A video copy detection system that is based on content fingerprinting can be used for video indexing and copyright applications. Most of the video copy detection algorithms proposed so far focus mostly on coping with signal distortions introduced by different encoding parameters; however, these algorithms do not cope well with display format conversions. They may rely on a fingerprint extraction algorithm followed by a fast approximate search algorithm. . To find whether a query video is copied from a video in a video database, the fingerprints of all the videos in the database are extracted and stored in advance. The search algorithm searches the stored fingerprints to find close enough matches for the fingerprints of the query video. The content based fingerprint extraction process does not work to get a better level search. Further in an enhancement of this concept of fingerprints this system handles the TIRI-DCT and DWT features to detect the copyright information. This paper proposes a novel sequence matching technique to detect copies of a video clip. If a video copy detection technique is to be effective, it needs to be robust to the many digitization and encoding processes that give rise to several distortions, including changes in brightness, color, frame format, as well as different blocky artifacts. It also handles a new nonmetric distance measure to find the similarity between the query and a database video fingerprint and it is proposed to achieve accurate duplicate detection. Then the performance of the TIRI DCT and the co-efficient is compared. The proposed method has been extensively tested and the results show that the proposed scheme is effective in detecting copies which has been subjected to wide range of modifications.
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This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and medical staff to know their status easily and more accurately. In the treatment of Parkinson’s disease, the affected patients with Parkinson’s disease can assess the prognostic risk factors, and the symptoms are evaluated to predict rapid progression in the early stages after diagnosis. The presented seizure prediction system introduces deep learning algorithms into EEG score analysis. This proposed work long short-term memory (LSTM) network model is mainly implemented for the identification and classification of qualitative patterns in the EEG of patients. While compared with other techniques like deep learning models such as convolutional neural networks (CNNs) and traditional machine learning algorithms, the proposed LSTM model plays a significant role in predicting impending crises over 4 different qualifying intervals from 10 minutes to 1.5 hours with very few wrong predictions.
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