Predicting tensile and fracture parameters in polypropylene-based nanocomposites using machine learning with sensitivity analysis and feature impact evaluation by Pouya Rajaee, Faramarz Ashenai Ghasemi, Amir Hossein Rabiee, Mohammad Fasihi, Behnam Kakeh, Alireza Sadeghi

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TEXT CLASSIFICATION USING ADAPTIVE BOOSTING ALGORITHM WITH OPTIMIZATION OF PARAMETERS TUNING ON CABLE NEWS NETWORK (CNN) ARTICLES by Dewi Retno Sari Saputro, Krisna Sidiq, Harun Al Rasyid, Sutanto Sutanto

“…Therefore, this study implements the AdaBoost algorithm with parameter tuning on CNN news article classification. …”
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The Role of Nano-Fertilizers in Sustainable Agriculture: Boosting Crop Yields and Enhancing Quality by Mcholomah Annalisa Kekeli, Quanlong Wang, Yukui Rui

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Boosting vapor flux in osmotic distillation: A comprehensive evaluation of operating conditions and membrane properties by Longxi Zhou, Di He, Zhangxin Wang, Yuanmiaoliang Chen

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Evaluating reservoir permeability from core data: Leveraging boosting techniques and ANN for heterogeneous reservoirs by Amad Hussen, Tanveer Alam Munshi, Minhaz Chowdhury, Labiba Nusrat Jahan, Abu Bakker Siddique, Mahamudul Hashan

“…These approaches include hybrid stacking and three boosting techniques: AdaBoost, gradient boosting, and extreme gradient boosting (XGB). …”
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Optimasi Model Extreme Gradient Boosting Dalam Upaya Penentuan Tingkat Risiko Pada Ibu Hamil Berbasis Bayesian Optimization (BOXGB) by Edi Jaya Kusuma, Ririn Nurmandhani, Lenci Aryani, Ika Pantiawati, Guruh Fajar Shidik

“…Pada penelitian ini diusulkan metode optimasi berbasis Bayesian untuk mengoptimalisasikan hyper-parameter dari model Decision Tree (DT) dan Extreme Gradient Boosting (XGB). …”
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Evaluating structural safety of trusses using Machine Learning by Tran-Hieu Nguyen, Anh-Tuan Vu

“…Overall, the Adaptive Boosting model outperforms the remaining models. In addition, an investigation is carried out to show the influence of the parameters on the performance of the Adaptive Boosting model…”
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Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction by Osahon Idemudia, Jacob Odeh Ehiorobo, Christopher Osadolor Izinyon, Idowu Ilaboya

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Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction by Osahon Idemudia, Jacob Odeh Ehiorobo, Christopher Osadolor Izinyon, Idowu Ilaboya

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State of Health Estimation for Lithium-Ion Batteries Using an Explainable XGBoost Model with Parameter Optimization by Zhenghao Xiao, Bo Jiang, Jiangong Zhu, Xuezhe Wei, Haifeng Dai

“…Then, a SOH estimation method based on the XGBoost algorithm is established, and the model’s hyper-parameters are tuned using the Bayesian optimization algorithm (BOA) to enhance the adaptiveness of the proposed estimation model. …”
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ProBoost: Reducing Uncertainty Using a Boosting Method for Probabilistic Models by Fabio Mendonca, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias, Antonio G. Ravelo-Garcia, Mario A. T. Figueiredo

“…The learners herein considered are standard convolutional neural networks, and the probabilistic models underlying the uncertainty estimation use either variational inference or Monte Carlo dropout. The experimental evaluation carried out on MNIST and CIFAR 10 benchmark datasets shows that ProBoost yields significant performance improvement, compared to not using ProBoost, and outperforms a wider single model with a similar number of parameters.…”
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Comparative Evaluation of Machine Learning Models for Mobile Phone Price Prediction: Assessing Accuracy, Robustness, and Generalization Performance by Saima Anwar Lashari, Muhammad Muntazir Khan, Abdullah Khan, Sana Salahuddin, Muhammad Noman Ata

“…The F1-score, recall, accuracy, and precision of these models were evaluated. According to the findings, the results indicated that LR, with its use of the Elastic Net parameter, outperformed the others with 96% accuracy, 97% precision, 94% recall, and 96% F1-score. …”
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Evaluation of Machine Learning Models for Enhancing Sustainability in Additive Manufacturing by Waqar Shehbaz, Qingjin Peng

“…Subsequently, four ML models, Linear Regression, Decision Trees, Random Forest, and Gradient Boosting, are trained and evaluated. Hyperparameter tuning is conducted using the Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Box constraints (L-BFGS-B) algorithm, which demonstrates the superior computational efficiency compared to traditional approaches such as grid and random search. …”
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Machine learning based prediction of geotechnical parameters affecting slope stability in open-pit iron ore mines in high precipitation zone by John Gladious, Partha Sarathi Paul, Manas Mukhopadhyay

“…The models’ accuracies were evaluated using metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared (R2), Mean Bias Deviation (MBD), and Willmott’s Index of Agreement (d). …”
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Performance evaluation of rock fragmentation prediction based on RF‐BOA, AdaBoost‐BOA, GBoost‐BOA, and ERT‐BOA hybrid models by Junjie Zhao, Diyuan Li, Jian Zhou, Danial J. Armaghani, Aohui Zhou

“…However, accurate prediction of rock fragmentation after blasting is challenging due to the complicated blasting parameters and rock properties. For this reason, optimized by the Bayesian optimization algorithm (BOA), four hybrid machine learning models, including random forest, adaptive boosting, gradient boosting, and extremely randomized trees, were developed in this study. …”
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Real-Time Intelligent Recognition and Precise Drilling in Strongly Heterogeneous Formations Based on Multi-Parameter Logging While Drilling and Drilling Engineering by Aosai Zhao, Yang Yu, Bin Wang, Yewen Liu, Jingyue Liu, Xubiao Fu, Wenhao Zheng, Fei Tian

“…The K-means clustering algorithm is employed to extract the deep geo-engineering characteristics from multi-source LWD data, thereby constructing a lithology label library and categorizing the training and testing datasets. The optimized CatBoost machine learning model is subsequently utilized for lithology classification, enabling real-time and high-precision geological evaluation during directional drilling. …”
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Machine learning-based evaluation of performance of silicon nitride waveguide fabrication: Gradient-boosted forests for predicting propagation and bend excess losses by Hinum-Wagner Jakob Wilhelm, Hoermann Samuel Marko, Feigl Gandolf, Schmidt Christoph, Kraft Jochen, Bergmann Alexander

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Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users by Christopher Meaney, Xuesong Wang, Jun Guan, Therese A. Stukel

“…For each HPO method, we estimated 100 extreme gradient boosting models at different hyper-parameter configurations; and evaluated model performance using an AUC metric on a randomly sampled validation dataset. …”
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Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle by Shing-Hong Liu, Alok Kumar Sharma, Bo-Yan Wu, Xin Zhu, Chun-Ju Chang, Jia-Jung Wang

“…The analysis utilized three machine learning models (Random Forest, CatBoost, XGBoost), evaluated through various evaluation metrics. …”
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Optimizing control strategies for DC-DC boost converters: Real-time application of an adaptive gain scheduled ISA-PI controller with hybrid state-space and linear parameter-varying... by Cağfer Yanarateş

“…This paper introduces an innovative sophisticated control scheme for a DC-DC boost converter (DCBC), employing an adaptive gain scheduled ISA-PI controller. …”
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