Manuscript Number : IJSRST2296831

Ai-Driven Real-Time Scheduling for Linear TV Broadcasting : A Data-Driven Approach

Authors(3) :-Shashishekhar Ramagundam, Dinesh Patil, Niharika Karne

The rapid evolution of television broadcasting and the increasing demand for personalized content delivery necessitate intelligent scheduling solutions. Traditional linear TV broadcasting relies on static schedules, which often fail to adapt to real-time audience preferences. This paper presents an AI-driven real-time scheduling framework that integrates Long Short-Term Memory (LSTM) networks with Grey Wolf Optimizer (GWO)-based Q-learning to dynamically optimize TV programming. Our model leverages historical viewership data, real-time social media interactions, external influencing factors, and audience analytics to predict engagement levels across different time slots. By dynamically adjusting the broadcasting schedule based on real-time data, our approach enhances viewer engagement, maximizes advertisement revenue, and improves overall broadcasting efficiency. The proposed framework demonstrates the potential of AI-powered decision-making in modern television scheduling, offering a more responsive and audience-centric broadcasting experience.

Grey Wolf Optimizer, Long Short-Term Memory, Q-learning, Television Broadcasting.

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Publication Details

Published in : Volume 9 | Issue 4 | July-August 2022
Date of Publication : 2022-07-14
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 775-783
Manuscript Number : IJSRST2296831
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Shashishekhar Ramagundam, Dinesh Patil, Niharika Karne, " Ai-Driven Real-Time Scheduling for Linear TV Broadcasting : A Data-Driven Approach", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 9, Issue 4, pp.775-783, July-August-2022. Available at doi : https://doi.org/10.32628/IJSRST2296831    
Journal URL : https://ijsrst.com/IJSRST2296831
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