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Titlebook: 7th International Conference on Advancements of Medicine and Health Care through Technology; Proceedings of MEDIT Simona Vlad,Nicolae Mariu

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https://doi.org/10.1007/978-3-658-01942-6proceeding to classification with the help of a Margin Pruning Adaboost classifier. This kind of a different and interesting approach provided results with a high classification accuracy of 97.33% when SVNLR features and Brainstorm optimization is utilized to classify with Margin Pruning Adaboost Classifier.
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Besonderheiten der Bauproduktion,overed/recovering hand or fingers. The device is microcontroller based and the software used for the application is . The force sensors – FSR type – were calibrated and the actual mass-voltage diagram was experimentally determined.
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https://doi.org/10.1007/978-3-658-01942-6eve this and especially how long does it take until the specialized intervention in these cases? Starting from these questions, we set out to create a device designed to have a dual role: monitoring respiratory activity and the second role is to alert the person in question in the event that he develops breathing problems and thus oxygenation.
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https://doi.org/10.1007/978-3-658-01942-6nced Classification Accuracy of 72% and 68% respectively while usage of Crow Search Algorithm with modified Adam as transformation technique in Support Vector Machine and K-Nearest Neighbor classifiers provide Balanced Classification Accuracy of 90% and 86%.
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