Relevant biochar characteristics influencing compressive strength of biochar-cement mortars by Julia Hylton, Aaron Hugen, Steven M. Rowland, Michael Griffin, Lori E. Tunstall

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Machine learning-enabled prediction of bone metastasis in esophageal cancer by Liqiang Liu, Wanshi Duan, Tao She, Shouzheng Ma, Haihui Wang, Jiakuan Chen

“…National Institutes of Health from 2010 to 2020. Six machine learning models were constructed: Support Vector Machine, Logistic Regression, Extreme Gradient Boosting, Neural Network, Random Forest, and k-Nearest Neighbors. …”
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Suicide risk prediction for Korean adolescents based on machine learning by Haitao Wang, Han Yuan, Yunong Zhang, Qixuan Wang, Zeng Gao, Mujuan Zhao

“…The predictive performance of six ML models-Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Extremely Randomized Trees (ET), and Distributed Random Forest (DRF)-was systematically compared. …”
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A Hybrid Neutrosophic and Machine Learning Model for Assessing Environmental Literacy in Biodiversity Conservation by Pablo Santiago López Freire, Jocelyn Estefanía Morocho Hidalgo, Leslye Pamela Calderón, Andy Stiwer Jhostin Quiroz

“…This study proposes the hybrid NEAML-BIOPASTAZA (Neutrosophic and Explainable Artificial Learning) model for Biodiversity and Legal-Ecological Assessment in Pastaza, which integrates multivariate statistical analysis, neutrosophic logic, and supervised machine learning to assess the relationship between environmental literacy and the effectiveness of the legal framework for biodiversity conservation in the Pastaza canton. …”
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A machine learning model for hub-height short-term wind speed prediction by Zongwei Zhang, Lianlei Lin, Sheng Gao, Junkai Wang, Hanqing Zhao, Hangyi Yu

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A comparative study of explainable machine learning models with Shapley values for diabetes prediction by Keona Pang

“…This paper aims to study how these features influence diabetes risk. 80 % of the dataset is used for training and 20 % for testing. Six machine learning models, as well as the Multivariate Adaptive Regression Splines (MARS) model, were used in the investigation. …”
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Evaluation of Several Machine Learning Models for Field Canal Improvement Project Cost Prediction by Saadi Shartooh Sharqi, Aayush Bhattarai

“…Due to the massive advancement of soft computing (SC) and Internet of things (IoT), the main research objective of the current study was initiative. Several machine learning (ML) models including extreme learning machine (ELM), multivariate adaptive regression spline (MARS), and partial least square regression (PLS) were adopted to predict field canal cost. …”
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Machine learning gene expression predicting model for ustekinumab response in patients with Crohn's disease by Manrong He, Chao Li, Wanxin Tang, Yingxi Kang, Yongdi Zuo, Yufang Wang

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Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning by Jia Liu, Dan Zhu, Lingzhi Deng, Xiaoliang Chen

“…Early identification of patients at risk remains challenging due to complex, multivariate clinical relationships. Machine learning (ML) methods offer promise for more accurate prognostication. …”
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Estimation of Anthocyanins in Apple Leaves Based on Ground Hyperspectral Imaging and Machine Learning Models by Yu Zhang, Mi Zou, Yanjun Li, Qingrui Chang, Xing Chen, Zhiyong Dai, Weihao Yuan

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Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning by Neng Wang, Shuai Tao, Liang Chen

“…Abstract Objective To develop and validate a novel diagnostic model for detecting bacterial infections in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) using advanced machine learning algorithms. …”
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Crop Classification and Yield Prediction Using Robust Machine Learning Models for Agricultural Sustainability by Abid Badshah, Basem Yousef Alkazemi, Fakhrud Din, Kamal Z. Zamli, Muhammad Haris

“…Secondly, we investigate wheat yield prediction data snagged from the World Bank and Food and Agriculture Organization (FAO), covering the years 1992-2013 for Pakistan. Using Multivariate Imputation by Chained Equations (MICE) to tackle data restrictions, we gauge wheat production for 2014-2024 and forecast the 2025 yield using machine learning regression models. …”
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Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model by Ting Chen, Xuefeng Zhang, Qunfeng Yu, Qin Yang, Lingmin Yuan, Fei Tong

“…ObjectiveTo explore the construction and clinical visualization application of a mortality risk prediction model for sepsis patients based on an improved machine learning model.MethodsThis retrospective study analyzed 1,050 sepsis patients admitted to Longyou County People’s Hospital between January 2010 and August 2023. …”
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Using a Multivariate Virtual Experiment for Uncertainty Evaluation with Unknown Variance by Manuel Marschall, Finn Hughes, Gerd Wübbeler, Gertjan Kok, Marcel van Dijk, Clemens Elster

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Multivariate Screening and Automated Clustering of Macrophage Immunoreactome to Nanoparticles and Photothermal Therapy by Sonia Becharef, Léa Jabbour, Nassima Bekaddour, Giulio Avveduto, Nathalie Luciani, Gautier Laurent, Rana Bazzi, Edouard Alphandery, Stéphane Roux, Amanda K. A. Silva, Kelly Aubertin, Jean‐Philippe Herbeuval, Florence Gazeau

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Solar Flare Prediction Using Multivariate Time Series of Photospheric Magnetic Field Parameters: A Comparative Analysis of Vector, Time Series, and Graph Data Representations by Onur Vural, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

“…The purpose of this study is to provide a comprehensive resource for the selection of data representations for machine learning-oriented models and components in solar flare prediction tasks. …”
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The Quality Prediction of Olive and Sunflower Oils Using NIR Spectroscopy and Chemometrics: A Sustainable Approach by Taha Mehany, José M. González-Sáiz, Consuelo Pizarro

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A comprehensive review on the applications of chemometrics in analytical chemistry by Projesh Saha, Bibhas Pandit, Soma Pramanik, Bhupendra Shrestha

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Retail Demand Forecasting: A Comparative Analysis of Deep Neural Networks and the Proposal of LSTMixer, a Linear Model Extension by Georgios Theodoridis, Athanasios Tsadiras

“…The results indicate that the proposed LSTMixer model is the better predictor, whilst all the other aforementioned models outperform the common statistical and machine learning methods. …”
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Mid-infrared spectra of dried and roasted cocoa (Theobroma cacao L.): A dataset for machine learning-based classification of cocoa varieties and prediction of theobromine and caffe... by Gentil A. Collazos-Escobar, Andrés F. Bahamón-Monje, Nelson Gutiérrez-Guzmán

“…The dataset is organized into Excel sheets and structured according to experimental conditions and replicates, providing a valuable framework for further analysis, model development, and calibration of multivariate statistical models.…”
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