A novel one-dimensional cnn with exponential adaptive gradients for air pollution index prediction
Air pollution is one of the world's most significant challenges. Predicting air pollution is
critical for air quality research, as it affects public health. The Air Pollution Index (API) is a …
critical for air quality research, as it affects public health. The Air Pollution Index (API) is a …
Particle swarm optimization of partitions and fuzzy order for fuzzy time series forecasting of COVID-19
Major hyperparameters which affect fuzzy time series (FTS) forecasting are the number of
partitions, length of partition intervals in the universe of discourse, and the fuzzy order. There …
partitions, length of partition intervals in the universe of discourse, and the fuzzy order. There …
Supervised Machine Learning Approaches for Predicting Key Pollutants and for the Sustainable Enhancement of Urban Air Quality: A Systematic Review
Urban air pollution is a pressing global issue driven by factors such as swift urbanization,
population expansion, and heightened industrial activities. To address this challenge, the …
population expansion, and heightened industrial activities. To address this challenge, the …
A performance comparison study on PM2. 5 prediction at industrial areas using different training algorithms of feedforward-backpropagation neural network (FBNN)
P Chinatamby, J Jewaratnam - Chemosphere, 2023 - Elsevier
Presence of particulate matters with aerodynamic diameter of less than 2.5 μm (PM 2.5) in
the atmosphere is fast increasing in Malaysia due to industrialization and urbanization …
the atmosphere is fast increasing in Malaysia due to industrialization and urbanization …
Improved seagull optimization algorithm of partition and XGBoost of prediction for fuzzy time series forecasting of COVID-19 daily confirmed
S ** a fuzzy time series (FTS) algorithm. The MWTS has …
[HTML][HTML] A new hybrid fuzzy time series model with an application to predict PM10 concentration
Fuzzy time series (FTS) forecasting models show a great performance in predicting time
series, such as air pollution time series. However, they have caused major issues by utilizing …
series, such as air pollution time series. However, they have caused major issues by utilizing …
[PDF][PDF] Time series-based quantitative risk models: enhancing accuracy in forecasting and risk assessment
O Olukoya - … Journal of Computer Applications Technology and …, 2023 - researchgate.net
In an increasingly complex financial and operational landscape, accurate forecasting and
robust risk assessment are critical for organizational resilience and decision-making. Time …
robust risk assessment are critical for organizational resilience and decision-making. Time …
Bayesian network based probabilistic weighted high-order fuzzy time series forecasting
B Wang, X Liu, M Chi, Y Li - Expert Systems with Applications, 2024 - Elsevier
The present article proposes a probabilistic weighted high-order fuzzy time series (FTS)
forecasting model employing Bayesian network (BN) to address complex relationships and …
forecasting model employing Bayesian network (BN) to address complex relationships and …
Short-term air pollution prediction using graph convolutional neural networks
Pollution is a major concern in the present day, causing multiple illnesses and deaths,
specifically in develo** countries in Asia and Africa. While it has drawn worldwide …
specifically in develo** countries in Asia and Africa. While it has drawn worldwide …