Hybrid Neural Networks - Integration and Applications: Investigating approaches for integrating multiple neural network architectures to leverage their complementary strengths

Authors

  • Dr. Iben Nielsen Associate Professor of Computer Science, Aarhus University, Denmark Author

Keywords:

Hybrid Neural Networks, Integration, Neural Network Architectures

Abstract

Hybrid Neural Networks (HNNs) have emerged as a promising approach to combine the strengths of different neural network architectures. This paper explores various integration methods for HNNs and their applications across different domains. We first discuss the motivation behind using HNNs and then delve into the techniques used to integrate different architectures. We also highlight several successful applications of HNNs, including image classification, natural language processing, and reinforcement learning. Finally, we discuss the challenges and future directions of HNN research.

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References

Tatineni, Sumanth, and Anjali Rodwal. “Leveraging AI for Seamless Integration of DevOps and MLOps: Techniques for Automated Testing, Continuous Delivery, and Model Governance”. Journal of Machine Learning in Pharmaceutical Research, vol. 2, no. 2, Sept. 2022, pp. 9-41, https://pharmapub.org/index.php/jmlpr/article/view/17.

Prabhod, Kummaragunta Joel. "Advanced Machine Learning Techniques for Predictive Maintenance in Industrial IoT: Integrating Generative AI and Deep Learning for Real-Time Monitoring." Journal of AI-Assisted Scientific Discovery 1.1 (2021): 1-29.

Tatineni, Sumanth, and Venkat Raviteja Boppana. "AI-Powered DevOps and MLOps Frameworks: Enhancing Collaboration, Automation, and Scalability in Machine Learning Pipelines." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 58-88.

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Published

14-06-2023

How to Cite

[1]
Dr. Iben Nielsen, “Hybrid Neural Networks - Integration and Applications: Investigating approaches for integrating multiple neural network architectures to leverage their complementary strengths”, Distrib Learn Broad Appl Sci Res, vol. 9, pp. 308–316, Jun. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/36

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