Optimizing Artificial Intelligence Systems for Real-World Applications

  • Authors

    • Ridwan Boya Marqas Computer Engineering
    • Saman M. Almufty Reasearcher at Computer Science dept., Knowledge University, Erbil, Iraq
    • Prof. Dr. ENGİN AVCI Software Engineering, Firat University, Elazig, Turkey
    • Renas R. Asaad Computer Department, Knowledge University, Erbil, Iraq

    Received date: January 1, 2025

    Accepted date: February 9, 2025

    Published date: February 20, 2025

    https://doi.org/10.14419/xxc0jx38
  • AI optimization, algorithmic improvements, hardware acceleration, scalable AI, efficient computing, ethical AI, real-world AI applications

  • Abstract

    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

    1. 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.
    2. Amodei, D., & Hernandez, D. (2018). AI and compute. OpenAI Blog. Retrieved from https://openai.com
    3. Brock, A., Lim, T., Ritchie, J. M., & Weston, N. (2017). SMASH: One-shot model architecture search through hypernetworks. arXiv preprint, arXiv:1708.00193.
    4. Elsken, T., Metzen, J. H., & Hutter, F. (2019). Neural architecture search: A survey. Journal of Machine Learning Research, 20(55), 1–21.
    5. Gao, H., Wang, Z., & Ji, S. (2019). Graph U-Net: Learning hierarchical graph representations. International Conference on Machine Learning (ICML).
    6. 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).
    7. Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint, arXiv:1503.02531.
    8. Marr, B. (2020). How AI is transforming business. Forbes. Retrieved from https://www.forbes.com
    9. 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.
    10. Shi, W., Cao, J., Zhang, Q., et al. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
    11. 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.
    12. Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
    13. Zhang, C., Bengio, S., Hardt, M., et al. (2021). Understanding deep learning (still) requires rethinking generalization. Communications of the ACM, 64(3), 107–115.
    14. Zoph, B., & Le, Q. V. (2017). Neural architecture search with reinforcement learning. International Conference on Learning Represen-tations (ICLR).
    15. Almufti, S., Marqas, R., & Ashqi, V. (2019). Taxonomy of bio-inspired optimization algorithms. Journal of Advanced Computer Sci-ence & Technology, 8(2), 23.
    16. 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
    17. 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
    18. 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
    19. 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
    20. 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
    21. 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
    22. 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

  • Downloads

  • How to Cite

    Boya Marqas , R., Saman M. Almufty, ENGİN AVCI, P. D. ., & R. Asaad, R. . (2025). Optimizing Artificial Intelligence Systems for Real-World Applications. International Journal of Scientific World, 11(1), 40-47. https://doi.org/10.14419/xxc0jx38

    Received date: January 1, 2025

    Accepted date: February 9, 2025

    Published date: February 20, 2025