(2019) Detection of Myocardial Infarction Based on Novel Deep Transfer Learning Methods for Urban Healthcare in Smart Cities, arXiv, p.
Results show significant improvement in accuracy and recall rate as compared to the existing state-of-the-art techniques.Īlghamdi A, Hammad M, Ugail H, Abdel-Raheem A, Muhammad K, Khalifa HS et al. Three different angles 18 0, 36 0 and 54 0 of the CASIA B dataset are selected for the evaluation process and accuracy of 94.3%, 93.8% and 94.7% is achieved respectively. These selected features are serially combined and fed to One against All Multi Support Vector Machine (OAMSVM) for final recognition. At a later stage, best features are selected by the Firefly algorithm and Skewness based approach. To achieve this goal, we fuse the features of both second last and third last layers in a parallel process. The extraction of CNN features is a key step in which our target is to extract the most active features. Four primary steps are involved such as: preprocessing of original video frames, exploiting pre-trained Densenet-201 CNN model for features extraction, reduction of additional features from extracted vector based on a hybrid selection method, and finally recognition using supervised learning methods. In this work, a novel fully automated method is proposed for HGR under various view angles using deep learning. Moreover, recognition under various view angles is another key challenge in HGR. In HGR, the change in an individual walk along with wearing clothes and carrying bag are major covariant controls which impact the performance of a system. Human Gait Recognition (HGR) is a biometric approach, widely used for security purposes from the past few decades.
0 kommentar(er)
