MANAGING DIGITAL MEDIA PRODUCTION THROUGH INTELLIGENT AUTOMATION
DOI:
https://doi.org/10.29121/shodhkosh.v6.i3s.2025.6778Keywords:
Intelligent Automation, Digital Media Production, Artificial Intelligence, Machine Learning, Creative Workflows, Digital TransformationAbstract [English]
The digital media sphere of production has blistered, which has led to the increase of the complexity of workflow, the presence of lower response time and the quality of the obtained output. The latest addition of intelligent automation in terms of artificial intelligence (AI) and machine learning (ML) has become the new game changer that can change the traditional production pipelines. The paper will also consider the application of smart automation to every stage of production process of digital media, such as the pre-production, production and post-production and how it can be applied to achieve the maximum efficiency, waste minimization, and creativity. Based on an analysis of the current automation models and systems, the paper explains how technologies such as natural language processing, computer vision, predictive analytics, and so forth can streamline processes, such as scriptwriting, camera control, editing, and even visual effects. Through the case studies of films, TV channels and content delivery websites, the paper gives examples of actual real-life applications of such tools as Adobe Sensei, Runway ML and Synthesia to automate the processes in the media industry without compromising the creative process. Moreover, the research touches upon the theoretical and ethical issues, as the problem is the dilemma of human creativity and algorithms. It also discusses the issues of intellectual property, data security, and workforce revolution in the creative industry.
References
Bertolini, M., Mezzogori, D., Neroni, M., and Zammori, F. (2021). Machine Learning for lications: A Comprehensive Literature Review. Expert Systems with Applications, 175, Article 114820. https://doi.org/10.1016/j.eswa.2021.114820 DOI: https://doi.org/10.1016/j.eswa.2021.114820
Borghesi, A., Burrello, A., and Bartolini, A. (2023). ExaMon-X: A Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems. IEEE Internet of Things Journal, 10, 2995-3005. https://doi.org/10.1109/JIOT.2021.3125885 DOI: https://doi.org/10.1109/JIOT.2021.3125885
Fordal, J. M., Schjølberg, P., Helgetun, H., Skjermo, T. Ø., Wang, Y., and Wang, C. (2023). Application of Sensor Data-Based Predictive Maintenance and Artificial Neural Networks to enable Industry 4.0. Advanced Manufacturing, 11, 248-263. https://doi.org/10.1007/s40436-022-00433-x DOI: https://doi.org/10.1007/s40436-022-00433-x
Guo, D., Chen, X., Ma, H., Sun, Z., and Jiang, Z. (2021). State Evaluation Method of Robot Lubricating Oil Based on Support Vector Regression. Computational Intelligence and Neuroscience, 2021, Article 9441649. https://doi.org/10.1155/2021/9441649 DOI: https://doi.org/10.1155/2021/9441649
Hadi, R. H., Hady, H. N., Hasan, A. M., Al-Jodah, A., and Humaidi, A. J. (2023). Improved Fault Classification for Predictive Maintenance in Industrial IoT Based on AutoML: A Case Study of Ball-Bearing Faults. Processes, 11, Article 1507. https://doi.org/10.3390/pr11051507 DOI: https://doi.org/10.3390/pr11051507
Kim, S. W., Kong, J. H., Lee, S. W., and Lee, S. (2022). Recent Advances of Artificial Intelligence in Manufacturing Industrial Sectors: A Review. International Journal of Precision Engineering and Manufacturing, 23, 111-129. https://doi.org/10.1007/s12541-021-00600-3 DOI: https://doi.org/10.1007/s12541-021-00600-3
Lu, B.-L., Liu, Z.-H., Wei, H.-L., Chen, L., Zhang, H., and Li, X.-H. (2021). A Deep Adversarial Learning Prognostics Model for Remaining Useful Life Prediction of Rolling Bearing. IEEE Transactions on Artificial Intelligence, 2, 329-340. https://doi.org/10.1109/TAI.2021.3097311 DOI: https://doi.org/10.1109/TAI.2021.3097311
Meddaoui, A., Hain, M., and Hachmoud, A. (2023). The Benefits of Predictive Maintenance in Manufacturing Excellence: A Case Study to Establish Reliable Methods for Predicting Failures. The International Journal of Advanced Manufacturing Technology, 128, 3685-3690. https://doi.org/10.1007/s00170-023-12086-6 DOI: https://doi.org/10.1007/s00170-023-12086-6
Msakni, M. K., Risan, A., and Schütz, P. (2023). Using Machine Learning Prediction Models for Quality Control: A Case Study from the Automotive Industry. Computational Management Science, 20, Article 14. https://doi.org/10.1007/s10287-023-00448-0 DOI: https://doi.org/10.1007/s10287-023-00448-0
Müller, D., März, M., Scheele, S., and Schmid, U. (2022). An Interactive Explanatory AI System for Industrial Quality Control. arXiv. https://doi.org/10.1609/aaai.v36i11.21530 DOI: https://doi.org/10.1609/aaai.v36i11.21530
Nunes, P., Rocha, E., Santos, J., and Antunes, R. (2023). Predictive Maintenance on Injection Molds by Generalized Fault Trees and Anomaly Detection. Procedia Computer Science, 217, 1038-1047. https://doi.org/10.1016/j.procs.2022.12.302 DOI: https://doi.org/10.1016/j.procs.2022.12.302
Pejić Bach, M., Topalović, A., Krstić, I., and Ivec, A. (2023). Predictive Maintenance in Industry 4.0 for SMEs: A Decision Support System Case Study Using Open-Source Software. Designs, 7, Article 98. https://doi.org/10.3390/designs7040098 DOI: https://doi.org/10.3390/designs7040098
Plathottam, S. J., Rzonca, A., Lakhnori, R., and Iloeje, C. (2023). A Review of Artificial Intelligence Applications in Manufacturing Operations. Journal of Advanced Manufacturing Processes, 5, Article e10159. https://doi.org/10.1002/amp2.10159 DOI: https://doi.org/10.1002/amp2.10159
Scalabrini Sampaio, G., Vallim Filho, A. R. D. A., Santos da Silva, L., and Augusto da Silva, L. (2019). Prediction of Motor Failure Time Using an Artificial Neural Network. Sensors, 19, Article 4342. https://doi.org/10.3390/s19194342 DOI: https://doi.org/10.3390/s19194342
Sundaram, S., and Zeid, A. (2023). Artificial Intelligence-Based Smart Quality Inspection for Manufacturing. Micromachines, 14, Article 570. https://doi.org/10.3390/mi14030570 DOI: https://doi.org/10.3390/mi14030570
Taşcı, B., Omar, A., and Ayvaz, S. (2023). Remaining Useful Lifetime Prediction for Predictive Maintenance in Manufacturing. Computers and Industrial Engineering, 184, Article 109566. https://doi.org/10.1016/j.cie.2023.109566 DOI: https://doi.org/10.1016/j.cie.2023.109566
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Copyright (c) 2025 Garima Chauhan, Prerak Sudan, Nitish Vashisht, Mr. Krishna Reddy BN, Gurpreet Kaur, Ashok Kumar S, Bipin Sule
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