Association of composite dietary antioxidant index and endometriosis risk in reproductive—age women: a cross-sectional study using big data-machine learning approach by Wenxin Chen, Kui Xiao, Chenyu Zhou, Jiajia Cheng, Zixuan Zeng, Fang Zhang

“…To investigate the relationship between the CDAI and the EM, a variety of statistical techniques were employed, including a weighted multiple logistic regression model, smooth curve fitting, machine learning analysis, and subgroup analyses.ResultsAfter controlling for potential confounding variables, the results indicated an inverse relationship between CDAI and EM (OR = 0.92, 95% CI 0.86–0.98, p = 0.011). …”
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Assessing COVID-19 Vaccine Effectiveness and Risk Factors for Severe Outcomes through Machine Learning Techniques: A Real-World Data Study in Andalusia, Spain by Álvaro Serrano-Ortiz, Juan Luis Romero-Cabrera, Jaime Monserrat Villatoro, Jaime Cordero-Ramos, Rafael Ruiz-Montero, Álvaro Ritoré, Joaquín Dopazo, Jorge del Diego Salas, Valle García Sánchez, Inmaculada Salcedo-Leal, Miguel Ángel Armengol de la Hoz, Isaac Túnez, Miguel Ángel Guzmán

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Groundwater estimation and determination of its probable recharge source in the Lower Swat District, Khyber Pakhtunkhwa, Pakistan, using analytical data and multiple machine learni... by Imran Ahmad, Ibrar Ul Haq, Mansoor Ahmad, Iram Gul, Mursaleen Khan, Khushnuma Khushnuma, Ubaid Ullah, Maqsood Ur Rehman, Mohamed Metwaly

“…It aims to identify recharge sources using a combination of analytical data and advanced machine learning (ML) algorithms. …”
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Machine Learning Applications for Predicting Longitudinal Cracking in Continuously Reinforced Concrete Pavement by Ali Alnaqbi, Ghazi G. Al-Khateeb, Waleed Zeiada

“…The research uses a dual-phase methodology to leverage data from the Long-Term Pavement Performance (LTPP) database. …”
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Explainable machine learning-driven models for predicting Parkinson’s disease and its prognosis: obesity patterns associations and models development using NHANES 1999–2018 data... by Jiaxin Fan, Shuai Cao, Hang Peng, Yuanjie Zhi, Shuqin Zhan, Rui Li

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University students’ subjective experiences with problem-based learning and associated generic skills by Ahram Lee, Eunju Jung

“…The current study investigated how university students subjectively experienced PBL and explored generic skills associated with their learning.MethodsUniversity students’ reflection papers were used as document data for analysis. …”
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Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma by Kevin Atsou, Anne Auperin, Jôel Guigay, Sébastien Salas, Sebastien Benzekry

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A scientometric review of the relationship between learning agility and work engagement in modern management context by Farira Nareswari, Rini Juni Astuti

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A scientometric review of the relationship between learning agility and work engagement in modern management context by Farira Nareswari, Rini Juni Astuti

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Edges are all you need: Potential of medical time series analysis on complete blood count data with graph neural networks. by Daniel Walke, Daniel Steinbach, Sebastian Gibb, Thorsten Kaiser, Gunter Saake, Paul C Ahrens, David Broneske, Robert Heyer

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Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes by Xueli Zhang, Yu Huang, Shunming Liu, Shuo Ma, Min Li, Zhuoting Zhu, Wei Wang, Xiayin Zhang, Jiahao Liu, Shulin Tang, Yijun Hu, Zongyuan Ge, Honghua Yu, Mingguang He, Xianwen Shang

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Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt by Aubin Ramon, Mingyang Ni, Olga Predeina, Rebecca Gaffey, Patrick Kunz, Shimobi Onuoha, Pietro Sormanni

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Big Data Market Optimization Pricing Model Based on Data Quality by Jian Yang, Chongchong Zhao, Chunxiao Xing

“…Then, from the perspective of data science, we analyzed the impact of quality level on big data analysis (i.e., machine learning algorithms) and defined the utility function of data quality. …”
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Cross-sensor data reconstruction for optical remote sensing gap-filling with attention-enhanced multi-scale fusion network by Xi Wang, Songchao Chen, Chang Zhou, Si-Bo Duan, Zhou Shi

“…Furthermore, Sentinel-2′s distinct red-edge and narrow near-infrared spectral bands play a vital role in vegetation monitoring, and their absence significantly hinders precise tracking of vegetation growth and detailed analysis. In response to these issues, we propose a deep learning framework incorporating an enhanced attention mechanism and multi-scale connection for precise remote sensing image reconstruction. …”
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Resource Time Series Analysis and Forecasting in Large-Scale Virtual Clusters by Yue Lin, Jiamin Wen, Xudong Zhang, Yan Liang, Jianjiang Li

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Pan-cancer predictive survival model development and evaluation using electronic health record and genetic data across 10 cancer types by Jurgita Gammall, Alvina G. Lai

“…Genetic data collected through the 100,000 Genomes Project was linked with clinical and demographic data provided by the National Cancer Registration and Analysis Service, Hospital Episode Statistics and Office for National Statistics. …”
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Social Factors Influencing Healthcare Expenditures: A Machine Learning Perspective on Australia’s Fiscal Challenges by Wei Gu, Zhantian Zhang, Ou Liu

“…While traditional statistical methods struggle to capture complex data relationships, machine learning offers a more robust approach to handling intricate and non-linear data. …”
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A Bibliometric Analysis on Artificial Intelligence in the Production Process of Small and Medium Enterprises by Federico Briatore, Marco Tullio Mosca, Roberto Nicola Mosca, Mattia Braggio

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Multimodal data-driven prognostic model for predicting long-term outcomes in older adult patients with sarcopenia: a retrospective cohort study by Mengdie Liu, Wen Guo, Jin Peng, Jinhui Wu

“…This study aims to utilize machine learning techniques that incorporate sociodemographic factors, health-related metrics, lifestyle variables, and biomarker data to improve risk stratification and management in older adults with SP.MethodsWe analyzed data from the NHANES from 1999–2006 and 2010–2018, including a total of 1,619 older adult patients with SP, with a 10-year follow-up period for this population, during which 541 (33%) patients died and 1,078 (67%) survived. …”
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High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network by Jianghe Chen, Pan Xiong, Haochen Wu, Xiaoran Zhang, Xuemin Zhang, Rongzi Chai, Ting Zhang, Kaixin Wang, Chaoyu Wang

“…At the algorithmic level, an Auxiliary Attention Temporal Convolutional Network (AuxATTCN) model is proposed, integrating an auxiliary attention mechanism with a Temporal Convolutional Network (TCN) to effectively capture long‐term dependencies and dynamically incorporate external driving factors such as geomagnetic activity and solar radiation. Comparative analysis with multiple experiments under varying geomagnetic and solar conditions shows that the AuxATTCN model significantly outperforms traditional time‐series methods (such as ARIMA, Prophet), mainstream deep learning models (including ConvLSTM, CONGRU, and TCN), and international ionospheric models (IRI2020, NeQuick2) in terms of overall error, seasonal and diurnal variations, and prediction accuracy during geomagnetic storms and solar activity peaks. …”
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