Unmanned Aerial Vehicle in the Machine Learning Environment

被引:26
作者
Khan, Asharul Islam [1 ]
Al-Mulla, Yaseen [1 ,2 ]
机构
[1] Sultan Qaboos Univ, Remote Sensing & GIS Res Ctr, POB 33, Muscat 123, Oman
[2] Sultan Qaboos Univ, Dept Soils Water & Agr Engn, POB 33, Muscat 123, Oman
来源
10TH INT CONF ON EMERGING UBIQUITOUS SYST AND PERVAS NETWORKS (EUSPN-2019) / THE 9TH INT CONF ON CURRENT AND FUTURE TRENDS OF INFORMAT AND COMMUN TECHNOLOGIES IN HEALTHCARE (ICTH-2019) / AFFILIATED WORKOPS | 2019年 / 160卷
关键词
unmanned aerial vehicle; drone; machine learning; deep learning; object detection; pattern recognition; neural network; SPECTROSCOPY;
D O I
10.1016/j.procs.2019.09.442
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Unmanned Aerial Vehicles and machine learning have started gaining attentions of academic and industrial research. The Unmanned Aerial Vehicles have extended the freedom to operate and monitor the activities from remote locations. This study retrieved and synthesized research on the use of Unmanned Aerial Vehicles along with machine learning and its algorithms in different areas and regions. The objective was to synthesize the scope and importance of machine learning models in enhancing Unmanned Aerial Vehicles capabilities, solutions to problems, and numerous application areas. The machine learning implementation has reduced numbers of challenges to Unmanned Aerial Vehicles besides enhancing the capabilities and opening the door to the different sectors. The Unmanned Aerial Vehicles and machine learning association has resulted in fast and reliable outputs. The combination of Unmanned Aerial Vehicles and machine learning has helped in real time monitoring, data collection and processing, and prediction in the computer/wireless networks, smart cities, military, agriculture, and mining. (C) 2019 The Authors. Published by Elsevier B.V.
引用
收藏
页码:46 / 53
页数:8
相关论文
共 29 条
[1]  
Alipour-Fanid Amir, 2019, ARXIV190506396
[2]  
[Anonymous], 2019, ARXIV190107703
[3]  
Bejo S.K., 2014, Journal of food science and engineering, V4, P1, DOI DOI 10.17265/2159-5828/2014.01.001
[4]  
Berezovskaya F, 2019, STEAM H SCI TECH ENG, P1, DOI 10.1007/978-3-030-15715-9_1
[5]   Liquid State Machine Learning for Resource and Cache Management in LTE-U Unmanned Aerial Vehicle (UAV) Networks [J].
Chen, Mingzhe ;
Saad, Walid ;
Yin, Changchuan .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2019, 18 (03) :1504-1517
[6]  
Damgaard Christian, 2018, BIORXIV 2018
[7]   Rapid prediction of total petroleum hydrocarbons concentration in contaminated soil using vis-NIR spectroscopy and regression techniques [J].
Douglas, R. K. ;
Nawar, S. ;
Alamar, M. C. ;
Mouazen, A. M. ;
Coulon, F. .
SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 616 :147-155
[8]   Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring [J].
Ge, Xiangyu ;
Wang, Jingzhe ;
Ding, Jianli ;
Cao, Xiaoyi ;
Zhang, Zipeng ;
Liu, Jie ;
Li, Xiaohang .
PEERJ, 2019, 7
[9]   Random Forests for land cover classification [J].
Gislason, PO ;
Benediktsson, JA ;
Sveinsson, JR .
PATTERN RECOGNITION LETTERS, 2006, 27 (04) :294-300
[10]  
Jonsson Sigurbjorn, 2019, Student thesis series