Optimizing Artificial Intelligence Systems for Real-World Applications
DOI:
https://doi.org/10.14419/xxc0jx38Published
20-02-2025Keywords:
AI optimization, algorithmic improvements, hardware acceleration, scalable AI, efficient computing, ethical AI, real-world AI applicationsAbstract
The optimization of Artificial Intelligence (AI) systems is critical for improving performance, scalability, and adaptability across various real-world applications. This paper explores key optimization techniques, including algorithmic enhancements, hardware acceleration, software tools, and data preprocessing. Challenges such as resource constraints, domain-specific requirements, and ethical concerns are analyzed. Case studies in healthcare, finance, manufacturing, and autonomous systems demonstrate notable improvements in accuracy, efficiency, and scalability. A systematic framework is proposed to guide AI optimization, incorporating iterative testing, hardware-software integration, and deployment strategies. The findings highlight AI optimization’s transformative potential in developing scalable, efficient, and ethical systems. Future research directions include the creation of generalizable frameworks, energy-efficient AI, and fairness-aware optimization to ensure broader applicability and equity.
References
Abadi, M., Barham, P., Chen, J., et al. (2016). TensorFlow: A system for large-scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI), 265–283.
Amodei, D., & Hernandez, D. (2018). AI and compute. OpenAI Blog. Retrieved from https://openai.com
Brock, A., Lim, T., Ritchie, J. M., & Weston, N. (2017). SMASH: One-shot model architecture search through hypernetworks. arXiv preprint, arXiv:1708.00193.
Elsken, T., Metzen, J. H., & Hutter, F. (2019). Neural architecture search: A survey. Journal of Machine Learning Research, 20(55), 1–21.
Gao, H., Wang, Z., & Ji, S. (2019). Graph U-Net: Learning hierarchical graph representations. International Conference on Machine Learning (ICML).
Han, S., Mao, H., & Dally, W. J. (2016). Deep compression: Compressing deep neural networks with pruning, trained quantization, and Huffman coding. International Conference on Learning Representations (ICLR).
Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint, arXiv:1503.02531.
Marr, B. (2020). How AI is transforming business. Forbes. Retrieved from https://www.forbes.com
Shojaei, B., Naserabadi, H. D., & Amiri, M. J. T. (2024). Optimizing competency-based human resource allocation in construction pro-ject scheduling: A multi-objective meta-heuristic approach. Qubahan Academic Journal, 4(3), 861–881.
Shi, W., Cao, J., Zhang, Q., et al. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650.
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
Zhang, C., Bengio, S., Hardt, M., et al. (2021). Understanding deep learning (still) requires rethinking generalization. Communications of the ACM, 64(3), 107–115.
Zoph, B., & Le, Q. V. (2017). Neural architecture search with reinforcement learning. International Conference on Learning Represen-tations (ICLR).
Almufti, S., Marqas, R., & Ashqi, V. (2019). Taxonomy of bio-inspired optimization algorithms. Journal of Advanced Computer Sci-ence & Technology, 8(2), 23.
Almufti, S. M., Marqas, R. B., Nayef, Z. A., & Mohamed, T. S. (2021). Real-time face-mask detection with Arduino to prevent COVID-19 spreading. Qubahan Academic Journal, 1(2), 39–46. https://doi.org/10.48161/qaj.v1n2a47
Almufti, S. M., & Zeebaree, S. R. M. (2024). Leveraging distributed systems for fault-tolerant cloud computing: A review of strategies and frameworks. Academic Journal of Nawroz University, 13(2), 9–29. https://doi.org/10.25007/ajnu.v13n2a2012
Almufti, S. M., Marqas, R. B., & Rajab, R. A. (2022). Firebase efficiency in CSV data exchange through PHP-based websites. Academic Journal of Nawroz University, 11(3), 410–414. https://doi.org/10.25007/ajnu.v11n3a1480
Ahmad, H. B., Asaad, R. R., Almufti, S. M., Hani, A. A., Sallow, A. B., & Zeebaree, S. R. M. (2024). Smart home energy saving with big data and machine learning. Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 8(1), 11–20. https://doi.org/10.22437/jiituj.v8i1.32598
Abdulrahman, S. M., Asaad, R. R., Ahmad, H. B., Hani, A. A., Zeebaree, S. R. M., & Sallow, A. B. (2024). Machine learning in nonlin-ear material physics. Journal of Soft Computing and Data Mining, 5(1). https://doi.org/10.30880/jscdm.2024.05.01.010
Sallow, A. B., Asaad, R. R., Ahmad, H. B., Abdulrahman, S. M., Hani, A. A., & Zeebaree, S. R. M. (2024). Machine learning skills to K–12. Journal of Soft Computing and Data Mining, 5(1). https://doi.org/10.30880/jscdm.2024.05.01.011
Thirugnanam, T., et al. (2024). PIRAP: Medical cancer rehabilitation healthcare center data maintenance based on IoT-based deep fed-erated collaborative learning. International Journal of Cooperative Information Systems, 33(1). https://doi.org/10.1142/S0218843023500053