Ensemble boosting-based soft-computing models for predicting the bond strength between steel and CFRP plate by Irwan Afriadi, Chanachai Thongchom, Divesh Ranjan Kumar, Suraparb Keawsawasvong, Warit Wipulanusat

“…For the machine learning boosting-based model approach, eight total input variables and one output variable were chosen to predict the maximum load (PU) of the bonding behavior between the CFRP and steel. …”
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Gradient Boosting-Based Simultaneous Classification and Regression Approach by Rawabi Alwanin, Ouiem Bchir, Mohamed Maher Ben Ismail

“…It also evidenced better performance relative to conventional machine learning models.…”
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Analyzing the severity of distracted driving crashes on horizontal curves of rural highways: A hybrid approach of boosting-based ensemble machine learning and discrete choice metho... by Morteza Safari, Meysam Effati, Mahyar Arabani

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Automated Design Method Based on Boosting Algorithms for Improving the Radiation Performance of Microstrip Antenna Arrays by Sina Hasibi Taheri, Ali Lalbakhsh, Amirhassan Zareanborji, Slawomir Koziel

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Enhanced data-driven shear strength predictive modeling framework for RCDBs using explainable boosting-based ensemble learning algorithms coupled with Bayesian optimization by Imad Shakir Abbood, Noorhazlinda Abd Rahman, B.H. Abu Bakar

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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

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ARTEMIX: A community-boosting-based framework for airdrop hunter detection in the Web3 community by Yuyang Qin, Tengfei Ma, Hongzhou Chen, Haihan Duan

“…We introduce ARTEMIX, a community-boosting-based framework that integrates custom-engineered features and community detection techniques to identify airdrop hunters in NFT transactions. …”
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Gradient boosting-based classification of interactome hub genes in periimplantitis with periodontitis – an integrated bioinformatic approach by Pradeep Kumar Yadalam, Sarvagya Sharma, Prabhu Manickam Natarajan, Carlos M. Ardila

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Disaggregating IMERG satellite precipitation over Czech Republic: an innovative approach using hybrid Extreme Gradient Boosting based on Fuzzy Spatial-Temporal Multivariate Cluster... by Ujjwal Singh, Sadaf Nasreen, Gaurav Tripathi, Pragya Mehrishi, Rajani Kumar Pradhan, Poppová Bestakova, Vivek Vikram Singh, K C Gouda, Laxmi Kant Sharma, Kiran Jalem, Petr Maca, Rama Rao Nidamanuri, Akhilesh Singh Raghubanshi, Yannis Markonis, Rakovec Oldřich, Martin Hanel

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Explainable artificial intelligence-machine learning models to estimate overall scores in tertiary preparatory general science course by Sujan Ghimire, Shahab Abdulla, Lionel P. Joseph, Salvin Prasad, Angela Murphy, Aruna Devi, Prabal Datta Barua, Ravinesh C. Deo, Rajendra Acharya, Zaher Mundher Yaseen

“…Neural Network-based, Tree-Based, Ensemble-Based, and Boosting-based methods are evaluated against the hybrid TPE-optimised SVR model for forecasting final examination grades among 492 students enrolled in the TPP7155 (General Science) course at the University of Southern Queensland, Australia, during the 2020-2021 academic year. …”
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An improved machine-learning model for lightning-ignited wildfire prediction in Texas by Qi Zhang, Cong Gao, Chunming Shi

“…Using this dataset, we developed an eXtreme gradient boosting-based machine learning model that integrates meteorological, soil, vegetative, lightning, topographic, and human activity variables to predict LIW probability. …”
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Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study by Han Yang, Mingchen Li, Huixue Zhou, Yongkang Xiao, Qian Fang, Shuang Zhou, Rui Zhang

“…We introduced the LLM-Synergy framework, consisting of two ensemble methods: (1) a Boosting-based Weighted Majority Vote ensemble, refining decision-making by adaptively weighting each LLM and (2) a Cluster-based Dynamic Model Selection ensemble, dynamically selecting optimal LLMs for each query based on question-context embeddings and clustering. …”
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Early Detection of Elevated Ketone Bodies in Type 1 Diabetes Using Insulin and Glucose Dynamics Across Age Groups: Model Development Study by Simon Cichosz, Clara Bender

“…Features were derived from CGM, insulin delivery data, and self-monitoring of blood glucose to develop an extreme gradient boosting-based prediction model. A total of 259 participants aged 6-79 years with over 49,000 days of full-time monitoring were included in the study. …”
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A Large-Scale Empirical Study of Aligned Time Series Forecasting by Polina Pilyugina, Svetlana Medvedeva, Kirill Mosievich, Ilya Trofimov, Alina Kostromina, Dmitry Simakov, Evgeny Burnaev

“…From our large-scale empirical study, we draw the following main conclusions: boosting-based methods, which are often overlooked, have a strong performance; the global modeling approach is promising because it provides competitive performance with a small computational cost; meta-learning via portfolio selection performs better than one based on meta-features. …”
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Post-TACE ALBI-Score Trajectory in Intermediate and Advanced Hepatocellular Carcinoma: Prognostic Implications and Influencing Factors Analysis by Li J, Feng T, Cui C, Wang H, Su T, Jin L, Zhao X, Xiao W

“…Clinical outcomes and patient characteristics were compared across trajectory groups. A CatBoost-based clinical prediction model was developed to identify factors influencing ALBI-score trajectories, with Shapley Additive Explanations (SHAP) values providing feature importance interpretation.Results: Among 501 patients, three ALBI-score trajectories were identified: improve, stable, and decline. …”
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Predicting cancer risk using machine learning on lifestyle and genetic data by Mohamed Abdelmoaty Ahmed, Ahmed AbdelMoety, Asmaa Mohamed Ahmed Soliman

“…The findings highlight the effectiveness of boosting-based ensemble models in capturing complex interactions within health data and support their potential use in personalized cancer risk assessment. …”
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AdapTree: Data-Driven Approach to Assessing Plant Stress Through the AI-Sensor Synergy by Divisha Garg, Harpreet Singh, Yosi Shacham-Diamand

“…The key task addressed was the prediction of stress-related parameters using machine learning. A novel boosting-based ensemble method, AdapTree, combining AdaBoost and decision trees, was proposed to improve predictive accuracy and model interpretability. …”
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Domain Adaptation for Pedestrian Detection Based on Prediction Consistency by Yu Li-ping, Tang Huan-ling, An Zhi-yong

“…In this paper, we propose a novel domain adaptation model for merging plentiful source domain samples with scared target domain samples to create a scene-specific pedestrian detector that performs as well as rich target domain simples are present. …”
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An Integrated Approach for Emergency Response and Long-Term Prevention for Rainfall-Induced Landslide Clusters by Wenxin Zhao, Yajun Li, Yunfei Huang, Guowei Li, Fukang Ma, Jun Zhang, Mengyu Wang, Yan Zhao, Guan Chen, Xingmin Meng, Fuyun Guo, Dongxia Yue

“…The R.avaflow simulations captured the spatial extent and depositional features of landslides, assisting post-disaster operations. The Gradient Boosting-based susceptibility model achieved an accuracy of 0.870, with 8.0% of the area classified as highly susceptible. …”
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Easy Data Augmentation untuk Data yang Imbalance pada Konsultasi Kesehatan Daring by Anisa Nur Azizah, Misbachul Falach Asy'ari, Ifnu Wisma Dwi Prastya, Diana Purwitasari

“…We also verified the EDA results by measuring coherences of texts before and after augmentation using a topic modeling of Latent Dirichlet Allocation (LDA) to ensure topic consistency. …”
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