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Titlebook: Biomedical Engineering Systems and Technologies; 16th International J Ana Moita,Katja Bühler,Hugo Gamboa Conference proceedings 2024 The Ed

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發(fā)表于 2025-3-21 16:51:22 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Biomedical Engineering Systems and Technologies
期刊簡稱16th International J
影響因子2023Ana Moita,Katja Bühler,Hugo Gamboa
視頻videohttp://file.papertrans.cn/193/192706/192706.mp4
學(xué)科分類Communications in Computer and Information Science
圖書封面Titlebook: Biomedical Engineering Systems and Technologies; 16th International J Ana Moita,Katja Bühler,Hugo Gamboa Conference proceedings 2024 The Ed
影響因子.This book constitutes the refereed post-conference proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023, held in Lisbon, Portugal, during February 16-18, 2023...The 7 regular papers? included in this book were carefully reviewed and selected from 246 submissions. They contribute to the understanding of relevant trends of current research on Biomedical Engineering Systems and Technologies, that include Pattern Recognition and Machine Learning, Decision Support Systems, Data Mining and Data Analysis, eHealth Applications, eHealth, Detection and Identification, Computational Intelligence, Cognitive Informatics, Big Data in Healthcare and Assistive Technologies..
Pindex Conference proceedings 2024
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Diachronic Salience in Love Analogiesntifying these states, individual channel analysis are presented. Using the CHB-MIT scalp EEG dataset, the proposed method outperforms stae of the art and achieves seizure detection sensitivity of 99.3% and seizure onset prediction sensitivity of 98%. Extremely intensive requirements of computing ar
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Time-Specific Mappings in War: The Crusadesstomized loss function for the LOS regression task. Benchmarking against baseline models and related research demonstrates the superior performance of the proposed methods, with an average absolute error of 2.36 days for LOS prediction and a 50% accuracy for the next hospital unit classification. Th
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https://doi.org/10.1007/978-94-007-0341-4ce. To reach an optimal set, the best approach is branch and bound, albeit its heavy computational cost. This overhead can be addressed through a trade-off between the feature set’s optimisation level and the process’ computational feasibility. The improvement of the feature space has implications o
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,Topic Modelling and?Interpretable Cost Estimation for?Medical Insurance Fraud Detection,dvantage of immediately interpretable results. Results show incorporating context discovery and domain knowledge into fraud detection algorithms assists identification of comparable providers and generation of interpretable results for subject-matter experts in the decision-support process.
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