Deep Learning-Driven Intelligent Automation in Modern Computer Application Frameworks

  • Unique Paper ID: 199986
  • Volume: 12
  • Issue: 11
  • PageNo: 15549-15556
  • Abstract:
  • Intelligent automation has become a defining feature of modern computer application frameworks. As software systems become increasingly distributed, data-intensive, cloud-based, and user-centered, traditional rule-based automation is no longer sufficient to manage the complexity, speed, and adaptability required in contemporary computing environments. Deep learning has emerged as a transformative force in this context because it enables systems to learn patterns, predict outcomes, interpret unstructured data, optimize workflows, and support autonomous decision-making. This paper examines the theoretical foundations, practical applications, recent innovations, challenges, and future directions of deep learning-driven intelligent automation in modern computer application frameworks. It discusses key deep learning models such as Convolutional Neural Networks, Recurrent Neural Networks, and transformers, along with foundational algorithms that support automated perception, prediction, classification, generation, and decision-making. The paper further explores applications in software development automation, system management, cloud-native operations, cybersecurity, user interaction frameworks, and intelligent enterprise systems. It concludes that deep learning-driven intelligent automation is reshaping modern computer applications from static software tools into adaptive, self-improving, and context-aware intelligent systems

Copyright & License

Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{199986,
        author = {Shilpa Ghosh},
        title = {Deep Learning-Driven Intelligent Automation in Modern Computer Application Frameworks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15549-15556},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199986},
        abstract = {Intelligent automation has become a defining feature of modern computer application frameworks. As software systems become increasingly distributed, data-intensive, cloud-based, and user-centered, traditional rule-based automation is no longer sufficient to manage the complexity, speed, and adaptability required in contemporary computing environments. Deep learning has emerged as a transformative force in this context because it enables systems to learn patterns, predict outcomes, interpret unstructured data, optimize workflows, and support autonomous decision-making. This paper examines the theoretical foundations, practical applications, recent innovations, challenges, and future directions of deep learning-driven intelligent automation in modern computer application frameworks. It discusses key deep learning models such as Convolutional Neural Networks, Recurrent Neural Networks, and transformers, along with foundational algorithms that support automated perception, prediction, classification, generation, and decision-making. The paper further explores applications in software development automation, system management, cloud-native operations, cybersecurity, user interaction frameworks, and intelligent enterprise systems. It concludes that deep learning-driven intelligent automation is reshaping modern computer applications from static software tools into adaptive, self-improving, and context-aware intelligent systems},
        keywords = {Deep learning, intelligent automation, computer application frameworks, CNN, RNN, transformers, MLOps, AIOps, software automation, intelligent systems.},
        month = {April},
        }

Cite This Article

Ghosh, S. (2026). Deep Learning-Driven Intelligent Automation in Modern Computer Application Frameworks. International Journal of Innovative Research in Technology (IJIRT), 12(11), 15549–15556.

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