Abstract

Machine Learning, a pivotal technology within the realm of artificial intelligence, has experienced remarkable progress in recent times. This research offers a thorough and structured presentation of machine learning. It begins with a comprehensive look at the evolution of machine learning throughout history, then zeroes in on dissecting the foundational algorithms that underpin the field. Following this, the study sheds light on the cutting-edge developments in machine learning, with the goal of thoroughly examining its applications across different sectors and contemplating the prospective trajectories for its future.

Keywords

Machine LearningArtificial Neural NetworksGenrative AI

Publication

Accepted Unknow ; Published 2024 November 2

Journal of Computer Technology and Applied Mathematics, 2024, 1(4), 3007-4126

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Metadata

DOI

10.5281/zenodo.14003350

ARK

ark:/40704/JCTAM.v1n4a14

Pages

117–122

References

7

Disciplines

Artificial Intelligence

Subjects

Artificial Neural Networks

Cite This Article

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

Funding

Not applicable.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Informed Consent Statement

Not applicable.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author Contributions

Not applicable.

References

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2. Che, C., Li, C., & Huang, Z. (2024). The Integration of Generative Artificial Intelligence and Computer Vision in Industrial Robotic Arms. International Journal of Computer Science and Information Technology, 2(3), 1-9.
3. Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281-285.
4. Che, C., Huang, Z., Li, C., Zheng, H., & Tian, X. (2024). Integrating generative AI into financial market prediction for improved decision making. Applied and Computational Engineering, 64, 155-161.
5. Liu, H., Wang, C., Zhan, X., Zheng, H., & Che, C. (2024). Enhancing 3D Object Detection by Using Neural Network with Self-adaptive Thresholding. arXiv preprint arXiv:2405.07479.
6. Huang, Z., Che, C., Zheng, H., & Li, C. (2024). Research on Generative Artificial Intelligence for Virtual Financial Robo-Advisor. Academic Journal of Science and Technology, 10(1), 74-80.
7. Che, C., Lin, Q., Zhao, X., Huang, J., & Yu, L. (2023, September). Enhancing Multimodal Understanding with CLIP-Based Image-to-Text Transformation. In Proceedings of the 2023 6th International Conference on Big Data Technologies (pp. 414-418).

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2024 The Author(s). Published by Southern United Academy of Sciences.
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