Quantum machine learning-based electrokinetic mining for the identification of nanoparticles and exosomes with minimal training data by Abhimanyu Thakur, Pedro Correia Santos Bezerra, Abhishek, Shihao Zeng, Kui Zhang, Werner Treptow, Alexander Luna, Urszula Dougherty, Akushika Kwesi, Isabella R. Huang, Christine Bestvina, Marina Chiara Garassino, Fuyu Duan, Yash Gokhale, Bin Duan, Yin Chen, Qizhou Lian, Marc Bissonnette, Jianpan Huang, Huanhuan Joyce Chen

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Adaptive Weighted Diversity Ensemble Learning Approach for Fetal Health Classification on Cardiotocography Data by K. Aditya Shastry, Mohan Sellappa Gounder, T. G. Mohan Kumar, D. U. Karthik, V. Sushma, D. Subashree

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Early Risk Prediction in Acute Aortic Syndrome on Clinical Data Using Machine Learning by Mehdi Tavafi, Kalpdrum Passi, Robert Ohle

Subjects: “…machine learning…”
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Multi-Step Natural Gas Load Forecasting Incorporating Data Complexity Analysis with Finite Features by Ning Tian, Bilin Shao, Huibin Zeng, Meng Ren, Wei Zhao, Xue Zhao, Shuqiang Wu

“…This study aims to quantify and evaluate the complexity features of natural gas loads and to develop a multi-step-ahead forecasting model that integrates data decomposition and ensemble deep learning while considering these complexity features. …”
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A New Machine Learning Algorithm for Regional Low-Carbon Economic Development Analysis Based on Data Mining by Xinlei Liu

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Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review by Rita Banzi, Jacques Demotes, Paula Garcia, Enrico Glaab, Armin Rauschenberger, Chiara Gerardi

“…Objective To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects.Design Scoping review.Methods We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. …”
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Integrating Data Mining, Deep Learning, and Gene Ontology Analysis for Gene Expression-Based Disease Diagnosis Systems by Sergii Babichev, Igor Liakh, Jiri Skvor

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A Comparative Analysis of Machine Learning and Pedotransfer Functions Under Varying Data Availability in Two Greek Regions by Panagiotis Tziachris, Panagiota Louka, Eirini Metaxa, Miltiadis Iatrou, Konstantinos Tsiouplakis

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Predicting Early Employability of Vietnamese Graduates: Insights from Data-Driven Analysis Through Machine Learning Methods by Long-Sheng Chen, Thao-Trang Huynh-Cam, Van-Canh Nguyen, Tzu-Chuen Lu, Dang-Khoa Le-Huynh

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A comparative analysis of machine learning-based methods for impervious surface mapping using SAR and optical data by Siqi He, Lihong Zhu, Yiman Li, Qing Xia, Qiong Zheng, Zheng Wang, Xinyu Zou

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Multicenter comparative analysis of local and aggregated data training strategies in COVID-19 outcome prediction with Machine learning. by Carine Savalli, Roberta Moreira Wichmann, Fabiano Barcellos Filho, Fernando Timoteo Fernandes, Alexandre Dias Porto Chiavegatto Filho, IACOV-BR Network

“…Our study brings to light an important concern about the impact of grouping data from different hospitals in predictive machine learning models. …”
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Correction: Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data by Tongtong Jin, Ayitijiang· Halili

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LiDAR Sensor Parameter Augmentation and Data-Driven Influence Analysis on Deep-Learning-Based People Detection by Lukas Haas, Florian Sanne, Johann Zedelmeier, Subir Das, Thomas Zeh, Matthias Kuba, Florian Bindges, Martin Jakobi, Alexander W. Koch

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The Effect of Various Text Representation Methods for Sentiment Analysis on Movie Review Data with Different Machine Learning Methods by Veysel Göç, Muhammet Sinan Başarslan

“…In this study, we explore the potential of machine learning (ML) models after different text representation methods on the balanced IMDB dataset, which is widely regarded as a gold standard in sentiment analysis, one of the Natural Language processing (NLP) tasks. …”
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Analysis of seismicity in the Haicheng-Xiuyan region based on dense array data and deep learning methodsKey points by Zemin Liu, Weitao Wang, Lu Li, Zihao Li, Ziye Yu, Songyong Yuan, Lanshu Bai

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Bridging resource gaps in cross-lingual sentiment analysis: adaptive self-alignment with data augmentation and transfer learning by Li Chen, Shifeng Shang, Yawen Wang

“…This study proposes an adaptive self-alignment framework for large language models, incorporating novel data augmentation techniques and transfer learning strategies to mitigate resource imbalances. …”
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Analysis of Deep Learning Techniques for Vehicle Detection and Reidentification Using Data from Multiple Drones and Public Datasets by FELIPE P.A. EUPHRÁSIO, RAFAEL M. DE ANDRADE, ELCIO H. SHIGUEMORI, LIANGRID L. SILVA, MOISÉS JOSÉ S. FREITAS, NATHAN AUGUSTO Z. XAVIER, ARGEMIRO S.S. SOBRINHO

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scMoMtF: An interpretable multitask learning framework for single-cell multi-omics data analysis. by Wei Lan, Tongsheng Ling, Qingfeng Chen, Ruiqing Zheng, Min Li, Yi Pan

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A systematic review of machine learning-based remote sensing data analysis for geological and mined materials characterisation by Sureka Thiruchittampalam, Bikram Pratap Banerjee, Nancy F Glenn, Simit Raval

“…To explore current practices and the application of machine learning in interpreting complex imaging data for mine material characterisation, a review of 92 studies from 2004 to 2024 was conducted. …”
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Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion by Li Wan, Wenzhi Cheng, Jie Yang

“…In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. …”
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