Advanced Deep Learning Architectures for Scalable and Explainable Artificial Intelligence
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I1P105Keywords:
Deep Learning, Explainable AI, Scalability, Convolutional Neural Networks, Recurrent Neural Networks, Transformers, Generative Adversarial Networks, Attention Mechanism, Hybrid ModelsAbstract
The rapid evolution of artificial intelligence (AI) has necessitated the development of advanced deep learning architectures that not only enhance performance but also ensure scalability and explainability. This paper reviews various state-of-the-art architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers, and Generative Adversarial Networks (GANs), emphasizing their roles in complex data processing tasks across different domains. We explore the significance of scalability in deploying these models in real-world applications, particularly in resource-constrained environments. Furthermore, we delve into the emerging field of Explainable AI (XAI), which seeks to demystify AI decision-making processes. Techniques such as attention mechanisms and hybrid models combining neural networks with symbolic reasoning are discussed as effective means to enhance interpretability without compromising accuracy. By synthesizing insights from recent literature, this paper aims to provide a comprehensive understanding of how these architectures can be optimized for both performance and transparency, paving the way for more trustworthy AI systems. The findings underscore the necessity for ongoing research to balance the trade-offs between model complexity, computational efficiency, and explainability
References
[1] MDPI. (2023). Scalable deep learning architectures for real-world applications. Information, 15(12), 755. Retrieved from https://www.mdpi.com/2078- 2489/15/12/755
[2] MDPI. (2023). Machine learning and deep learning architectures: A review. Mathematics, 10(15), 2552. Retrieved from https://www.mdpi.com/2227- 7390/10/15/2552
[3] MDPI. (2020). Efficient and scalable deep learning for healthcare applications. Journal of Personalized Medicine, 10(4), 213. Retrieved from https://www.mdpi.com/2075-4426/10/4/213
[4] ResearchGate. (2023). Machine learning and deep learning architectures and trends: A review. Retrieved from https://www.researchgate.net/publication/385095492
[5] ResearchGate. (2019). Deep learning architectures. Retrieved from https://www.researchgate.net/publication/336904955 _Deep_Learning_Architectures
[6] Wikipedia contributors. (n.d.). Deep neural networks. In Wikipedia. https://en.wikipedia.org/wiki/Deep_neural_networks
[7] IBM Developer. (n.d.). Machine learning and deep learning architectures. https://developer.ibm.com/articles/cc-machinelearning-deep-learning-architectures/
[8] Suman Chintala, "Next - Gen BI: Leveraging AI for Competitive Advantage", International Journal of Science and Research (IJSR), Volume 13 Issue 7, July 2024, pp. 972-977, https://www.ijsr.net/getabstract.php?paperid=SR247 20093619, DOI: https://www.doi.org/10.21275/SR24720093619
[9] Simplilearn. (n.d.). Deep learning algorithm tutorial. https://www.simplilearn.com/tutorials/deep-learningtutorial/deep-learning-algorithm [10] Nature. (2023). Advances in scalable AI systems. Nature, 615(867). Retrieved from https://www.nature.com/articles/s41586-023-06735- 9
[11] Suman Chintala, Vikramrajkumar Thiyagarajan, 2023. "Harnessing AI for Transformative Business Intelligence Strategies", ESP International Journal of Advancements in Computational Technology (ESPIJACT) Volume 1, Issue 3: 81-96.
[12] IEEE. (2020). Scalable deep learning models for cloud environments. Proceedings of IEEE. Retrieved from https://ieeexplore.ieee.org/document/9139677/
[13] Microsoft Research. (n.d.). Efficient and scalable deep learning systems. https://www.microsoft.com/enus/research/video/efficient-and-scalable-deeplearning/
[14] Suman, Chintala (2024). Evolving BI Architectures: Integrating Big Data for Smarter Decision-Making. American Journal of Engineering, Mechanics and Architecture, 2 (8). pp. 72-79. ISSN 2993-2637
[15] ProjectPro. (n.d.). Deep learning architectures. https://www.projectpro.io/article/deep-learningarchitectures/996
[16] Functionize. (n.d.). Neural network architectures and generative models: Part 1. https://www.functionize.com/blog/neural-networkarchitectures-and-generative-models-part1
[17] Sunrise Geek. (n.d.). Scaling deep learning models for real-world applications. https://www.sunrisegeek.com/post/scaling-deeplearning-models-for-real-world-applications
[18] Chintala, S. and Thiyagarajan, V., “AI-Driven Business Intelligence: Unlocking the Future of Decision-Making,” ESP International Journal of Advancements in ComputationalTechnology, vol. 1, pp. 73-84, 2023.
[19] GeeksforGeeks. (n.d.). Introduction to deep learning. https://www.geeksforgeeks.org/introduction-deeplearning/
[20] TechTarget. (n.d.). Preventing machine learning scalability problems. https://www.techtarget.com/searchenterpriseai/tip/Ti ps-to-prevent-machine-learning-scalability-problems
[21] NVIDIA. (n.d.). Multi-GPU scalability for deep learning systems. https://info.nvidia.com/multi-gpu-ondemand
[22] MarkovML. (n.d.). Model scalability in machine learning. https://www.markovml.com/blog/modelscalability
[23] Radhika Kanubaddhi, "Real-Time Recommendation Engine: A Hybrid Approach Using Oracle RTD, Polynomial Regression, and Naive Bayes," SSRG International Journal of Computer Science and Engineering, vol. 8, no. 3, pp. 11-16, 2021. Crossref, https://doi.org/10.14445/23488387/IJCSE-V8I3P103
[24] Vikramrajkumar Thiyagarajan, 2024. “Predictive Modeling for Revenue Forecasting in Oracle EPBCS: A Machine Learning Perspective”, International Journal of Innovative Research of science, Engineering and technology (IJIRSET), Volume 13, Issue 4.
[25] Ganesh, A. ., & Crnkovich, M., (2023). Artificial Intelligence in Healthcare: A Way towards Innovating Healthcare Devices. Journal of Coastal Life Medicine, 11(1), 1008–1023. Retrieved from https://jclmm.com/index.php/journal/article/view/46 7
