Predicting the infecting dengue serotype from antibody titre data using machine learning. by Bethan Cracknell Daniels, Darunee Buddhari, Taweewun Hunsawong, Sopon Iamsirithaworn, Aaron R Farmer, Derek A T Cummings, Kathryn B Anderson, Ilaria Dorigatti

“…We applied four machine learning classifiers and multinomial logistic regression to the titre data to predict the infecting serotype. …”
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Collaborative Data Cleaning Framework: a Pilot Case Study for Machine Learning Development by Nikolaus Parulian, Bertram Ludäscher

“… This study experiments with collaborative data cleaning, a pivotal phase in data preparation for both analysis and machine learning. …”
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Responsive early childhood parenting, preschool enrollment, and eventual student learning outcomes: a cross-country analysis using hierarchical linear modeling with TIMSS 2019 data by Aditi Bhutoria, Nayyaf Aljabri, Saheli Bose

“…Abstract This paper examines whether parental engagement in early childhood and preschooling act as substitutes, or whether their joint effect enhances students’ learning outcomes. We utilize the TIMSS 2019 dataset and employ a hierarchical linear modeling (HLM) approach to analyze data from 52 countries, ensuring a robust examination of cross-national variations. …”
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Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty–guided space-time analysis by Dilek M. Yalcinkaya, MS, Khalid Youssef, PhD, Bobak Heydari, MD, Janet Wei, MD, C. Noel Bairey Merz, MD, Robert Judd, PhD, Rohan Dharmakumar, PhD, Orlando P. Simonetti, PhD, Jonathan W. Weinsaft, MD, Subha V. Raman, MD, Behzad Sharif, PhD

“…Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. …”
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Predictive analysis of total organic carbon (TOC) in shale targets: example from the Lower Cretaceous of the Austral Basin (Patagonia, Argentina) using machine learning on outcrop data by Sebastian M. Richiano, Federico Ares

“…This study explores the potential of machine learning (ML) for predicting Total Organic Carbon (TOC) content using outcrop data, a novel approach compared to traditional subsurface data applications. …”
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Comprehensive analysis of scRNA-seq and bulk RNA-seq data via machine learning and bioinformatics reveals the role of lysine metabolism-related genes in gastric carcinogenesis by Yongfu Shao, Chujia Chen, Xuan Yu, Jianing Yan, Junming Guo, Guoliang Ye

“…Methods The roles of lysine metabolism-related genes in GC were investigated by in-depth analysis of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data via machine learning and multiple bioinformatics methods and confirmed by multiple cell and molecular biology methods. …”
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A synthetic data-driven machine learning approach for athlete performance attenuation prediction by Mauricio C. Cordeiro, Ciaran O. Cathain, Ciaran O. Cathain, Lorcan Daly, Lorcan Daly, David T. Kelly, David T. Kelly, Thiago B. Rodrigues

“…However, applying machine learning (ML) frameworks to this domain remains challenging due to data scarcity limitations. …”
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Thermographic Data Processing and Feature Extraction Approaches for Machine Learning-Based Defect Detection by Alexey Moskovchenko, Michal Svantner

“…Data preparation and feature extraction are crucial factors affecting ML model results, especially in thermographic data analysis. …”
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Statistical Data-Generative Machine Learning-Based Credit Card Fraud Detection Systems by Xiaomei Feng, Song-Kyoo Kim

“…This study addresses the challenges of data imbalance and missing values in credit card transaction datasets by employing mode-based imputation and various machine learning models. …”
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Nonlinear Volatility Risk Prediction Algorithm of Financial Data Based on Improved Deep Learning by Wangsong Xie

“…To increase the prediction accuracy of financial data, a new nonlinear volatility risk prediction algorithm is proposed based on the improved deep learning algorithm. …”
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Predicting financial default risks: A machine learning approach using smartphone data by Shinta Palupi, Gunawan, Ririn Kusdyawati, Richki Hardi, Rana Zabrina

“…This study leverages machine learning (ML) techniques to predict financial default risks using smartphone data, providing a novel approach to financial risk assessment. …”
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Traffic concealed data detection method based on contrastive learning and pre-trained Transformer by HE Shuai, ZHANG Jingchao, XU Di, JIANG Shuai, GUO Xiaowei, FU Cai

“…By transforming the problem of concealed data detection into a similarity analysis problem, a diversity-sensitive Transformer architecture was developed leveraging contrastive learning, which enhanced the model’s sensitivity to traffic differences through the use of positive and negative sample pairs, and using information noise contrastive estimation (Info NCE) as the loss function for fine-tuning downstream tasks of encrypted traffic. …”
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A Secure Data Collection Method Based on Deep Reinforcement Learning and Lightweight Authentication by Yunlong Wang, Jie Zhang, Guangjie Han, Dugui Chen

“…According to simulation analysis, LCAP-SIoT outperforms existing solutions in terms of computing and communication costs, and LS-QMIX results in superior performance in terms of data collection rate, task completion time, and the success rate of authentication for newly joined UAVs, indicating the feasibility of LS-QMIX in dynamic expansion scenarios.…”
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Bank data protection and fraud identification based on improved adaptive federated learning and WGAN by Yunjiao Zheng

“…Abstract To enhance the protection of bank data privacy, build a more secure and efficient data privacy protection system, and effectively identify transaction fraud, this study first proposes an improved adaptive data protection architecture by combining federated learning and differential privacy technology. …”
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Mitigating data bias and ensuring reliable evaluation of AI models with shortcut hull learning by Wenhao Zhou, Faqiang Liu, Hao Zheng, Rong Zhao

“…Addressing these inherent biases is particularly difficult due to the complex, high-dimensional nature of data. Here, we introduce shortcut hull learning, a diagnostic paradigm that unifies shortcut representations in probability space and utilizes diverse models with different inductive biases to efficiently learn and identify shortcuts. …”
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Teachers Co-Designing and Enacting Elementary Data Science Curriculum through Connected Learning by Danielle Herro, Ibrahim Oluwajoba Adisa, Oluwadara Abimbade

“…Qualitative methodology guided the data analysis of observations, reflective journals, interviews, and curriculum artifacts. …”
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Reinforcement Learning for Data-driven Workflows in Radio Interferometry. I. Principal Demonstration in Calibration by Brian M. Kirk, Urvashi Rau, Ramyaa Ramyaa

“…Data processing consists of a sequence of calibration and analysis procedures where choices must be made about the sequence of procedures as well as the specific configuration of the procedure itself. …”
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Developing animated video-based learning with powtoon to foster critical thinking in data presentation by Tomi Listiawan, Jane Eka Sari Radiyah

“…This study focuses on developing and evaluating animated video-based learning content using Powtoon software to enhance critical thinking skills in data presentation, following the ADDIE (Analyze, Design, Develop, Implement, and Evaluate) instructional design model. …”
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Deep Aramaic: Towards a synthetic data paradigm enabling machine learning in epigraphy. by Andrei C Aioanei, Regine R Hunziker-Rodewald, Konstantin M Klein, Dominik L Michels

“…Our results validate the model's capabilities in handling diverse real-world scenarios, proving the viability of our synthetic data approach and avoiding the dependence on scarce training data that has constrained epigraphic analysis. …”
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Big data-driven machine learning: transforming multi-omics lung cancer research by Yanqi Zhang, Mingyu Liu, Jinhua Luo, Zhongqing Xu

“…Adding clinical data to biological information significantly improved model accuracy and enhanced patient stratification. …”
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