Most of the present hiding techniques on video are considered over plaintext domain and plain video sequences are used to embed information bits. The work presented here reveals the novelty for information embedding in a video sequence over the ciphered domain. The carrier video signal is encrypted using chaos technique which uses multiple chaotic maps for encryption. The proposed reversible video information hiding scheme (RVIHS) exhibits an innovative property that, at the decoding side we can perfectly extract the information along with carrier video without any distortion. The public key modulation is a mechanism used to achieve data embedding, where as in secret key encryption is not required. The proposed approach is used to differentiate encoded and non-encoded picture patches at decoder end by implementing 2 class Support Vector Machine grouping. This helps for us to retrieve the original visual sequence with embedded message and to scale up embedding capacity. The experiment is conducted using real time videos for embedding the information. The outcome of proposed work bring about best embedding capacity, compared to existing techniques.

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Recommender systems are the systems that are designed to recommend items to the consumer depending on several different criteria. These systems estimate the most possible product that the consumers are most likely to buy and are of interest to. Companies like Netflix, Amazon, etc. use recommender services to allow their customers to find the right items or movies for them.In the current system recommendations, the content of ltering and collective ltering typically fall into two groups. The method is formerly Periment in our paper in all methods. We take film features such as stars, directors, for content-based ltering. Movie definition and keywords as inputs use TF-IDF and doc2vec for measuring the film resemblance. For the first time, Input to our algorithm is the film ranking encountered by users, and we use neighbours nearest K, as Factorization of matrix to estimate film scores for consumers. We find that teamwork functions better than content. Predictive error and estimation time ltering.

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