Energy saving and low carbon oriented renovation framework for educational buildings with Tianjin University case study by Xinge Du, Xiang Liu, Feng Gao, Zhihua Zhou

“…The XGBoost model based on Bayesian optimization performed well in performance prediction with an accuracy of 0.86, precision of 0.77, recall of 0.86, and F1 score of 0.816, which is a significant advantage over LGBM, AdaBoost, and Random Forest models. Sensitivity analyses show that parameters such as north-facing window-to-wall ratio, facade and roof thicknesses significantly affect Av.LM and Av.UDI, while roof and facade selection and thicknesses have the greatest impact on GWP. …”
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Developing an explainable machine learning and fog computing-based visual rating scale for the prediction of dementia progression by Zainab H. Ali, Esraa Hassan, Shimaa Elgamal, Nora El-Rashidy

“…To accurately determine health risks, we employ an ensemble AdaBoost model, providing superior performance in accuracy, precision, recall, F-score, and Area Under the Curve (AUC). …”
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Estimation of state of health for lithium-ion batteries using advanced data-driven techniques by Smitanjali Rout, Sudhansu Kumar Samal, Demissie Jobir Gelmecha, Satyasis Mishra

“…Advanced machine learning models, including Adaboost, Xgboost, Ridge Regression, Decision Trees, Random Forests, Artificial Neural Networks, and Long Short-Term Memory Networks (LSTM), are employed to analyze battery performance. …”
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Automated detection and identification of white-backed planthoppers in paddy fields using image processing by Qing YAO, Guo-te CHEN, Zheng WANG, Chao ZHANG, Bao-jun YANG, Jian TANG

“…In the first two detection layers, we used an AdaBoost classifier that was trained on a histogram of oriented gradient (HOG) features and a support vector machine (SVM) classifier that was trained on Gabor and Local Binary Pattern (LBP) features to detect WBPHs and remove impurities. …”
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Adaptive deep SVM for detecting early heart disease among cardiac patients by S. N. Netra, N. N. Srinidhi, E. Naresh

“…The accuracy of the designed framework is 96.07%, which is enhanced than the other existing frameworks like CNN-LSTM, DCNN, Adaboost and SVM, respectively. Thus, the results proved that the developed model can effectively detect heart disease at the early stages and identify the AF rate, providing timely treatments.…”
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Long-term comparative analysis of machine learning models: A deep dive into applications of artificial intelligence for enhancing photovoltaic performance prediction by Ali Akbar Yaghoubi, Mahdi Gandomzadeh, Aslan Gholami, Roghayeh Gavagsaz-Ghoachani, Majid Zandi

“…Although most ensemble methods performed well, Adaboost’s accuracy declined as the number of estimators increased. …”
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Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting by Lili Tang, Liangliang Huang, Yi Yuan

“…Next, it was compared with four representative boosting models, i.e., XGBoost, AdaBoost, CatBoost, and LightGBM, under the above three different cross validations. …”
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The negative linear relationship between oxidative balance scores and constipation: a cross-sectional study from NHANES 2005–2010 by Liqian Xuan, Yang Chen, Chang Liu, Yahui Dai

“…The three machine learning algorithms including Xgboost, Randomforest, and AdaBoost was used to analyze the important component of OBS in constipation.ResultsA total of 8,074 participants were involved. …”
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Development of data driven models to accurately estimate density of fatty acid ethyl esters by Walid Abdelfattah, Munthar Kadhim Abosaoda, Hardik Doshi, H. S. Shreenidhi, Manoranjan Parhi, Devendra Singh, Prabhjot Singh, Abdolali Yarahmadi Kandahari

“…The objective of this study is to construct advanced predictive algorithms using various machine learning methods, including AdaBoost, Decision Trees, KNN, Random Forests, Ensemble Learning, CNN, and SVR. …”
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Predicting Tropical Cyclone Extreme Rainfall in Guangxi, China: An Interpretable Machine Learning Framework Addressing Class Imbalance and Feature Optimization by Yuexing Cai, Cuiyin Huang, Fengqin Zheng, Guangtao Li, Sheng Lai, Liyun Zhu, Qiuyu Zhu

“…The framework integrated three supervised learning algorithms, namely XGBoost, Random Forest, and AdaBoost, along with feature selection techniques and an explainable method. …”
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Dynamic frailty risk prediction in elderly hip replacement: a deep learning approach to personalized rehabilitation by Xujing Lv, Hongmei Li, Yue Li, Ruibing Zhuo, Yiting Yue, Ying Wang, Xiaoyun Zheng, Huanling Gao

“…Seven survival analysis models—Cox-Time, DeepHit, DeepSurv, MP-RSF, MP-AdaBoost, MP-LogitR—were employed to dynamically predict frailty risk over time. …”
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Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review by Williams Kwarah, Frances Baaba da-Costa Vroom, Duah Dwomoh, Samuel Bosomprah

“…Random Forest, XGBoost, and AdaBoost were predominant models (91.7%). Internal validation was performed in all models, but only two models included external validation. …”
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Forecasting Delivery Time of Goods in Supply Chains Using Machine Learning Methods by V. K. Rezvanov, O. M. Romakina, E. V. Zaytseva

“…The following algorithms were used with the cleaned data: Decision tree, Random Forest, k-nearest neighbors, Naïve Bayes, Linear discriminant analysis, XGBoost, CatBoost, LightGBM, AdaBoost, and Perceptron.Results. The basic algorithm for the delivery forecasting model was the Decision Tree algorithm. …”
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Predicting depressive symptoms through social support: a machine learning approach in military populations by Kun-Huang Chen, Pao-Lung Chiu, Ming-Hsuan Chen

“…Five ML classifiers, Random Forest, Decision Tree, Support Vector Machine (SVM), AdaBoost, and k-Nearest Neighbors, were applied to predict depressive symptoms, with model performance evaluated across full and subgroup samples.Results: The Random Forest model achieved the highest area under the precision-recall curve (AUPRC) at 96.3% and consistently outperformed other classifiers across a range of evaluation metrics. …”
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Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms by Rakesh Salakapuri, Panduranga Vital Terlapu, Kishore Raju Kalidindi, Ramesh Naidu Balaka, D. Jayaram, T. Ravikumar

“…It also employs ensemble methods such as Stacking, k-NN, Gradient Boosting, AdaBoost, Multi-Layer Perceptron (MLP), and Support Vector Machines for classification and predicts the BTs using MRI scans. …”
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Using machine learning to predict the probability of incident 2-year depression in older adults with chronic diseases: a retrospective cohort study by Ying Zheng, Taotao Zhang, Shu Yang, Fuzhi Wang, Li Zhang, Yuwen Liu

“…Methods Four ML algorithms (logistic regression [LR], AdaBoost, random forest [RF] and k-nearest neighbor [kNN]) were applied to develop RPMs using the 2011–2015 cohort data. …”
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Enhancing Marshall stability of asphalt concrete using a hybrid deep neural network and ensemble learning by Henok Desalegn Shikur, Ming-Der Yang, Yared Bitew Kebede

“…This study proposes and evaluates hybrid machine learning models, specifically integrating a deep neural network (DNN) base learner with various ensemble techniques (Random Forest, XGBoost, LightGBM, CatBoost, AdaBoost) through stacking, to enhance the accuracy and efficiency of MS prediction. …”
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Machine learning analysis of survival outcomes in breast cancer patients treated with chemotherapy, hormone therapy, surgery, and radiotherapy by Eyachew Misganew Tegaw, Betelhem Bizuneh Asfaw

“…The models assessed blanketed Support Vector Machines (SVM), K-Nearest Neighbor (KNN), AdaBoost, Gradient Boosting, Random Forest, Gaussian Naive Bayes, Logistic Regression, Extreme Gradient Boosting (XG boost), and Decision tree. …”
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Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning by Mohamed Kamel Elshaarawy, Martina Zeleňáková, Asaad M. Armanuos

“…Abstract This study investigates the effectiveness of inclined double cutoff walls installed beneath hydraulic structures by employing five machine learning models: Random Forest (RF), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost). …”
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Predictive modeling of gestational weight gain: a machine learning multiclass classification study by Audêncio Victor, Hellen Geremias dos Santos, Gabriel Ferreira Santos Silva, Fabiano Barcellos Filho, Alexandre de Fátima Cobre, Liania A. Luzia, Patrícia H.C. Rondó, Alexandre Dias Porto Chiavegatto Filho

“…Five machine learning algorithms (Random Forest, LightGBM, AdaBoost, CatBoost, and XGBoost) were employed for model development. …”
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