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.