Leveraging LLMs for optimised feature selection and embedding in structured data: A case study on graduate employment classification by Radiah Haque, Hui-Ngo Goh, Choo-Yee Ting, Albert Quek, M.D. Rakibul Hasan

“…The application of Machine Learning (ML) for predicting graduate student employability is a growing area of research, driven by the need to align educational outcomes with job market requirements. …”
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Association between accelerometer-measured physical activity volume and sleep duration in older adults: a cross-sectional interpretable machine learning analysis by XiaoTao Cai, Yi Xian, YuXin Zhou, TongYi Liu, Xinyue Zhang, Qing Chen

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Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as addition... by Karen Rischmüller, Vanessa Caton, Markus Wolfien, Markus Wolfien, Markus Wolfien, Luise Ehlers, Matti van Welzen, David Brauer, Lea F. Sautter, Fatuma Meyer, Luzia Valentini, Mats L. Wiese, Ali A. Aghdassi, Robert Jaster, Olaf Wolkenhauer, Olaf Wolkenhauer, Georg Lamprecht, Saptarshi Bej, Saptarshi Bej

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Leveraging machine learning to identify determinants of zero utilization of maternal continuum of care in Ethiopia: Insights from SHAP analysis and the 2019 mini DHS. by Shimels Derso Kebede, Agmasie Damtew Walle, Daniel Niguse Mamo, Ermias Bekele Enyew, Jibril Bashir Adem, Meron Asmamaw Alemayehu

“…The dataset was preprocessed and modeled using various machine learning algorithms through the PyCaret library, with lightGBM emerging as the best model after various models trained and evaluated based on classification performance metrics. …”
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Stroke risk prediction: a deep learning approach for identifying high-risk patients by Afeez A. Soladoye, Kazeem M. Olagunju, Sunday A. Ajagbe, Ibrahim A. Adeyanju, Precious I. Ogie, Pragasen Mudali

“…Abstract The application of Artificial Intelligence (AI) to diverse field has been widely accepted ranging from transportation, education, logistics, entertainment and health. Specifically, in recent time, the application of Machine Learning (ML) a subset of AI has equally got wide acceptance and relevance in various aspect of medicine ranging from diagnosis and prediction of diseases, development of drugs and treatment plan among others. …”
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Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies by Kuncham Sreenivasa Rao, Panduranga Vital Terlapu, D. Jayaram, Kalidindi Kishore Raju, G. Kiran Kumar, Rambabu Pemula, M. Venu Gopalachari, S. Rakesh

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Early warning forecasting of river ecological flow in a typical Karst basin, southwest China: Leveraging baseflow segmentation and interpretable machine learning by Chongxun Mo, Haiyang Wang, Shufeng Lai, Yi Huang, Wei Gao, Lingling Tang, Siting Zhong

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Understanding the determinants of treated bed net use in Ethiopia: A machine learning classification approach using PMA Ethiopia 2023 survey data. by Abraham Keffale Mengistu

“…<h4>Conclusion</h4>This study demonstrates the superiority of machine learning (ML) models in capturing complex, nonlinear determinants of ITN utilization, providing actionable insights for targeted malaria prevention strategies. …”
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Prediction of zero-dose children using supervised machine learning algorithm in Tanzania: evidence from the recent 2022 Tanzania Demographic and Health Survey by Beminate Lemma Seifu, Angwach Abrham Asnake, Alemayehu Kasu Gebrehana

“…This comprehensive approach enabled the accurate identification of zero-dose children, highlighting the effectiveness of machine learning in enhancing public health initiatives and optimising vaccination strategies. …”
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A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein by Bahareh Behkamal, Fatemeh Asgharian Rezae, Amin Mansoori, Rana Kolahi Ahari, Sobhan Mahmoudi Shamsabad, Mohammad Reza Esmaeilian, Gordon Ferns, Mohammad Reza Saberi, Habibollah Esmaily, Majid Ghayour-Mobarhan

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Predicting phage-host interaction via hyperbolic Poincaré graph embedding and large-scale protein language technique by Jie Pan, Rui Wang, Wenjing Liu, Li Wang, Zhuhong You, Yuechao Li, Zhemeng Duan, Qinghua Huang, Jie Feng, Yanmei Sun, Shiwei Wang

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DIGITAL TRANSFORMATION OF BUSINESS MANAGEMENT: INNOVATIVE MODELS, FORECAST SCENARIOS AND CURRENT CHALLENGES OF ECONOMY MODERNIZATION by I.V. Burtnyak, T.V. Khaludilo

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A novel and efficient personalized stress detection technique using a deep learning model by Ulligaddala Srinivasarao, Gopisetty Rathnamma, M. Satish Kumar, Lakshmipathi Anantha, Rakesh Kumar Donthi, T. Jhansi Rani

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A machine learning analysis of suicidal ideation and suicide attempt among U.S. youth and young adults from multilevel, longitudinal survey data by Molly M. Jacobs, Anne V. Kirby, Jessica M. Kramer, Nicole M. Marlow

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EEG-based neurodegenerative disease diagnosis: comparative analysis of conventional methods and deep learning models by B. R. Nayana, M. N. Pavithra, S. Chaitra, T. N. Bhuvana Mohini, Thompson Stephan, Vijay Mohan, Neha Agarwal

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Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015–2018 by Danhui Mao, Junfang Mu, Yajing Li, Lu He, Qianhui Chai, Xin Zhao, Xiaojun Ren, Hui Cheng

“…The Louvain algorithm was used to divide the CMM graph network into communities to obtain CMM patterns. Six machine learning models (RandomForest, GradientBoosting, SVM, KNN, Logistic Regression, and XGBoost) were trained using these patterns as labels to identify key factors influencing CMM patterns This study identified four CMM patterns: Hypertension Predominant Group (HPG, Pattern I), Uric Acid and Dyslipidemia Coexistence Group (UADCG, Pattern II), Multiple Diseases High Group (MDHG, Pattern III), and Kidney Disease Low Group (KDLG, Pattern IV) (Modularity = 0.748). …”
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Predictive value of anthropometric indices for incident of dyslipidemia: a large population-based study by Somayeh Ghiasi Hafezi, Atena Ghasemabadi, Negar Soleimani, Maryam Allahyari, Mina Moradi, Amin Mansoori, Rana Kolahi Ahari, Mark Ghamsary, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

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Multi-kernel inception-enhanced vision transformer for plant leaf disease recognition by Sk Mahmudul Hassan, Kumar Sekhar Roy, Ruhul Amin Hazarika, Mehbub Alam, Mithun Mukherjee

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Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach by Mahbuba Aktar, Nur Islam, Chaoyu Yang

“…Despite ongoing advancements in deep learning (DL), machine learning (ML), and image processing (IP), accurately identifying Bangla characters continues to be a demanding and unresolved issue. …”
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Explainable artificial intelligence for predicting medical students’ performance in comprehensive assessments by Haniye Mastour, Toktam Dehghani, Ehsan Moradi, Saeid Eslami

“…While Artificial intelligence (AI) holds transformative potential for predictive analytics, existing models lack the interpretability and reliability required for educational decision-making. …”
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