1,120 research outputs found
A New Approach for Improving Computer Inspections by using Fuzzy Methods for Forensic Data Analysis
Now a day2019;s digital world data in computers has great significance and this data is extremely critical in perspective for upcoming position and learn irrespective of different fields. Therefore we the assessment of such data is vital and imperative task. Computer forensic analysis a lot of data there in the digital campaign is study to extract data and computers consist of hundreds of thousands of files which surround shapeless text or data here clustering algorithms is of plays a great interest. Clustering helps to develop analysis of documents under deliberation. This document clustering analysis is extremely useful to analyze the data from seized devices like computers, laptops, hard disks and tablets etc. There are total six algorithms used for clustering of documents like K-means, K-medoids, single link, complete link, Average Link and CSPA. These six algorithms are used to cluster the digital documents. Existing document clustering algorithms are operated in single document at a time. In the proposed approach of these working algorithm applied on multiple documents at a time. Now we using clustering technique named as agglomerative hierarchical clustering which gives better finer clusters compared to existing techniques
Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies
Stories can have tremendous power -- not only useful for entertainment, they
can activate our interests and mobilize our actions. The degree to which a
story resonates with its audience may be in part reflected in the emotional
journey it takes the audience upon. In this paper, we use machine learning
methods to construct emotional arcs in movies, calculate families of arcs, and
demonstrate the ability for certain arcs to predict audience engagement. The
system is applied to Hollywood films and high quality shorts found on the web.
We begin by using deep convolutional neural networks for audio and visual
sentiment analysis. These models are trained on both new and existing
large-scale datasets, after which they can be used to compute separate audio
and visual emotional arcs. We then crowdsource annotations for 30-second video
clips extracted from highs and lows in the arcs in order to assess the
micro-level precision of the system, with precision measured in terms of
agreement in polarity between the system's predictions and annotators' ratings.
These annotations are also used to combine the audio and visual predictions.
Next, we look at macro-level characterizations of movies by investigating
whether there exist `universal shapes' of emotional arcs. In particular, we
develop a clustering approach to discover distinct classes of emotional arcs.
Finally, we show on a sample corpus of short web videos that certain emotional
arcs are statistically significant predictors of the number of comments a video
receives. These results suggest that the emotional arcs learned by our approach
successfully represent macroscopic aspects of a video story that drive audience
engagement. Such machine understanding could be used to predict audience
reactions to video stories, ultimately improving our ability as storytellers to
communicate with each other.Comment: Data Mining (ICDM), 2017 IEEE 17th International Conference o
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