Titlebook: 6th Kuala Lumpur International Conference on Biomedical Engineering 2021; Proceedings of BioMe Juliana Usman,Yih Miin Liew,Fatimah Ibrahim

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6th Kuala Lumpur International Conference on Biomedical Engineering 2021Proceedings of BioMe

Blood-Clot

https://doi.org/10.1007/978-3-642-93707-1c auditory task processing using an optimally trained model. The trained Support Vector Machine (SVM) highest performance reported at random 2 training dataset with scoring of 92.3% using 70% triple-random training dataset. Based on the sensor ranking, the C. electrode outperformed the other electro

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Laconic

Wissenschaftliche Kongresse und Preise,ïve Bayes, Linear Discriminant Analysis, and Support Vector Machine. The accuracy of Support Vector Machine is 64% when no transformation technique is used and it is increased to 88% when Improved Bald Eagle Search Optimization is used for transforming the features.

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https://doi.org/10.1007/978-3-642-93718-7king speeds from 27 sets of gait data with total of 453 gait cycles collected from the walking trial, captured via via Vicon Motion System (Vicon MX, Oxford Metrics, UK). Kinematic parameters of the gait cycles were employed as the input layer of the Bi-LSTM deep learning architecture. Our proposed

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Wissenschaftliche Kongresse und Preise,erative time frame. In this paper, an automated stent strut segmentation algorithm was developed, utilizing 3D information of stent structure and state-of-the-art U-Net. The implementation of the algorithm preserves the spatial resolution of the full-size OCT images without down-sampling. The algori