Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households by Seok Min Ji, Jeewuan Kim, Kyu Min Kim

“…The classification model was developed using four machine learning algorithms: Random Forest, Gradient boosting, Decision tree, Ridge regression, Neural network, and AdaBoost. …”
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Prediction of Percutaneous Coronary Intervention Success in Patients With Moderate to Severe Coronary Artery Calcification Using Machine Learning Based on Coronary Angiography: Pro... by Zixiang Ye, Zhangyu Lin, Enmin Xie, Chenxi Song, Rui Zhang, Hao-Yu Wang, Shanshan Shi, Lei Feng, Kefei Dou

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Learning Optimal Dynamic Treatment Regime from Observational Clinical Data through Reinforcement Learning by Seyum Abebe, Irene Poli, Roger D. Jones, Debora Slanzi

“…Our study aims to evaluate the performance and feasibility of such algorithms: tree-based reinforcement learning (T-RL), DTR-Causal Tree (DTR-CT), DTR-Causal Forest (DTR-CF), stochastic tree-based reinforcement learning (SL-RL), and Q-learning with Random Forest. …”
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Predicting Livestock Farmers’ Attitudes towards Improved Sheep Breeds in Ahar City through Data Mining Methods by Jabraeil Vahedi, Masoumeh Niazifar, Mohammad Ghahremanzadeh, Akbar Taghizadeh, Soheila Abachi, Valiollah Palangi, Maximilian Lackner

“…Next, we employed data mining-based methods, including multilayer perceptron neural networks, random forest, and random tree algorithms. These helped identify essential variables affecting ranchers’ attitudes. …”
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An artificial intelligence approach to palaeogeographic studies: a case study of the Late Ordovician brachiopods of Laurentia by Akbar Sohrabi

“…Based on the training algorithm and after 146 periods, the training error decreased, but the validation error increased (Fig. 7). …”
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The coverage method of unmanned aerial vehicle mounted base station sensor network based on relative distance by Taifei Zhao, Hua Wang, Qianwen Ma

“…The simulation results show that the coverage of the proposed algorithm is 22.4% higher than that of random deployment, and it is 9.9%, 4.7% and 2.1% higher than similar virtual force-oriented node, circular binary segmentation and hybrid local virtual force algorithms.…”
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Impact of climate change over distribution and potential range of chestnut in the Iberian Peninsula by Pedro Álvarez-Álvarez, Adrián Aviñoa-Arias, Adrián Aviñoa-Arias, Emilio Díaz-Varela, José Vicente López-Bao, José Carlos Pérez-Girón

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Improved mapping of highland bamboo forests using Sentinel-2 time series and machine learning in Google Earth Engine by Dagnew Yebeyen, Binyam Tesfaw Hailu, Worku Zewdie, Temesgen Abera, Gudeta W. Sileshi, Melaku Getachew, Sileshi Nemomissa

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Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments by Leyang Zhao, Weixi Wang, Qizhi He, Li Yan, Xiaoming Li

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Characteristics and Rapid Prediction of Seismic Subsidence of Saturated Seabed Foundation with Interbedded Soft Clay–Sand by Liuyuan Zhao, Miaojun Sun, Jianhong Ye, Fuqin Yang, Kunpeng He

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Investigating the contributory factors influencing speeding behavior among long-haul truck drivers traveling across India: Insights from binary logit and machine learning technique... by Balamurugan Shandhana Rashmi, Sankaran Marisamynathan

“…While conventional statistical methods like binary logit technique lacked prediction capabilities, machine learning (ML) algorithms including decision tree (DT), random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost) were employed to model speeding behavior among LHTDs. …”
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Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms by Ayesha Siddika, Momotaz Begum, Fahmid Al Farid, Jia Uddin, Hezerul Abdul Karim

“…The prediction of software defects is a crucial element in maintaining the stability and reliability of software systems. This research addresses this need by combining advanced techniques (ensemble techniques) with seventeen machine learning algorithms for predicting software defects, categorised into three types: semi-supervised, self-supervised, and supervised. …”
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Improving Attack Detection in IoV with Class Balancing and Feature Selection by Thierry Widyatama, Ifan Rizqa, Fauzi Adi Rafrastara

“…The ensemble algorithms evaluated in this research comprise Random Forest, Gradient Boosting, and XGBoost. …”
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Prediction of Anemia from Multi-Data Attribute Co-Existence by Talal Qadah, Asmaa Munshi

“…Therefore, this study has reevaluated the claims within the domain of detecting and predicting anemia with the best machine learning algorithm. Another research problem, lies with the fact that previous studies on anemia prediction utilized limited machine learning algorithms across a narrow range of datasets, whereas this current study employed numerous machine learning algorithms across a wide range of anemia datasets and tested three hypotheses. …”
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Early Detection of Parkinson's Disease: Ensemble Learning for Improved Diagnosis by Raut Komal, Balpande Vijaya

“…This paper proposed several machine learning algorithms such as Decision Tree, Random Forest, Logistic Regression and Support Vector Machine and design an ensemble of these models to detect and classify Parkinson's disease. …”
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Comparative study on Functional Machine learning and Statistical Methods in Disease detection and Weed Removal for Enhanced Agricultural Yield by Sudha D., Menaga D.

“…The technology has developed to rectify the problems using some machine learning algorithms like Random Forest algorithms, Decision trees, Naïve Bayes, KNN, K-Means clustering, Support vector machines. …”
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AN INTELLIGENT POSTOPERATIVE CHRONIC PAIN PREDICTION SYSTEM (I-POCPP) by Elif Kartal, Fatma Önay Koçoğlu, Zeki Özen, İlkim Ecem Emre, Gürcan Güngör, Pervin Sutaş Bozkurt

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Wavelet-Based ensembled intelligent technique for a better quality of fault detection and classification in AC microgrids by Nityananda Giri, Pravati Nayak, Ranjan Kumar Mallick, Sairam Mishra, Aymen Flah, Habib Kraiem, Lukas Prokop, Mohammad Kanan

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Fault Detection in Photovoltaic Systems Using a Machine Learning Approach by Jossias Zwirtes, Fausto Bastos Libano, Luis Alvaro de Lima Silva, and Edison Pignaton de Freitas

“…The proposed fault detection solutions rely on analyzing different algorithms, including Support Vector Machine, Artificial Neural Network, Random Forest, Decision Tree, and Logistic Regression. …”
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Changes Detection of Mangrove Vegetation Area in Banyak Islands Marine Natural Park, Sumatra, Southeast Asia by Muhammad Arif Nasution, Helmy Akbar, Singgih Afifa Putra, Ammar AL-Farga, Esraa E. Ammar, Yudi Setiawan

“…Spectral index combinations, including NDVI, NDMI, MNDWI, and MVI, were analyzed using random forest classification, a tree-based machine learning algorithm. …”
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