Advancing Artificial Intelligence Through Machine Learning: Exploring Novel Architectures, Algorithmic Innovations and Real-World Applications for Transformative Impact

References

[1] Ajani, T. S., Imoize, A. L., & Atayero, A. A. (2021). An overview of machine learning within embedded and mobile devices—Optimizations and applications. Sensors, 21(13), 4412. https://doi.org/10.3390/s21134412

[2] Busnatu, Ș., Niculescu, A. G., Bolocan, A., Petrescu, G. E., Păduraru, D. N., Năstasă, I., ... & Martins, H. (2022). Clinical applications of artificial intelligence—An updated overview. Journal of Clinical Medicine, 11(8), 2265. https://doi.org/10.3390/jcm11082265

[3] Chowdhury, M., & Chowdhury, M. (2021, August 11). The evolution of artificial intelligence: Past, present & future. Analytics Insight. https://www.analyticsinsight.net/artificial-intelligence/the-evolution-of-artificial-intelligence-past-present-future

[4] Dean, J. (2020). The deep learning revolution and its implications for computer architecture and chip design. In Proceedings of the 2020 IEEE International Solid-State Circuits Conference (ISSCC), San Francisco, CA, USA, 16–20 February 2020 (pp. 8–14).

[5] Fradkov, A. L. (2020). Early history of machine learning. IFAC-PapersOnLine, 53(2), 1385–1390.

[6] Haigh, K. Z., Mackay, A. M., Cook, M. R., & Lin, L. G. (2015). Machine learning for embedded systems: A case study. BBN Technologies, Cambridge, MA, USA, 8571, 1–12.

[7] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016 (pp. 770–778).

[8] Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al. (2012). Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 29, 82–97.

[9] Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.

[10] Madhurjya Chowdhury, & Madhurjya Chowdhury. (2021, August 11). The evolution of artificial intelligence: Past, present & future. Analytics Insight. https://www.analyticsinsight.net/artificial-intelligence/the-evolution-of-artificial-intelligence-past-present-future

[11] Rafique, D., & Velasco, L. (2018). Machine learning for network automation: Overview, architecture, and applications [Invited Tutorial]. Journal of Optical Communications and Networking, 10(10), D126–D143.

[12] Verganti, R., Vendraminelli, L., & Iansiti, M. (2020). Innovation and design in the age of artificial intelligence. Journal of Product Innovation Management, 37(3), 212–227. https://doi.org/10.1111/jpim.12523

[13] What is the attention mechanism? (n.d.). H2O.ai. https://h2o.ai/wiki/attention-mechanism/

[14] Recent advancements in artificial intelligence technology trends and implications. (n.d.). ResearchGate. https://www.researchgate.net/profile/DrAkarunamurthy/publication/373638231_Recent_Advancements_in_Artificial_Intelligence_Technology_Trends_and_Implications/links/64f47bb3fa851147de0f9d69/Recent-Advancements-in-Artificial-Intelligence-Technology-Trends-and-Implications.pdf

[15] Types of machine learning. (n.d.). Javatpoint. https://www.javatpoint.com/types-of-machine-learning

[16] Machine learning architecture. (n.d.). BMC. https://www.bmc.com/blogs/machine-learning-architecture/

[17] Bias and ethical concerns in machine learning. (n.d.). ISACA. https://www.isaca.org/resources/isaca-journal/issues/2022/volume-4/bias-and-ethical-concerns-in-machine-learning

[18] Machine learning vs AI: Differences, uses, & benefits. (n.d.). Qlik. https://www.qlik.com/us/augmented-analytics/machine-learning-vs-ai

[19] OakTrust digital repository. (n.d.). https://oaktrust.library.tamu.edu/items/ec75102d-b194-4a19-b820-da1cba4a6497

[20] Attention mechanism in machine learning. (n.d.). H2O.ai Wiki. https://h2o.ai/wiki/attention-mechanism/

[21] ADIMULAM, T., BHOYAR, M., & REDDY, P. (2019). AI-Driven Predictive Maintenance in IoT-Enabled Industrial Systems.

[22] ADIMULAM, T., BHOYAR, M., & REDDY, P. (2019). AI-Driven Predictive Maintenance in IoT-Enabled Industrial Systems.

[23] Selvarajan, G. P. (2019). Integrating machine learning algorithms with OLAP systems for enhanced predictive analytics.

[24] Selvarajan, G. P. The Role of Machine Learning Algorithms in Business Intelligence: Transforming Data into Strategic Insights.

[25] Damacharla, P., Javaid, A. Y., Gallimore, J. J., & Devabhaktuni, V. K. (2018). Common metrics to benchmark human-machine teams (HMT): A review. IEEE Access, 6, 38637-38655.

[26] Damacharla, P., Rao, A., Ringenberg, J., & Javaid, A. Y. (2021, May). TLU-net: a deep learning approach for automatic steel surface defect detection. In 2021 International Conference on Applied Artificial Intelligence (ICAPAI) (pp. 1-6). IEEE.

[27] Ashraf, S., Aggarwal, P., Damacharla, P., Wang, H., Javaid, A. Y., & Devabhaktuni, V. (2018). A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment. International Journal of Distributed Sensor Networks, 14(6), 1550147718781750.

[28] Dhakal, P., Damacharla, P., Javaid, A. Y., & Devabhaktuni, V. (2019). A near real-time automatic speaker recognition architecture for voice-based user interface. Machine learning and knowledge extraction, 1(1), 504-520.

[29] Ashraf, S., Aggarwal, P., Damacharla, P., Wang, H., Javaid, A. Y., & Devabhaktuni, V. (2018). A low-cost solution for unmanned aerial vehicle navigation in a global positioning system–denied environment. International Journal of Distributed Sensor Networks, 14(6), 1550147718781750.

[30] Chaudhary, A. A. (2018). Enhancing Academic Achievement and Language Proficiency Through Bilingual Education: A Comprehensive Study of Elementary School Students. Educational Administration: Theory and Practice, 24(4), 803-812.

[31] Chaudhary, Arslan Asad. "EXPLORING THE IMPACT OF MULTICULTURAL LITERATURE ON EMPATHY AND CULTURAL COMPETENCE IN ELEMENTARY EDUCATION." Remittances Review 3.2 (2018): 183-205.

[32] Chaudhary, A. A. (2022). Asset-Based Vs Deficit-Based Esl Instruction: Effects On Elementary Students Academic Achievement And Classroom Engagement. Migration Letters, 19(S8), 1763-1774.

[33] https://www.researchgate.net/figure/The-Artificial-Intelligence-Realm-and-the-implied-Techniques-6_fig1_364360534

[34] https://www.researchgate.net/figure/Transformer-neural-network-architecture-overview_fig1_369476958

@article{1703908,
      author = {Manjeet Malaga},
      title = {Advancing Artificial Intelligence Through Machine Learning: Exploring Novel Architectures, Algorithmic Innovations and Real-World Applications for Transformative Impact},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {6},
      pages = {332-351},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703908.pdf},
      abstract = {Several years have seen the tremendous development of AI technology that has transformed markets and economies and redefined human interactions. Thus, this paper focuses on the modern trends of AI discoveries and the changes incorporated into different fields in recent years. The most important branches of AI technologies such as deep learning methods, natural language processing, and generative adversarial networks have taken AI applications to new heights. These have created accurate diagnostics in health, empowered finance with predictive models, and self-driving cars worldwide. But on the flip side, the emergence of AI has seen discrete evolution which presents ethical and social concerns. The problem-solving of bias, data security, and the management of the tension between process automation and the human workforce are three crucial issues. The future trends to consider include explainability, federated learning, and AI integration at edge applications. Another field that also looks set to revolutionize the range of AI algorithms and performance is quantum computing. However, there are still some issues in this respect Some of them are discussed below: The search for generalized AI models goes on but data quality and domain invariance are still issues. The administration and participation approaches define the ethical and policy impact of AI on technology and society. In this paper, the authors discuss the details of the latest developments in AI, their functioning, consequences, and possible future trends. Analyzing the advantages and risks, we emphasize the potential for constructive AI and meaningful terms with citizens, on the one hand, while maintaining reference to ethical parameters on the other.},
      keywords = {Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Neural Network Architectures, Algorithmic Innovations, Real-World AI Applications, Transformer Models, Reinforcement Learning.},
      month = {December},
  }