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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Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…Among the tested algorithms, including bagging, gradient boosting, and AdaBoost, the bagging model achieved the highest accuracy (R 2 = 0.975). …”
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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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Efficient Machine Learning for Prediction of Malicious URLs under Neutrosophic Uncertainty Framework by Mohamed Eassa, Ahmed Abdelhafeez, Ahmed A. Metwaly, Ahmed S. Salama

“…Hence, we use different machine learning (ML) models such as decision tree, AdaBoosting, Naïve Bayes, random forest, gradient boosting, and XGBoosting. …”
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Privacy-Preserving Diabetes and Heart Disease Prediction via Federated Learning and WCO by Sachikanta Dash, Sasmita Padhy, Preetam Suman, Sandip Mal, Lokesh Malviya, Amrit Suman, Jaydeep Kishore

“…In Centralized Learning, AdaBoost with WCO achieves an accuracy of 95.32% when tested on a Kaggle dataset consisting of 96,146 instances. …”
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An ensemble strategy for piRNA identification through hybrid moment-based feature modeling by Mansoor Ahmed Rasheed, Tamim Alkhalifah, Fahad Alturise, Yaser Daanial Khan

“…The Boosting approach involved the use of XGBoost (XGB), AdaBoost, and Gradient Boost. For the Stacking method, base learners such as k-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Decision Trees were employed, with a Neural Network (NN) serving as the meta-learner. …”
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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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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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Enhanced Neutrosophic Set and Machine Learning Approach for Breast Cancer Prediction by Ashika T, Hannah Grace, Nivetha Martin, Florentin Smarandache

“…When trained on the N-dataset instead of traditional datasets, ML algorithms such as Decision Tree (DT), Random Forest (RF), and Adaptive Boosting (AdaBoost) perform better. Notably, N-AdaBoost models achieve outstanding results with 99.12% accuracy and 100% precision, highlighting the efficacy of NS in enhancing diagnostic reliability.…”
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CLASSIFYING LIVER DISEASE WITH BOOSTING MACHINE LEARNING APPROACHES by Erol Özçekiç, Ümit Yılmaz

“…This research examines the application of advanced machine learning techniques such as Gradient Boosting, AdaBoost, XGBoost and CatBoost for classification of liver diseases using a publicly available dataset of 1700 clinical records. …”
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Extended length of stay in open versus minimally invasive surgery with robotic-assisted sub-analysis for spinal nerve sheath tumor resection: a nationwide analysis by Taha Khalilullah, Abdul Karim Ghaith, Xinlan Yang, Shaan Bhandarkar, Linda Tang, Yuanxuan Xia, Richard Crawford, Tej Azad, Jawad Khalifeh, A. Karim Ahmed, Nicholas Theodore, Daniel Lubelski

“…Gradient Boosting had the highest predictive performance among machine learning models (AUC: 0.594), followed by AdaBoost and logistic regression. SHAP analysis identified surgical approach, comorbidity score, tumor size, and behavior as the most influential features on extended LOS. …”
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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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EXCHANGE RATE DYNAMICS AND FORECASTING ACCURACY IN EMERGINGECONOMIES: INTEGRATING ENSEMBLE LEARNING MODELS WITH STRUCTURAL TIME SERIES DECOMPOSITION FOR THE USD/ZAR RATE by Agbeyinka Yinka Ibrahim

“…Among the models tested, AdaBoost outperformed Random Forest and K-Nearest Neighbors in terms of forecast accuracy, as measured by RMSE and MAE, despite lowexplanatorypower across all models. …”
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CT Radiomics-based machine learning approach for the invasiveness of pulmonary ground-glass nodules prediction by Rui Chen, Hu Zhang, Xingwen Huang, Haitao Han, Jinbo Jian

“…Then seven machine learning models—logistic regression (LR), support vector machine (SVM), random forest (RF), extra trees, XGBoost, GradientBoosting, and AdaBoost—were constructed. Model performance and prediction efficacy were evaluated based on indicators such as area under the curve (AUC), accuracy, specificity, and sensitivity using receiver operating characteristic (ROC) curves. …”
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Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…A range of sophisticated artificial intelligence methods, including One-Dimensional Convolutional Neural Network (1D-CNN), Artificial Neural Networks (ANN), Decision Tree (DT), Ensemble Learning (EL), Adaptive Boosting (AdaBoost), Random Forest (RF), and Least Squares Support Vector Machine (LSSVM), were utilized to model and predict pH variations with high accuracy. …”
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