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    "abstract": "Movie Industry across the globe is a multi-billion dollar in revenue. There are several factors and somewhat complicated, which makes a movie a box-office success. The financial cost of making a movie [1] can range from modest cost to mammoth several 100 million dollars in spending. The audience rules in terms of liking/disliking of a movie. Financiers and producers reel through several financial losses if the movie does not do well in the box-office. Can Artificial Intelligence techniques help from historical data, be it movies, audience reviews/sentiments, cast/crews to predict the success of a movie? Our paper explores the proposal for a hybrid RNN/LSTM/CNN based neural network architecture that can predict the success of the content. It leads to a useful foundation that enables content creators to manage the pre-production part of the journey better by making the right decisions that enhance the probability of success. The foundation of our proposal can help the pre-production state with a certain degree of predictability to the success of the content, explainability causing the success, and the next best action.",
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    "authors": [
      {
        "affiliation": "Data Scientist, Ness Digital Engineering",
        "name": "Suman Kalyan"
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      {
        "affiliation": "Solution Architect, Ness Digital Engineering",
        "name": "Ashwyn Tirkey"
      },
      {
        "affiliation": "SVP Engineering, Ness Digital Engineering",
        "name": "Angshuman Patra"
      },
      {
        "affiliation": "VP of Business Development, Ness Digital Engineering",
        "name": "Sujay Kumar"
      },
      {
        "affiliation": "AVP Business Development, Ness Digital Engineering",
        "name": "Pranav Singh"
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      {
        "affiliation": "Data Scientist, Ness Digital Engineering",
        "name": "Abraham Addanki"
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      "holder": "Society of Motion Picture and Television Engineers, Inc.",
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    "docTitle": "Deep Learning Approach to Predicting the Success of Content",
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