Version 1
: Received: 23 June 2023 / Approved: 27 June 2023 / Online: 27 June 2023 (12:27:38 CEST)
How to cite:
Haque, A.; Chowdhury, M.N.; Hassanalian, M. A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology. Preprints2023, 2023061901. https://doi.org/10.20944/preprints202306.1901.v1
Haque, A.; Chowdhury, M.N.; Hassanalian, M. A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology. Preprints 2023, 2023061901. https://doi.org/10.20944/preprints202306.1901.v1
Haque, A.; Chowdhury, M.N.; Hassanalian, M. A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology. Preprints2023, 2023061901. https://doi.org/10.20944/preprints202306.1901.v1
APA Style
Haque, A., Chowdhury, M.N., & Hassanalian, M. (2023). A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology. Preprints. https://doi.org/10.20944/preprints202306.1901.v1
Chicago/Turabian Style
Haque, A., Md Naseef-Ur-Rahman Chowdhury and Mostafa Hassanalian. 2023 "A Comprehensive Review of Classification and Application of Machine Learning in Drone Technology" Preprints. https://doi.org/10.20944/preprints202306.1901.v1
Abstract
The use of drones for various applications has become increasingly popular in recent years, and machine learning has played a significant role in this trend. In this paper, we provide a comprehensive survey of the classification and application of machine learning in drones. The paper begins with an overview of the different types of machine learning algorithms and their applications in drones, including supervised learning, unsupervised learning, and reinforcement learning. Next, we present a detailed analysis of various real-world applications of machine learning in drones, such as object recognition, route planning, obstacle avoidance, search area optimization, and autonomous search. The paper also discusses the challenges and limitations of using machine learning in drones, such as data privacy, data quality, and computational requirements. Finally, the paper concludes with a discussion of the future directions of machine learning in drones and its potential impact on various industries and fields. This paper provides a valuable resource for researchers, practitioners, and students interested in the intersection of machine learning and drones.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
