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IoT 22(29): e3

Research Article

A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation

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  • @ARTICLE{10.4108/eetiot.v8i29.987,
        author={Wasswa Shafik and S. Mojtaba Matinkhah and Fawad Shokoor and Lule Sharif},
        title={A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation},
        journal={EAI Endorsed Transactions on Internet of Things},
        volume={8},
        number={29},
        publisher={EAI},
        journal_a={IOT},
        year={2022},
        month={5},
        keywords={Machine learning, Reinforcement learning, supervised learning, non-supervised learning, semi-supervised learning},
        doi={10.4108/eetiot.v8i29.987}
    }
    
  • Wasswa Shafik
    S. Mojtaba Matinkhah
    Fawad Shokoor
    Lule Sharif
    Year: 2022
    A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation
    IOT
    EAI
    DOI: 10.4108/eetiot.v8i29.987
Wasswa Shafik1, S. Mojtaba Matinkhah1,*, Fawad Shokoor1, Lule Sharif2
  • 1: Yazd University
  • 2: Islamic University in Uganda
*Contact email: Matinkhah@yazd.ac.ir

Abstract

Machine learning (ML) entails artificial procedures that improve robotically through experience and using data. Supervised, unsupervised, semi-supervised, and Reinforcement Learning (RL) are the main types of ML. This study mainly focuses on RL and Deep learning, since necessitates mainly sequential and consecutive decision-making context. This is a comparison to supervised and non-supervised learning due to the interactive nature of the environment. Exploiting a forthcoming accumulative compensation and its stimulus of machines, complex policy decisions. The study further analyses and presents ML perspectives depicting state-of-the-art developments with advancement, relatively depicting the future trend of RL based on its applicability in technology. It's a challenge to an Internet of Things (IoT) and demonstrates what possibly can be adopted as a solution. This study presented a summarized perspective on identified arenas on the analysis of RL. The study scrutinized that a reasonable number of the techniques engrossed in alternating policy values instead of modifying other gears in an exact state of intellectual. The study presented a strong foundation for the current studies to be adopted by the researchers from different research backgrounds to develop models, and architectures that are relevant.

Keywords
Machine learning, Reinforcement learning, supervised learning, non-supervised learning, semi-supervised learning
Received
2022-03-25
Accepted
2022-05-13
Published
2022-05-16
Publisher
EAI
http://dx.doi.org/10.4108/eetiot.v8i29.987

Copyright © 2022 Wasswa Shafik et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.

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