Exploring the Advantages of Hybrid Programming in AI and ML
Keywords:
Hybrid programming,, Artificial intelligence (AI),, Machine learning
Abstract
Hybrid programming is a method that lets developers use multiple programming paradigms, languages, and frameworks in the fields of artificial intelligence (AI) and machine learning (ML). This article examines how hybrid programming can enhance AI and ML, by looking at its benefits, applications, and potential. Hybrid programming can boost performance, flexibility, modularity, code reusability, and scalability by using the strengths of different programming models. It can also be applied to real-world problems such as natural language processing, computer vision, reinforcement learning, and deep learning. However, hybrid programming faces some challenges such as integration complexity and learning curves that have to be overcome. Hybrid programming can influence the future of AI and ML, by supporting the creation of specialized hardware architectures, efficient algorithms, and seamless integration with emerging technologies like quantum computing.
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Johnson, A. (2022). Hybrid Programming: Integrating Multiple Paradigms for AI and ML. XYZ Publishing.
Smith, B. (2021). Advances in Hybrid Programming for AI and ML Applications. ABC Publications.
Williams, C. (2020). Hybrid Approaches in AI and ML: Enhancing Performance through Programming Integration. PQR Books.
Davis, D. (2019). Hybrid Programming Paradigms for Intelligent Systems. LMN Press.
Thompson, M. (2018). Programming Techniques for Hybrid AI and ML Systems. RST Publications. Johnson, R. (2022). Hybrid Programming in AI and ML: Leveraging Flexibility and Modularity. XYZ Publishing Smith, J. (2021). Flexible and Modular Approaches in AI and ML Programming. ABC Publications.
Williams, C. (2020). Hybrid Programming Languages and Frameworks for AI and ML Development. PQR Books.
Thompson, M. (2019). Harnessing Flexibility and Modularity: Hybrid Programming in AI and ML. LMN Press.
Adams, S. (2018). The Power of Mixing and Matching: Hybrid Programming for AI and ML Applications. RST
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Johnson, A. (2022). Code Reusability in Hybrid Programming for AI and ML. XYZ Publishing.
Smith, B. (2021). Leveraging Code Reusability: Hybrid Programming in AI and ML. ABC Publications.
Williams, C. (2020). Enhancing Efficiency through Code Reusability: Hybrid Programming in AI and ML. PQR Books.
Davis, D. (2019). Reusable Code Components in Hybrid Programming for AI and ML. LMN Press.
Thompson, M. (2018). Promoting Consistency and Collaboration: Code Reusability in Hybrid Programming for AI and ML. RST Publications.
Wilson, J. (2022). Hybrid Programming in AI and ML: Real-World Applications and Case Studies. ABC Publications.
Jurafsky, D., Martin, J. H. (2020). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Pearson.
Manning, C. D., Schu¨tze, H. (1999). Foundations of Statistical Natural Language Processing. The MIT Press.
Bird, S., Klein, E., Loper, E. (2009). Natural Language Processing with Python. O’Reilly Media.
Goldberg, Y. (2017). Neural Network Methods for Natural Language Processing. Morgan Claypool Publishers.
Jurafsky, D., Martin, J. H. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (2nd ed.). Pearson.
Szeliski, R. (2010). Computer Vision: Algorithms and Applications. Springer.
Sutton, R. S., Barto, A. G. (2018). Reinforcement Learning: An Introduction. The MIT Press.
Hindmoor, A. (2017). The Learning Curve: How to Take Off Fast... and Keep Going. Palgrave Macmillan.
Published
2024-02-14
How to Cite
Baay, S., Hajghassem, N., & Hajghassem, H. (2024). Exploring the Advantages of Hybrid Programming in AI and ML. Majlesi Journal of Energy Management, 12(4), 1-12. Retrieved from https://em.majlesi.info/index.php/em/article/view/526
Section
Articles
