Assimilation of Moderate-Resolution Imaging Spectroradiometer Level Two Cloud Products for Typhoon Analysis and Prediction by Haomeng Zhang, Yubao Liu, Yu Qin, Zheng Xiang, Yueqin Shi, Zhaoyang Huo

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An Accurate Deep Learning-Based Computer-Aided Diagnosis System for Gastrointestinal Disease Detection Using Wireless Capsule Endoscopy Image Analysis by Sameh Abd El-Ghany, Mahmood A. Mahmood, A. A. Abd El-Aziz

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AI-driven health analysis for emerging respiratory diseases: A case study of Yemen patients using COVID-19 data by Saleh I. Alzahrani, Wael M. S. Yafooz, Ibrahim A. Aljamaan, Ali Alwaleedi, Mohammed Al-Hariri, Gameel Saleh

“…We explored the use of AI-powered predictive models and classifiers to enhance healthcare preparedness by forecasting respiratory disease trends using COVID-19 data. We developed mathematical models based on autoregressive (AR), moving average (MA), ARMA, and machine and deep learning algorithms to predict daily confirmed deaths. …”
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Enhanced dry SO₂ capture estimation using Python-driven computational frameworks with hyperparameter tuning and data augmentation by Robert Makomere, Hilary Rutto, Alfayo Alugongo, Lawrence Koech, Evans Suter, Itumeleng Kohitlhetse

Subjects: “…Machine learning…”
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Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI by Pei-Chun Lin, Pei-Chun Lin, Tsai-Chung Li, Tsai-Chung Li, Tzu-Hsuan Huang, Ying-Lin Hsu, Wen-Chao Ho, Jia-Lang Xu, Ching-Liang Hsieh, Ching-Liang Hsieh, Ching-Liang Hsieh, Zih-En Jhang

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Prediction of Time-evolving Radial Magnetic Fields on the Solar Surface Using Deep Learning by Hyun-Jin Jeong, Stefaan Poedts, Haopeng Wang, Ekatarina Dineva, Panagiotis Gonidakis, Francesco Carella, George Miloshevich, Luis Linan, Harim Lee

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Exploring the Transformative Potential of Learning Analytics in Medical Education: A Systematic Review by EHSAN TOOFANINEJAD, SHANE DAWSON, SOMAYE SOHRABI, MASOMEH KALANTARION

“…Introduction: Learning Analytics (LA) has emerged as a potent tool in medical education, offering data-driven insights and personalized support to learners. …”
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Optimizing data-driven arctic marine forecasting: a comparative analysis of MariNet, FourCastNet, and PhyDNet by Aleksei V. Buinyi, Aleksei V. Buinyi, Dias A. Irishev, Edvard E. Nikulin, Aleksandr A. Evdokimov, Polina G. Ilyushina, Natalia A. Sukhikh, Natalia A. Sukhikh

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VANTAGE: An open‐source application for visualisation, navigation and analysis of video and ancillary time‐series data in bio‐logging studies by Stefan Schoombie, Tagen deWet, Stefan Britz, Andrew D. Lowther, W. Chris Oosthuizen

“…The labelled time‐series data can then be exported for further analyses (e.g., in machine learning applications). …”
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Evaluating the Effectiveness of Dimensionality Reduction on Machine Learning Algorithms in Time Series Forecasting by Rida Zaheer, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Ramzan Talib

Subjects: “…Time series analysis…”
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Detecting Galactic Rings in the DESI Legacy Imaging Surveys with Semisupervised Deep Learning by Jianzhen Chen, Zhijian Luo, Cheng Cheng, Jun Hou, Shaohua Zhang, Chenggang Shu

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Predicting PV Severity: A Cross-Sectional Study Using Machine Learning Methods and Multifactorial Data Analysis From a High Prevalence Region by Soraiya Ebrahimpour-Koujan, Anahita Najafi, Rahil Salari-Baghoonabad, Kamran Balighi, Maryam Daneshpazhooh

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Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. by Tiago de Oliveira Barreto, Fernando Lucas de Oliveira Farias, Nicolas Vinícius Rodrigues Veras, Pablo Holanda Cardoso, Gleyson José Pinheiro Caldeira Silva, Chander de Oliveira Pinheiro, Maria Valéria Bezerra Medina, Felipe Ricardo Dos Santos Fernandes, Ingridy Marina Pierre Barbalho, Lyane Ramalho Cortez, João Paulo Queiroz Dos Santos, Antonio Higor Freire de Morais, Gustavo Fontoura de Souza, Guilherme Medeiros Machado, Márcia Jacyntha Nunes Rodrigues Lucena, Ricardo Alexsandro de Medeiros Valentim

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Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the “RegulaRN Leitos Gerais” platform by Tiago de Oliveira Barreto, Fernando Lucas de Oliveira Farias, Nicolas Vinícius Rodrigues Veras, Pablo Holanda Cardoso, Gleyson José Pinheiro Caldeira Silva, Chander de Oliveira Pinheiro, Maria Valéria Bezerra Medina, Felipe Ricardo dos Santos Fernandes, Ingridy Marina Pierre Barbalho, Lyane Ramalho Cortez, João Paulo Queiroz dos Santos, Antonio Higor Freire de Morais, Gustavo Fontoura de Souza, Guilherme Medeiros Machado, Márcia Jacyntha Nunes Rodrigues Lucena, Ricardo Alexsandro de Medeiros Valentim

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The application and predictive value of the weight-adjusted-waist index in BC prevalence assessment: a comprehensive statistical and machine learning analysis using NHANES data by Wenjing Wang, Biao Wu, Jian Li, Yibiao Shang, Mengting Liu, Qi Fang, Han Zhang, Xiang Li, Dongdi Wu

“…This study aimed to investigate the association between WWI and BC prevalence using data from a nationally representative population in the United States. …”
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Integrative analysis of multi-omics data and gut microbiota composition reveals prognostic subtypes and predicts immunotherapy response in colorectal cancer using machine learning by Jun Wang, Yuan Cong, Bo Tang, Juan Liu, Ke Pu

“…We performed an integrative analysis of multi-omics data from 274 CRC patients to investigate the impact of gut microbiota composition on prognosis, identify novel subtypes, and develop a machine learning-based prognostic model. …”
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Comparative analysis of machine learning models for malaria detection using validated synthetic data: a cost-sensitive approach with clinical domain knowledge integration by Gudi V. Chandra Sekhar, Chekol Alemu

“…Abstract Malaria remains a critical global health challenge requiring innovative diagnostic approaches. Machine learning offers promising solutions for automated detection, but systematic algorithm comparison using clinically validated data remains limited. …”
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Enhancing liver disease diagnosis with hybrid SMOTE-ENN balanced machine learning models—an empirical analysis of Indian patient liver disease datasets by Ritu Rani, Garima Jaiswal, Nancy, Lipika, Shashi Bhushan, Fasee Ullah, Prabhishek Singh, Manoj Diwakar, Manoj Diwakar

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Harnessing artificial intelligence for predicting breast cancer recurrence: a systematic review of clinical and imaging data by Jaqueline Alvarenga Silveira, Alexandre Ray da Silva, Mariana Zuliani Theodoro de Lima

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A machine learning approach to predict positive coronary artery calcium scores in individuals with diabetes: a cross-sectional analysis of ELSA-Brasil baseline data by J.L. Amorim, I.M. Bensenor, A.P. Alencar, A.C. Pereira, A.C. Goulart, P.A. Lotufo, I.S. Santos

“…Considering all ML models in our analysis, age, systolic blood pressure, and sex were frequently influential variables. …”
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