An Artificial Intelligence Application for Drone-Assisted 5G Remote e-Health

Article Properties
  • DOI (url)
    10.1109/iotm.001.2100078
  • Publication Date
    2021/12/01
  • Journal
    IEEE Internet of Things Magazine
  • Indian UGC (journal)
  • Refrences
    15
  • Citations
    1
  • Naercio Magaia COPELABS/Universidade Lusófona,Portugal
  • Igor de L. Ribeiro University of Fortaleza,Brazil
  • Andre W. O. de Aguiar University of Fortaleza,Brazil
  • Ramon Fonseca Federal University of Ceará,Brazil
  • Khan Muhammad Sungkyunkwan University,Republic of Korea
  • Victor Hugo C. de Albuquerque Federal University of Ceará,Brazil
Cite
Magaia, Naercio, et al. “An Artificial Intelligence Application for Drone-Assisted 5G Remote E-Health”. IEEE Internet of Things Magazine, vol. 4, no. 4, 2021, pp. 30-35, https://doi.org/10.1109/iotm.001.2100078.
Magaia, N., Ribeiro, I. de L., de Aguiar, A. W. O., Fonseca, R., Muhammad, K., & de Albuquerque, V. H. C. (2021). An Artificial Intelligence Application for Drone-Assisted 5G Remote e-Health. IEEE Internet of Things Magazine, 4(4), 30-35. https://doi.org/10.1109/iotm.001.2100078
Magaia, Naercio, Igor de L. Ribeiro, Andre W. O. de Aguiar, Ramon Fonseca, Khan Muhammad, and Victor Hugo C. de Albuquerque. “An Artificial Intelligence Application for Drone-Assisted 5G Remote E-Health”. IEEE Internet of Things Magazine 4, no. 4 (2021): 30-35. https://doi.org/10.1109/iotm.001.2100078.
Magaia N, Ribeiro I de L, de Aguiar AWO, Fonseca R, Muhammad K, de Albuquerque VHC. An Artificial Intelligence Application for Drone-Assisted 5G Remote e-Health. IEEE Internet of Things Magazine. 2021;4(4):30-5.
Refrences
Title Journal Journal Categories Citations Publication Date
A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications IEEE Sensors Journal
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Science: Mathematics: Instruments and machines
  • Science: Physics
  • Technology: Engineering (General). Civil engineering (General)
 2 2021
10.1109/JIOT.2018.2850664
10.1109/TBME.2018.2871638
10.1109/ACCESS.2019.2922442
10.1109/TIFS.2018.2885287
Citations
Title Journal Journal Categories Citations Publication Date
Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model

 Computational Intelligence and Neuroscience
  • Medicine: Medicine (General): Computer applications to medicine. Medical informatics
  • Science: Biology (General)
  • Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
  • Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry
 2022
Citations Analysis
Category Category Repetition
Medicine: Medicine (General): Computer applications to medicine. Medical informatics1
Science: Biology (General)1
Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry1
The category Medicine: Medicine (General): Computer applications to medicine. Medical informatics 1 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model and was published in 2022. The most recent citation comes from a 2022 study titled Construction of Swimmer's Underwater Posture Training Model Based on Multimodal Neural Network Model. This article reached its peak citation in 2022, with 1 citations. It has been cited in 1 different journals. Among related journals, the Computational Intelligence and Neuroscience cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year