Comparison of sample preparation methods for higher heating values in various sugarcane varieties using near-infrared spectroscopy by Kantisa Phoomwarin, Khwantri Saengprachatanarug, Jetsada Posom, Seree Wongpichet, Kittipong Laloon, Arthit Phuphaphud

“…This study developed an efficient system for measuring the energy characteristics of energy canes in breeding programs using near-infrared spectroscopy with the aim of significantly improving the accuracy of selecting high-performing sugarcane clones. A key parameter for evaluating energy potential is the heating value, which is typically determined using bomb calorimetry. …”
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Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

“…The performance of these estimates was evaluated against reference FVC values derived from centimeter-level UAV data. …”
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Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

“…The performance evaluation showed that ML models effectively predicted friction behavior and wear behavior of magnesium-based hybrid composites using tribological test data. …”
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Enhanced slope stability prediction using ensemble machine learning techniques by Devendra Kumar Yadav, Swarup Chattopadhyay, Debi Prasad Tripathy, Pragyan Mishra, Pritiranjan Singh

“…We improved the slope stability prediction models through random cross-validation by selecting seven quantitative parameters based on 125 data points. From a classification model perspective, the best slope prediction accuracy (>90%) was attained by bagging with base classifier Decision Tree (DT), boosting with base classifier Random Forest (RF), and random forest with splitting criterion Gini-index. …”
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Machine-Learning-Based Optimal Feed Rate Determination in Machining: Integrating GA-Calibrated Cutting Force Modeling and Vibration Analysis by Yu-Peng Yeh, Han-Hao Tsai, Jen-Yuan Chang

“…These optimal feed rates are then used to train an Extreme Gradient Boosting (XGBoost) regression model, with Bayesian optimization employed for hyperparameter tuning. …”
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Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…Six machine learning algorithms - Random Forest (RF), AdaBoost, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Tabular Prior-data Fitted Network version 2.0 (TabPFN-V2) - were implemented with five-fold cross-validation to optimize model hyperparameters. …”
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Developing an Integrative Data Intelligence Model for Construction Cost Estimation by Zainab Hasan Ali, Abbas M. Burhan, Murizah Kassim, Zainab Al-Khafaji

“…Statistical indicators and graphical methods were used to evaluate the developed models. Several input predictors were used, and XGBoost highlighted inflation as the most crucial parameter. …”
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Visceral adiposity index as a predictor of metabolic dysfunction-associated steatotic liver disease: a cross-sectional study by Tuo Zhou, Xiang Ding, Linjie Chen, Qianxiong Huang, Linfang He

“…Machine learning models demonstrated robust predictive accuracy, with random forest (AUC=0.869) and gradient boosting machine (AUC=0.868) outperforming non-invasive scores. …”
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Machine Learning-Based Prediction Performance Comparison of Marshall Stability and Flow in Asphalt Mixtures by Muhammad Farhan Zahoor, Arshad Hussain, Afaq Khattak

“…We collected data from published studies in the literature encompassing 732 data points to train and evaluate ML models. Eight key input parameters were considered for modeling. …”
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Evolutionary Game Analysis of Green Technology Innovation Behaviour for Enterprises from the Perspective of Prospect Theory by Guancen Wu, Luqi Deng, Xing Niu

“…This paper first calculates the equilibrium stability and evolutionary stability strategies of the enterprise green technology innovation system and then simulates the effect of subjective gains and losses values and other psychological parameters in the prospect editing and evaluation stage. …”
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A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization by Dan Li, Ernie Che Mid, Shafriza Nisha Basah, Xiaochun Liu, Jian Tang, Hongyan Cui, Huilong Su, Qianliang Xiao, Shiyin Gong

“…However, optimizing the preparation parameters for PSCs is crucial. This study establishes a machine learning model incorporating a crude estimation of property (CEP) strategy to enhance prediction accuracy and precisely control process parameters. …”
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Separation of organic molecules from water by design of membrane using mass transfer model analysis and computational machine learning by Suranjana V. Mayani, Hessan Mohammad, Soumya V. Menon, Rishabh Thakur, Abdulqader Faris Abdulqader, S. Supriya, Prabhat Kumar Sahu, Kamal Kant Joshi

“…Utilizing a dataset of over 25,000 data points with r(m) and z(m) as inputs, four tree-based learning algorithms were employed: Decision Tree (DT), Extremely Randomized Trees (ET), Random Forest (RF), and Histogram-based Gradient Boosting Regression (HBGB). Hyper-parameter optimization was conducted using Successive Halving, a method aimed at efficiently allocating computational resources to optimize model performance. …”
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DSR-YOLO: A lightweight and efficient YOLOv8 model for enhanced pedestrian detection by Mustapha Oussouaddi, Omar Bouazizi, Aimad El mourabit, Zine el Abidine Alaoui Ismaili, Yassine Attaoui, Mohamed Chentouf

“…The WIoUv3 loss function was utilized to reduce the regression loss associated with bounding boxes, further boosting the performance. Evaluated on the CityPersons dataset, DSR-YOLO outperformed YOLOv8n with a 14.9 % increase in mAP@50 and 6.3 % increase in mAP@50:95, while maintaining competitive FLOPS, parameter counts, and inference speed.…”
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Comparative Analysis of Machine Learning Algorithms for Potential Evapotranspiration Estimation Using Limited Data at a High-Altitude Mediterranean Forest by Stefanos Stefanidis, Konstantinos Ioannou, Nikolaos Proutsos, Ilias Karmiris, Panagiotis Stefanidis

“…Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. …”
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Machine learning frameworks to accurately estimate the adsorption of organic materials onto resin and biochar by Raouf Hassan, Mohammad Reza Kazemi

“…Various machine learning methods were evaluated, including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Support Vector Regression (SVR), k-Nearest Neighbors (KNN), Decision Trees, Random Forests, Gradient Boosting Machines, Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Gaussian Processes, as well as ensemble algorithms such as XGBoost, LightGBM, and CatBoost. …”
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Optimizing protein-ligand docking through machine learning: algorithm selection with AutoDock Vina by Ala’ Omar Hasan Zayed

“…However, its application in virtual screening across diverse molecular structures presents challenges, particularly in optimizing search parameters. To address these limitations, we developed a machine learning (ML) framework that automates the selection of optimal docking parameters. …”
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An Explainable Machine Learning-Based Prediction of Backbone Curves for Reduced Beam Section Connections Under Cyclic Loading by Emrah Tasdemir, Mustafa Yavuz Cetinkaya, Furkan Uysal, Samer El-Zahab

“…Additionally, Shapley values from XAI are employed to evaluate the influence of input parameters on model predictions. …”
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From data to decisions: Leveraging ML for improved river discharge forecasting in Bangladesh by Md. Abu Saleh, H.M. Rasel, Briti Ray

“…The forecast was performed from 2021 to 2030. 11 statistical parameters were considered for performance evaluation. …”
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Machine learning approaches for predicting the structural number of flexible pavements based on subgrade soil properties by Asadullah Ziar

“…Four algorithms were evaluated, including random forest, extreme gradient boosting, gradient boosting, and K nearest neighbors. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The six classical machine algorithms including Classified Regression Tree (CART), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Stepwise Regression (STR) and Gradient Boosting Machine (GBM) were used for training. The algorithms (RF and STR) with the smallest Mean Absolute Error (MAE) and the highest residual error (RMSE) and the highest correlation coefficient (RP2) were selected for further parameter optimization and evaluation. …”
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