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Titlebook: CMBEBIH 2021; Proceedings of the I Almir Badnjevic,Lejla Gurbeta Pokvi? Conference proceedings 2021 The Editor(s) (if applicable) and The A

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發(fā)表于 2025-3-21 16:57:47 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱CMBEBIH 2021
副標(biāo)題Proceedings of the I
編輯Almir Badnjevic,Lejla Gurbeta Pokvi?
視頻videohttp://file.papertrans.cn/221/220325/220325.mp4
概述Reports on innovative biomedical engineering research and developments.Describes improved signal/image processing methods.Highlights the potential of systems engineering research in medicine
叢書名稱IFMBE Proceedings
圖書封面Titlebook: CMBEBIH 2021; Proceedings of the I Almir Badnjevic,Lejla Gurbeta Pokvi? Conference proceedings 2021 The Editor(s) (if applicable) and The A
描述.This book presents cutting-edge research and developments in the field of medical and biological engineering. It gathers the proceedings of the International Conference on Medical and Biological Engineering, CMBEBIH 2021, held partly virtually, partly physically, on April 21–24, 2021, from and in Mostar, Bosnia and Herzegovina. Focusing on the goal to ‘Stay Focused’, contributions report on both basic and applied research in a wide range of related fields, such as biomedical signal processing, medical physics and imaging, biosensors and micro/nanotechnologies, biomaterials, biomechanics and robotics, cardiorespiratory, endocrine and neural systems engineering. Novel models, methods and technologies for bio- and health informatics, as well as applications of machine learning and AI in health care, and advances in genetic engineering are also highlighted. All in all, this book provides academics and professionals with novel, practical solutions to solve the current problems in biomedical research and applications, and a source of inspiration for improving medicine and health care in the future.?.?.
出版日期Conference proceedings 2021
關(guān)鍵詞Physical Activity Monitoring; Electrical Impedance Signal; Adaptive Filtering; EMG Signal Classificatio
版次1
doihttps://doi.org/10.1007/978-3-030-73909-6
isbn_softcover978-3-030-73908-9
isbn_ebook978-3-030-73909-6Series ISSN 1680-0737 Series E-ISSN 1433-9277
issn_series 1680-0737
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Detection of Asthma Inflammatory Phenotypes Using Artificial Neural Networklume (FEV1), eosinophiles, FeNO and IgE were measured. The neural network was made and it detected asthma by analyzing these parameters. It had the accuracy of 92.2%. This shows a great potential of neural network use when it comes to the detection of asthma and this could be an instrument used in clinical practice.
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https://doi.org/10.1007/978-3-8348-9059-7he increase in the collectible amount of data, there is a growing demand for the automatization of the signal analysis process, which could reduce human error, save time and improve the quality and the accuracy of the calculated results.
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,Numerische Verfahren für Eigenwertprobleme,d limitations of intestinal modeling as opposed to in vivo studies. Finally, we discuss future directions that intestinal modeling researchers can explore, such as inclusion of other organs within the digestive system and more specific modeling applying to different types of foods.
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Simulating Passage and Absorption of Bolus with an Emphasis on the Small Intestine Using a MultiCompd limitations of intestinal modeling as opposed to in vivo studies. Finally, we discuss future directions that intestinal modeling researchers can explore, such as inclusion of other organs within the digestive system and more specific modeling applying to different types of foods.
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,L?sung linearer Gleichungssysteme,andom Subspace ensemble classifier model for diagnosis of neuromuscular disorders. Results are promising and showed that the SVM and ANN with Random Subspace ensemble method archived an accuracy of 99%.
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