An Edge Computing-Based and Threat Behavior-Aware Smart Prioritization Framework for Cybersecurity Intrusion Detection and Prevention of IEDs in Smart Grids With Integration of Mod... by Abdulmohsen Algarni, Zulfiqar Ahmad, Mohammed Alaa Ala'Anzy

“…We implemented the benchmark machine-learning models, i.e., Gradient Boosting Machine and Support Vector Machine, for performance comparison with the proposed modified machine-learning models. …”
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Graph-Based COVID-19 Detection Using Conditional Generative Adversarial Network by Imran Ihsan, Azhar Imran, Tahir Sher, Mahmood Basil A. Al-Rawi, Mohammed A. Elmeligy, Muhammad Salman Pathan

“…These reconstructed features serve as input to a classification module, comprising a multi-layer neural network, GCN, adept at processing graph-structured data, alongside conventional machine learning classifiers such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), facilitating categorization of chest X-ray images into COVID-19, pneumonia, and normal cases. …”
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Proposed Comprehensive Methodology Integrated with Explainable Artificial Intelligence for Prediction of Possible Biomarkers in Metabolomics Panel of Plasma Samples for Breast Canc... by Cemil Colak, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…Plasma metabolites were examined using LC-TOFMS and GC-TOFMS techniques. Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Random Forest (RF) were evaluated using performance metrics such as Receiver Operating Characteristic-Area Under the Curve (ROC AUC), accuracy, sensitivity, specificity, and F1 score. …”
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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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A Machine Learning Framework for Student Retention Policy Development: A Case Study by Sidika Hoca, Nazife Dimililer

“…The experimental results indicated that Categorical Boosting with an F1-score of 82% is the most effective classifier for the dataset. …”
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AI-Based Prediction of Warpage in Organic Substrates by Jingyi Zhao, Meiying Su, Rui Ma

“…Utilizing this dataset, the network architectures and hyperparameters of Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM) algorithms were optimized, and their performance was evaluated in terms of loss convergence, learning rate adaptability, training efficiency, and robustness. …”
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Drivers of the Integration of Virtual Reality into Construction Safety Training in Ghana by Hutton Addy, Clinton Aigbavboa, Simon Ofori Ametepey, Rexford Henaku Aboagye, Wellington Didibhuku Thwala

“…Technological advancement and boosting safety culture are the two highest drivers the research recommends. …”
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RSTHFS: A Rough Set Theory-Based Hybrid Feature Selection Method for Phishing Website Classification by Jahanggir Hossain Setu, Nabarun Halder, Ashraful Islam, M. Ashraful Amin

“…Performance was further assessed using three advanced classifiers: Light Gradient-Boosting Machine (LightGBM), Random Forest (RF), and Categorical Boosting (CatBoost), with CatBoost emerging as the most efficient, achieving the highest accuracy. …”
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Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence by Simona Moldovanu, Dan Munteanu, Keka C. Biswas, Luminita Moraru

“…ML classifiers such as Random Forest (RF), Extreme Gradient Boosting (XGB), Gradient Boosting Classifiers (GBC), and LASSO regression were trained with both customized feature classes. …”
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A 28 GHz Phased-Array Transmitter Based on Doherty Spatial Combining Technique With a Local Sub-Sampling PLL by Itamar Melamed, Avraham Sayag, Emanuel Cohen

“…This paper presents a 28 GHz integrated phased-array transmitter, utilizing an over-the-air (OTA) combining technique for power efficiency boosting and a local oscillator (LO) phase shifting. …”
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Constructing a predictive model of negative academic emotions in high school students based on machine learning methods by Shumeng Ma, Ning Jia, Xiuchao Wei, Wanyi Zhang

“…We applied various machine learning models, such as logistic regression, naive Bayes, support vector machine, decision tree, random forest, gradient boosting decision tree, and adaptive boosting, to analyze the students’ negative academic emotions. …”
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Optimized Breast Cancer Classification Using PCA-LASSO Feature Selection and Ensemble Learning Strategies With Optuna Optimization by Prabhat Kumar Sahu, Taiyaba Fatma

“…Additionally, feature importance scores for Random Forest and Gradient Boosting provide insights into the most influential factors in the classification process. …”
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Development of an AI-Based Image Analysis Model for Verifying Partial Defects in Nuclear Fuel Assemblies by Seulah Kim, Dayun Park, Hyung-Joo Choi, Chulhee Min, Jaejoon Ahn

“…By using emission tomography image data acquired from 3 × 3 nuclear fuel assemblies, we compare the performance of neural network models (AlexNet, ResNet, and the squeeze-and-excitation network (SENet)) and tree-based ensemble models (extreme gradient boosting (XGBoost), random forest model, and light gradient boosting machine (LightGBM)). …”
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Enhancing breast cancer prediction through stacking ensemble and deep learning integration by Fatih Gurcan

“…To achieve this, the efficacy of ensemble methods such as Random Forest, XGBoost, LightGBM, ExtraTrees, HistGradientBoosting, AdaBoost, GradientBoosting, and CatBoost in modeling breast cancer diagnosis was comprehensively evaluated. …”
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Pyrolysis Kinetics of Pine Waste Based on Ensemble Learning by Alok Dhaundiyal, Laszlo Toth

“…The TG model obtained through the boosting technique provided the best fitting for the experimental dataset of raw pine cone, with the root squared error varying from ±1.82 × 10⁻³ to ±1.84 × 10⁻³, whereas it was in the range of ±1.78 × 10⁻³ to ±1.83 × 10⁻³ for processed pine cone. …”
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An Approach to Truck Driving Risk Identification: A Machine Learning Method Based on Optuna Optimization by Zhaofei Wang, Hao Li, Qiuping Wang

“…Second, the truck driving risk was quantified into three categories of low level, medium level, and high level risk, and the unbalanced data were processed using a hybrid sampling algorithm. Finally, the tree-based decision tree (DT) model, random forest (RF) model, Light Gradient Boosting Machine (LightGBM) model and eXtreme Gradient Boosting (XGBoost) model were selected for training and Optuna was used for hyperparameter optimization of the model. …”
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The application of risk models based on machine learning to predict endometriosis‐associated ovarian cancer in patients with endometriosis by Xiaopei Chao, Shu Wang, Jinghe Lang, Jinhua Leng, Qingbo Fan

“…We extracted a total of 94 demographic and clinicopathologic features from the medical records using natural language processing. We used a machine learning method – gradient‐boosting decision tree – to construct a predictive model for EAOC and to evaluate the accuracy of the model. …”
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A new approach for monitoring spatial and temporal changes in forest types in subtropical regions with sample migration and multi-source remote sensing data by Pengfei Zheng, Dongyang Han, Jiang Liu, Bin Xu, Panfei Fang, Shaodong Huang, Wendou Liu, Shaozhi Chen

“…We propose a novel workflow for processing ground survey data to generate stable reference samples, which are then used with a transfer learning approach to construct a multi-year sample library. …”
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Combined influence of crushed brick powder and recycled concrete aggregate on the mechanical, durability and microstructural properties of eco-concrete: An experimental and machine... by Md. Habibur Rahman Sobuz, Mahmudur Hossain Khan, Md. Rakibul Islam, Md. Kawsarul Islam Kabbo, Abdullah Alzlfawi, M Jameel, Md. Munir Hayet Khan

“…Additionally, the study evaluates machine learning algorithms such as extreme gradient boosting (XG Boost), random forest (RF), and bagging model (BAG) for predicting the mechanical strength of concrete specimens. …”
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Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale by Harsh Vazirani, Xiaofeng Wu, Anurag Srivastava, Debajyoti Dhar, Divyansh Pathak

“…The models evaluated included XGBoost Regression, LightGBM, Gradient Boosting Regression (GBR), Random Forest Regression, Decision Tree Regression, and a Multilayer Perceptron (MLP) model. …”
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