The influence of Gen-AI tools application for text data augmentation: case of Lithuanian educational context data classification by Pavel Stefanovič, Urtė Radvilaitė, Birutė Pliuskuvienė, Simona Ramanauskaitė

“…All subsets were used to train several machine-learning algorithms. Additionally, the text has been processed into numerical data using two methods: bag-of-words and sBERT. …”
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Integrating data from unmanned aerial vehicles and Sentinel-2 with PROSAIL-5D-driven machine learning for fuel moisture content estimation in agroecosystems by Jinlong Liu, Jia Jin, Jing Huang, Mengjuan Wu, Shaozheng Hao, Haoyi Jia, Tengda Qin, Yuqing Huang, Dan Chen, Nathsuda Pumijumnong

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Development and validation of a machine learning-based predictive model for compassion fatigue in Chinese nursing interns: a cross-sectional study utilizing latent profile analysis by Lijuan Yi, Ting Shuai, Jingjing Zhou, Liang Cheng, Maria F. Jiménez-Herrera, Xu Tian

“…The AUC values for the eight machine learning models ranged from 0.644 to 0.826 for the training set and 0.651 to 0.757 for the test set, indicating moderate to good discriminatory ability. …”
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Early Childhood Anemia in Ghana: Prevalence and Predictors Using Machine Learning Techniques by Maryam Siddiqa, Gulzar Shah, Mahnoor Shahid Butt, Asifa Kamal, Samuel T. Opoku

“…With accuracy (94.74%), sensitivity (82.5%), specificity (50.78%), and AUC (86.62%), the random forest model was proven to be the most effective machine learning predictive model. …”
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An Investigation of factors Influencing electric vehicles charging Needs: Machine learning approach by Cuthbert Ruseruka, Judith Mwakalonge, Gurcan Comert, Saidi Siuhi, Debbie Indah, Sarah Kasomi, Tumlumbe Juliana Chengula

“…This research aims to investigate the changing needs of EV users to establish factors to be considered in the selection of charging demands using machine learning, using an extreme gradient boosting model. …”
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Voice biomarkers as prognostic indicators for Parkinson’s disease using machine learning techniques by Ifrah Naeem, Allah Ditta, Tehseen Mazhar, Muhammad Anwar, Mamoon M. Saeed, Habib Hamam

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Transforming Cardiac Care: Machine Learning in Heart Condition Prediction Using Phonocardiograms by Sandra D’Souza, Niranjan Reddy S, Saikonda Krishna Tarun, Sohan P, aneesha acharya k

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GENDER-SPECIFIC PREDICTORS OF VAULT PERFORMANCE IN GYMNASTICS: A MACHINE LEARNING APPROACH by Dušan Đorđević, Janez Vodičar, Robi Kreft, Edvard Kolar, Miloš Paunović, Saša Veličković, Miha Marinšek

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Predictors of depression among Chinese college students: a machine learning approach by Lin Luo, Junfeng Yuan, Chenghan Wu, Yanling Wang, Rui Zhu, Huilin Xu, Luqin Zhang, Zhongge Zhang

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Machine learning based prediction of specific heat capacity for half-Heusler compounds by Laxman Chaudhary, Keshab Chaudhary, Ambika Shahi, Kedar Nath Jaiswal, Dipendra Prasad Kalauni, Se-Hun Kim, Madhav Prasad Ghimire

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A Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling by Reza Lotfi, Soheila Sadeghi, Sadia Samar Ali, Fatemeh Ramyar, Ehsan Ghafourian, Ebrahim Farbod

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Designing Water‐in‐Oil Emulsion Using Microfluidic Systems Through Machine Learning by Samar Damiati, Mei Wu, Ayad Turky, Rimantas Kodzius, Safa A. Damiati

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Optimizing Machine Learning-Based Ovarian Cancer Prediction Through Normalization Strategies by Roopashri Shetty, Siddhant Gupta, Vansh Mediratta, Shwetha Rai, M. Geetha

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Multi-Sensor Fusion and Machine Learning for Forest Age Mapping in Southeastern Tibet by Zelong Chi, Kaipeng Xu

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Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an international multicenter study by Bingtian Dong, Ruiling He, Shenghong Ju, Yuping Chen, Ivica Grgurevic, Jianzhong Ma, Ying Guo, Huizhen Fan, Qiang Yan, Chuan Liu, Huixiong Xu, Anita Madir, Kristian Podrug, Jia Wang, Linxue Qian, Zhengzi Geng, Shanghao Liu, Tao Ren, Guo Zhang, Kun Wang, Meiqin Su, Fei Chen, Sumei Ma, Liting Zhang, Zhaowei Tong, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Hui Liu, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Li Bian, Ping An, Xin Zhang, Shaoting Zhang, Jinhua Shao, Xiangman Zhang, Wei Rao, Chaoxue Zhang, Christoph Frank Dietrich, Won Kim, Xiaolong Qi

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Feasibility of Mental Health Triage Call Priority Prediction Using Machine Learning by Rajib Rana, Niall Higgins, Kazi Nazmul Haque, Kylie Burke, Kathryn Turner, Terry Stedman

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Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning by Fan Jiang, Peng Peng, Zhenting Xu, Yu Xu, Ding Yang, Shouquan Chai, Shuai Yuan, Limin Hua, Dawei Wang, Xuanye Wen

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Application of Machine Learning Methods for Employee Turnover Prediction Based on Open Data by A. N. Kazinets

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Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly patients with cardiovascular metabolic diseases in China: a longitudinal st... by Gege Zhang, Sijie Dong, Li Wang

“…This study aims to construct a machine learning model to predict the risk of depression in middle-aged and elderly patients with CMD and to identssify key risk factors. …”
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Digital Recruitment Using Intelligent Dialogue Systems Based on Machine Learning Principles by I. N. Kalinouskaya

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