Nowadays people use social networks such as Facebook, Twitter, Orkut for sharing personal information and also significant events that occurs all over the world. Online news broadcast the most momentous events among users. Then the users discuss those events and post their reviews in micro blogs or in blogosphere. Since the web seems to be too huge and also the information available on the web is constantly updating, the task of identifying such events that are accessible on the web and the comments that are discussed related to those events in social networks is much daunting. Usually an event is temporal in nature, they change in time. Due to the temporal nature of events, the identification of event becomes even more difficult. Existing visual text analysis system extracts temporal themes from the large document stream and with that information; it provides temporal views of changes that occur on the event. Consequently, events that cause the changes can't be identified. In this paper, an Event Identification System is proposed to identify the most important events that occur and to identify the user discussion and also to rate their reviews. The proposed methodology make use of topic clustering, named entity recognition, latent dirichlet allocation, topic modeling to identify the significant events that are available on the web.
To detect audio manipulation in a pre recorded evidence videos by developing a synchronization verification algorithm to match the lip movements along with its audio pitch values. Audio video recognition has been considered as a key for speech recognition tasks when the audio is sullied, as well as visual recognition method used for speaker authentication in multispeaker scenarios. The primary aim of this paper is to point out the correspondence between the audio and video streams. Acquired audio feature sequences are processed with a Gaussian model. [1].This proposed method achieves parallel processing by effectively examining multiple videos at a time.In this paper, we train the machine by convolutional neural network (CNN) and deep neural network (DNN).CNN architecture maps both the modalities into a depiction space to evaluate the correspondence of audiovisual streams using the learned multimodal features. DNN is used as a discriminative model between the two modalities in order to concurrently distinguish between the correlated and uncorrelated components. The proposed architecture will deploy both spatial and temporal information jointly to effectively discover the correlation between temporal inf different modalities. We train a system by capturing the motion picture. This method achieves relative enhancement over 20% on the equal error rate and 7% on the average precision in comparison to the state of the art method.
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