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

Authors

  • Shashishekhar Ramagundam  
  • Dinesh Patil  
  • Niharika Karne  

DOI:

https://doi.org/10.32628/IJSRST2296831

Keywords:

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

Abstract

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.

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International Journal of Scientific Research in Science and Technology

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Published

2022-07-14

Issue

Volume 9, Issue 4, July-August-2022

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
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), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 4, pp.775-783, July-August-2022. Available at doi : https://doi.org/10.32628/IJSRST2296831