KEYWORDS: Medical imaging, Data hiding, Magnetic resonance imaging, Visualization, Image processing, Image segmentation, Deep learning, Cryptography, Steganography, Medicine
Due to a growth in information sharing and the use of multiple digital technologies, human lifestyles have been delivered into the virtual world. This digital world has seen the use of images in special fields increase dramatically, especially in healthcare. Healthcare services can be delivered remotely through telemedicine, a well-known method of providing quality healthcare for people worldwide. There is the risk of illegal exploitation of medical data when telemedicine applications involve exposing data over open networks. In particular, medical experts should exercise more caution when sharing a patient’s private information. In the proposed model, reversible data concealment is combined with visual cryptography to provide a secure method of exchanging medical images. A cover image is divided into nonoverlapping secret shares using Hadamard matrix. Secret digital imaging communication in medicine (DICOM) image is encoded by a deep learning model and embedded into secret shares for secure medical image sharing. Finally, the DICOM image is fully reversibly extracted with the cover image. In this case, a visual cryptographic design is used to secure the embedded secret shares. Furthermore, the metrics, such as mean squared error, peak-signal–to-noise ratio, and normalized correlations, are evaluated in the suggested scheme and are compared with various research outcomes to determine the performance of the nominated model.
KEYWORDS: Video, Data modeling, Motion models, Feature extraction, Visual process modeling, RGB color model, Detection and tracking algorithms, Video surveillance, Performance modeling, Video processing
Human activity recognition is a field of video processing that requires restricted temporal analysis of video sequences for estimating the existence of different human actions. Designing an efficient human activity model requires credible implementations of keyframe extraction, preprocessing, feature extraction and selection, classification, and pattern recognition methods. In the real-time video, sequences are untrimmed and do not have any activity endpoints for effective recognition. Thus, we propose a hybrid gated recurrent unit and long short-term memory-based recurrent neural network model for high-efficiency human action recognition in untrimmed video datasets. The proposed model is tested on the TRECVID dataset, along with other online datasets, and is observed to have an accuracy of over 91% for untrimmed video-based activity recognition. This accuracy is compared with various state-of-the-art models and is found to be higher when evaluated on multiple datasets.
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