Advancing Asthma Management: Recurrent Neural Networks (RNNS) and Nanosensors for Precision Forecasting of Indoor Air Pollution

被引:0
|
作者
Higgs, V. [1 ]
Ahmed, H. [1 ]
Flavier, J. [1 ]
机构
[1] Appl Nanodetectors Ltd, London, England
关键词
D O I
暂无
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
A1381
引用
收藏
页数:1
相关论文
共 35 条
  • [31] Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India
    Pande, Chaitanya Baliram
    Kushwaha, Nand Lal
    Alawi, Omer A.
    Sammen, Saad Sh
    Sidek, Lariyah Mohd
    Yaseen, Zaher Mundher
    Pal, Subodh Chandra
    Katipoglu, Okan Mert
    ENVIRONMENTAL POLLUTION, 2024, 351
  • [32] Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation
    Feng, Xiao
    Li, Qi
    Zhu, Yajie
    Hou, Junxiong
    Jin, Lingyan
    Wang, Jingjie
    ATMOSPHERIC ENVIRONMENT, 2015, 107 : 118 - 128
  • [33] Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain)
    Gonzalez-Enrique, Javier
    Ruiz-Aguilar, Juan Jesus
    Moscoso-Lopez, Jose Antonio
    Urda, Daniel
    Deka, Lipika
    Turias, Ignacio J.
    SENSORS, 2021, 21 (05) : 1 - 30
  • [34] Soft sensor validation for monitoring and resilient control of sequential subway indoor air quality through memory-gated recurrent neural networks-based autoencoders
    Loy-Benitez, Jorge
    Heo, SungKu
    Yoo, ChangKyoo
    CONTROL ENGINEERING PRACTICE, 2020, 97
  • [35] Data-driven forecasting with model uncertainty of utility-scale air-cooled condenser performance using ensemble encoder-decoder mixture-density recurrent neural networks
    Raidoo, Renita
    Laubscher, Ryno
    ENERGY, 2022, 238