Evolving 1D Convolutional Neural Networks for Human Activity Recognition

Tsokov, Stefan; Lazarova, Milena; Aleksieva-Petrova, Adelina

doi:10.1145/3472410.3472413

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…Tsokov et al 21 proposed a hybrid spatiotemporal deep model based on CNN and LSTM for air pollution prediction, demonstrating its effectiveness in capturing both spatial and temporal patterns in air quality data. Moursi et al 22 enhanced PM2.5 prediction using a NARX-based combined CNN and LSTM hybrid model, showing improved forecasting accuracy.Alkabbani et al 23 introduced an improved air quality index machine learning-based forecasting model with a multivariate data imputation approach, highlighting its potential in addressing missing data issues.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning algorithms for air pollution forecasting: an overview of recent developments

Shu,

Song,

Guo

et al. 2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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Air pollution is a major environmental issue that affects human health and the environment. In recent years, deep learning has been applied to the prediction of air pollution expansion with promising results. This paper provides a comprehensive review of the recent literature on the application of deep learning related algorithms to the prediction of pollution expansion. The paper focuses on the use of deep learning models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Hybrid models for air pollution forecasting. The literature review covers studies published between 2018 and 2023, and includes articles from various journals with high impact factors. The results of the reviewed studies show that deep learning models have outperformed traditional statistical models in terms of accuracy and robustness for air pollution forecasting. The paper concludes by highlighting the challenges and opportunities for further research in this area.

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Section: Literature Reviewmentioning

confidence: 99%