CO2 adsorption on NaOH and acid modified montmorillonite: Response surface methodology and machine learning modeling by Pardis Mehrmohammadi, Amir Ahmadvand, Ahad Ghaemi

“…This research underscores the critical role of machine learning in optimizing CO₂ adsorption models, emphasizing its potential to address the complex interactions between operational and modification parameters that traditional methods struggle to capture. …”
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Machine Learning Models for Predicting Abnormal Brain CT Scan Findings in Mild Traumatic Brain Injury Patients by Amirmohammad Toloui, Amir Ghaffari Jolfayi, Hamed Zarei, Arash Ansarian, Amir Azimi, Seyed Mohammad Forouzannia, Rosita Khatamian Oskooi, Gholamreza Faridaalaee, Shayan Roshdi Dizaji, Seyed Ali Forouzannia, Seyedeh Niloufar Rafiei Alavi, Mohammadreza Alizadeh, Hadis Najafimehr, Saeed Safari, Alireza Baratloo, Mostafa Hosseini, Mahmoud Yousefifard

“…This study aimed to develop and optimize machine learning (ML) algorithms to predict abnormal brain computed tomography (CT) scans in patients with mild TBI. …”
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DDNet: A Robust, and Reliable Hybrid Machine Learning Model for Effective Detection of Depression Among University Students by Nasirul Mumenin, Mohammad Abu Yousuf, Madini O. Alassafi, Muhammad Mostafa Monowar, Md. Abdul Hamid

“…The hyperparameter of the used models has been optimized using random search. The optimal configuration has been determined through extensive experimentation with various machine learning (ML) models and settings to ensure high performance. …”
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An Efficient Computational Risk Prediction Model of Heart Diseases Based on Dual-Stage Stacked Machine Learning Approaches by Subhash Mondal, Ranjan Maity, Yachang Omo, Soumadip Ghosh, Amitava Nag

“…This work presents a novel dual-stage stacked machine learning (ML) based computational risk prediction model for cardiac disorders. …”
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Prediction model of ocular metastasis from primary liver cancer: Machine learning‐based development and interpretation study by Jin‐Qi Sun, Shi‐Nan Wu, Zheng‐Lin Mou, Jia‐Yi Wen, Hong Wei, Jie Zou, Qing‐Jian Li, Zhao‐Lin Liu, San Hua Xu, Min Kang, Qian Ling, Hui Huang, Xu Chen, Yi‐Xin Wang, Xu‐Lin Liao, Gang Tan, Yi Shao

“…The purpose of this study was to establish a clinical predictive model of OM in PLC patients based on machine learning (ML). …”
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Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models by Tai Su, Peng Zhang, Peng Zhang, Bingyin Zhang, Zihao Liu, Zexing Xie, Xiaomei Li, Jixiang Ma, Tao Xin

“…Then, we established logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models. …”
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Comparative analysis of Sentinel-2 and PlanetScope imagery for chlorophyll-a prediction using machine learning models by Eden T. Wasehun, Leila Hashemi Beni, Courtney A. Di Vittorio, Christopher M. Zarzar, Kyana R.L. Young

“…Utilizing five machine learning models, namely linear regression (LR), least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR), we constructed inversion models. …”
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Development and validation of machine learning models for distant metastasis of primary hepatic carcinoma: a population-based study by Cong Lu, Ying He, Chun-Ru Chen, Lun Wu, Dan Song, Chen-Hong Wang, Le-Qing Zhang, Jing-Yi Miao, Yong-Bin Zheng, Wei Wang

“…This study aims to identify risk factors associated with distant metastasis and overall survival (OS) in primary liver cancer and to determine the optimal predictive models using machine learning. …”
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Applied AMT machine learning and multi-objective optimization for enhanced performance and reduced environmental impact of sunflower oil biodiesel in compression ignition engine by Ali A. Al-jabiri, Hyder H. Balla, Mudhaffar S. Al-zuhairy, Hussein Alahmer, Ahmed Al-Manea, Raed Al-Rbaihat, Ali Alahmer

“…Additionally, the study employs a multi-objective ameliorative whale optimization algorithm (AWOA) to optimize the model's output. …”
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Energy Trading in Local Energy Markets: A Comprehensive Review of Models, Solution Strategies, and Machine Learning Approaches by Sania Khaskheli, Amjad Anvari-Moghaddam

“…State-of-the-art reinforcement learning algorithms are classified into model-free and model-based methods. This classification examines various algorithms for energy transactions considering the agent type, learning methods, policy, state space, action space, and action selection for state, action, and reward function outputs. …”
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Predicting Remaining Useful Life of Lithium-Ion Batteries for Electric Vehicles Using Machine Learning Regression Models by Sravanthi C L, Dr.J N Chandra sekhar

“…Based on a supervised machine-learning regression approach, this work presents four different regression models like Gradient Boosting Regressor, K-Nearest Neighbor Regressor, Bagging Regressor, and Extra Tree Regressor models to forecast the li-ion battery life for electric vehicles. …”
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Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model by Sivaraja M., Swaminathen A. N., Kuttimarks M. S., Rajprasad J., Sakthivel M., Rex J.

“…In this study, a novel hybrid model combining random forest (RF) regression with a least squares support vector machine (LSSVM) is proposed to enhance the accuracy of ACP predictions. …”
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Identification and validation of a novel machine learning model for predicting severe pelvic endometriosis: A retrospective study by Siqi Cao, Xingzhe Li, Xin Zheng, Jiaxin Zhang, Ziyao Ji, Yanjun Liu

“…Least absolute shrinkage and selection operator (LASSO) was performed to identify the potential risk factors for severe endometriosis. Then, we used seven machine learning (ML) algorithms to construct the predictive models. …”
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Machine learning model for classifying high school students’ academic performance in mathematics amidst the COVID-19 context by Gisella Luisa Elena Maquen-Niño, Moisés Alvin Miguel_Flores, Leysi Yarely Aurich_Mio, Ivan Adrianzén Olano, Percy Edwin De la Cruz Vélez de Villa, Diana Mercedes Castro Cárdenas

“…Therefore, this research aimed to develop a machine learning model for the classification of mathematical academic performance in students of a private Regular Educational Institution in the department of Lambayeque, Peru. …”
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A spatially explicit containment modelling approach for escaped wildfires in a Mediterranean climate using machine learning by Gbenga Lawrence Alawode, Pere Joan Gelabert, Marcos Rodrigues

“…We employed a detailed geospatial approach to assess the spatial-temporal variations in containment probability for escaped wildfires in Catalonia. Using machine learning algorithms, geospatial data, and 124 historical wildfire perimeters from 2000 to 2015, we developed a predictive model with high accuracy (Area Under the Receiver Operating Characteristics Curve = 0.81 ± 0.03) over 32,108 km2 at a 30-meter resolution. …”
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Machine learning (ML) based reduced order modelling (ROM) for linear and non-linear solid and structural mechanics by Mikhael Tannous, Chady Ghnatios, Eivind Fonn, Trond Kvamsdal, Francisco Chinesta

“…This work introduces a minimally intrusive model order reduction technique that employs machine learning within a Proper Orthogonal Decomposition framework to achieve this alliance. …”
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Predicting soil compaction parameters in expansive soils using advanced machine learning models: a comparative study by Sultan Almuaythir, Muhammad Syamsul Imran Zaini, Rida Hameed Lodhi

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Mathematical Modeling as a Aspect for Designing Agricultural Machines and Units (Development History Of Southern Urals Scientific School) by Yu. S. Tsench, E. V. Godlevskaya

“…(Materials and methods) The paper analyses the creation of mathematical models based on field tests of tillage machine under diverse soil and climatic conditions emphasizing the authors’ optimal design and upgrade decisions (Results and discussion) The research reveals that the design process of soil-cultivating machines progressed through such stages as: empirical design, computational experimentation and the use of computer-aided design. …”
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Explainable machine learning models predicting the risk of social isolation in older adults: a prospective cohort study by Mingfei Jiang, Xiaoran Li, YongLu

“…After identifying these predictors, we trained and optimized 7 models to predict the risk of social isolation among older adults: Lightgbm, logistic regression, decision tree, support vector machine, random forest, gradient boosting decision tree (Gbdt), and Xgboost. …”
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Integrating Machine Learning for Early Mortality Prediction in Lung Adenosquamous Carcinoma: A Web-Based Prognostic Model by Min Liang MD, PhD, Xiaocai Li MD, Shangyu Xie MD, Xiaoying Huang MD, Shifan Tan MD

“…Conclusions We present a novel machine learning model, supported by an easily accessible web-based platform, to guide personalized clinical decision-making and optimize treatment strategies for patients with ASC.…”
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