Theoretical Understanding of Deep Learning in UAV Biomedical Engineering Technologies Analysis
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ORIGINAL RESEARCH
Theoretical Understanding of Deep Learning in UAV Biomedical Engineering Technologies Analysis Wasswa Shafik1 · S. Mojtaba Matinkhah1 · Mohammad Ghasemzadeh1 Received: 23 December 2019 / Accepted: 7 September 2020 © Springer Nature Singapore Pte Ltd 2020
Abstract The unmanned aerial vehicles (UAVs) emerged into a promising research trend within the recurrent year where current and future networks are to use enhanced connectivity in these digital immigrations in different fields like medical, communication, search, and rescue operations among others. The current technologies are using fixed base stations to operate on-site and off-site in the fixed position with its associated problems like poor connectivity. This opens gates for the UAVs technology to be used as a mobile alternative to increase accessibility with a fifth-generation (5G) connectivity that focuses on increased availability and connectivity. There has been less usage of wireless technologies in the medical field. This paper first presents a study on deep learning to medical field application in general, and provides detailed steps that are involved in the multi-armed bandit approach in solving UAV biomedical engineering technologies devices and medical exploration to exploitation dilemma. The paper further presents a detailed description of the bandit network applicability to achieve close optimal medical engineered devices’ performance and efficiency. The simulated results depicted that a multi-armed bandit problem approach can be applied in optimizing the performance of any medical networked device issue compared to the Thompson sampling, Bayesian algorithm, and ε-greedy algorithm. The results obtained further illustrated the optimized utilization of biomedical engineering technologies systems achieving thus close optimal performance on the average period through deep learning of realistic medical situations. Keywords Deep learning · Biomedical technology · Unmanned aerial vehicles
Introduction In the medical informatics, massive amounts of data have been produced. These data drive the development of the biomedical area studies in new conducts. Deep learning (DL) applications had experienced huge growth in medical image analysis as well as other related data because of the availability of many data sets to train the DL algorithms in multimodal modes. DL can identify patterns in healthcare data to improve diagnosis and prognosis. The most used DL techniques for healthcare applications are autoencoder, * Wasswa Shafik [email protected] S. Mojtaba Matinkhah [email protected] Mohammad Ghasemzadeh [email protected] 1
Computer Engineering Department, Yazd University, University Blvd , Safayieh, Yazd 98195 ‑ 741, Yazd, Iran
restricted Boltzmann machine, deep belief network, recurrent neural network, convolutional neural network, and generative adversarial network [1]. The DL techniques show an improved potential in learning patterns and extracting attributes from a complex dataset. Important examples include the u
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