Nested Case-Control Study): Metformin and Elevated Normal Blood Pressure Are Risk Factors, and Obesity Is Protective by Sergio A. Zaizar-Fregoso, Agustin Lara-Esqueda, Carlos M. Hernández-Suarez, Josuel Delgado-Enciso, Arturo Garcia-Nevares, Luis M. Canseco-Avila, Jose Guzman-Esquivel, Iram P. Rodriguez-Sanchez, Margarita L. Martinez-Fierro, Gabriel Ceja-Espiritu, Hector Ochoa-Díaz-Lopez, Francisco Espinoza-Gomez, Iyari Sanchez-Diaz, Ivan Delgado-Enciso

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Multi-patch attention Transformer for multivariate long-term time series forecasting of TBM excavation parameters by Mingjun Liu, Jianqin Liu, Wei Guo, Hongxu Liu, Xiao Guo

“…To address the research gap in multivariable long-term time series forecasting in the field of tunnel boring machine (TBM) and provide long-term insights for decision-making in TBM construction, this paper studies a novel Transformer-based forecasting model. …”
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Development and evaluation of a multivariable prediction model for clinical improvement in an established cohort of Colombian rheumatoid arthritis patients by Claudia Ibáñez-Antequera, Gabriel-Santiago Rodríguez-Vargas, Fernando Rodríguez-Florido, Pedro Rodríguez-Linares, Adriana Rojas-Villarraga, Pedro Santos-Moreno

“…The Transparent Reporting of a multivariate prediction model for Individual Prognosis or Diagnosis (TRIPOD) guidelines were followed to harmonize this study based on AI. …”
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Unboxing Tree ensembles for interpretability: A hierarchical visualization tool and a multivariate optimal re-built tree by Giulia Di Teodoro, Marta Monaci, Laura Palagi

“…The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. …”
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Nonlinear Multivariate Calibration of Shelf Life of Preserved Eggs (Pidan) by Near Infrared Spectroscopy: Stacked Least Squares Support Vector Machine with Ensemble Preprocessing by Lu Xu, Si-Min Yan, Chen-Bo Cai, Xiao-Ping Yu, Jian-Hui Jiang, Hai-Long Wu, Ru-Qin Yu

“…This paper aims at developing a rapid and nondestructive method for analyzing the shelf life of preserved eggs (pidan) by near infrared (NIR) spectroscopy and nonlinear multivariate calibration. A major concern with a nonlinear model is that the noncomposition-correlated spectral variations among pidan objects of different batches and production dates would unnecessarily increase model complexity and cause overfitting and degradation of prediction. …”
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Estimation of the yield and green water footprint of rainfed wheat based on remote sensing and machine learning by Mojgan Ahmadi, Hadi Ramezani Etedali, Abbass Kaviani, Alireza Tavakoli

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Diagnosis of Stator Inter-Turn Short Circuit Faults in Synchronous Machines Based on SFRA and MTST by Junsheng Ding, Zhongyong Zhao

“…Therefore, this study improves the frequency response analysis by combining it with a deep learning model of a multivariate time series transformer. …”
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MVSAPNet: A Multivariate Data-Driven Method for Detecting Disc Cutter Wear States in Composite Strata Shield Tunneling by Yewei Xiong, Xinwen Gao, Dahua Ye

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ActBeCalf: Accelerometer-based multivariate time-series dataset for calf behavior classificationZenoob - EU Open Research Repository by Oshana Iddi Dissanayake, Sarah E. McPherson, Joseph Allyndrée, Emer Kennedy, Pádraig Cunningham, Lucile Riaboff

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