Enhanced Viral Genome Classification Using Large Language Models by Hemalatha Gunasekaran, Nesaian Reginal Wilfred Blessing, Umar Sathic, Mohammad Shahid Husain

“…Among these are traditional algorithms such as Random Forest (RF), K-nearest neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB), each offering unique advantages in handling genetic data. …”
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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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DREAM(LoAX): Simultaneous Calibration and Diagnosis for Tracer‐Aided Ecohydrological Models Under the Equifinality Thesis by Songjun Wu, Doerthe Tetzlaff, Keith Beven, Chris Soulsby

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Monte Carlo Method for Predicting Educational Service Revenue at Each Level of Education at PT. Kanaka Belajar by Raden Radian Baratasena, Mukidin Mukidin, Kosim Kosim, Adinda Rainah Lova Ariatin

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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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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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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“…Despite the positive findings, only 21% (3/14) of the studies had entered the clinical validation phase, whereas the remaining 79% (11/14) were still in the exploratory phase of research. …”
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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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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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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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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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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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Optimizing ensemble learning for satellite-based multi-hazard monitoring and susceptibility assessment of landslides, land subsidence, floods, and wildfires by Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi-Niaraki, Farman Ali, Biswajeet Pradhan, Soo-Mi Choi

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Elicitation of Norwegian EQ-5D-5L values for hypothetical and experience-based health states based on the EuroQol Valuation Technology (EQ-VT) protocol by Knut Stavem, Tonya Moen Hansen, Ylva Helland, Liv Ariane Augestad, Kim Rand, Andrew Garratt

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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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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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Prediction of copper contamination in soil across EU using spectroscopy and machine learning: Handling class imbalance problem by Chongchong Qi, Nana Zhou, Tao Hu, Mengting Wu, Qiusong Chen, Han Wang, Kejing Zhang, Zhang Lin

“…To address this limitation, we conducted a comprehensive evaluation of three basic machine learning (ML) algorithms and four imbalanced ML algorithms. …”
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Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases by Huai-wen Zhang, Yi-ren Wang, Bo Hu, Bo Song, Zhong-jian Wen, Lei Su, Xiao-man Chen, Xi Wang, Ping Zhou, Xiao-ming Zhong, Hao-wen Pang, You-hua Wang

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Scientific and Engineering Proposals for Improving the Noise Immunity of Receiving Multi-Position Signals in Channels with Variable Parameters by M. R. Bibarsov, S. V. Dvornikov, A. F. Kryachko, A. V. Pshenichnikov

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