Stroke prediction has become one of the significant research areas due to the increasing fatality rate. Hence, this article proposes a novel Adaptive Weight Bi-Directional Long Short-Term Memory (AWBi-LSTM) classifier model for stroke risk level prediction for IoT data. To efficiently train the classifier, Hybrid Genetic removes the missing data with Kmeans Algorithm (HKGA), and the data are aggregated.
View Article and Find Full Text PDFThe integration of virtual reality (VR), augmented reality (AR), and mixed reality (MR) in urological practices and medical education has led to modern training systems that are cost-effective and with an increased expectation toward surgical performance and outcomes. VR aids the user in interacting with the virtual environment realistically by providing a three-dimensional (3D) view of the structures inside the body with high-level precision. AR enhances the real environment around users by integrating experience with virtual information over physical models and objects, which in turn has improved understanding of physiological mechanisms and anatomical structures.
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