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[HTML][HTML] Air quality prediction in smart cities using machine learning technologies based on sensor data: a review
The influence of machine learning technologies is rapidly increasing and penetrating almost
in every field, and air pollution prediction is not being excluded from those fields. This paper …
in every field, and air pollution prediction is not being excluded from those fields. This paper …
Air quality prediction using CT-LSTM
J Wang, J Li, X Wang, J Wang, M Huang - Neural Computing and …, 2021 - Springer
With the development of industry, air pollution has become a serious problem. It is very
important to create an air quality prediction model with high accuracy and good …
important to create an air quality prediction model with high accuracy and good …
Prediction of water quality index (WQI) using support vector machine (SVM) and least square-support vector machine (LS-SVM)
The current calculations of water quality index (WQI) were sometimes can be very complex
and time-consuming which involves sub-index calculation like BOD and COD, however with …
and time-consuming which involves sub-index calculation like BOD and COD, however with …
Prediction of air pollution index (API) using support vector machine (SVM)
WC Leong, RO Kelani, Z Ahmad - Journal of Environmental Chemical …, 2020 - Elsevier
The existing methods of calculating air pollution index are complex and time consuming.
Therefore new accurate and efficient modeling techniques need to be proposed. Thus, a …
Therefore new accurate and efficient modeling techniques need to be proposed. Thus, a …
A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction
This study investigates the applicability of multilayer perceptron (MLP), radial basis function
(RBF) and support vector machine (SVM) models for prediction of river flow time series …
(RBF) and support vector machine (SVM) models for prediction of river flow time series …
Coronary heart disease diagnosis through self-organizing map and fuzzy support vector machine with incremental updates
The trade-off between computation time and predictive accuracy is important in the design
and implementation of clinical decision support systems. Machine learning techniques with …
and implementation of clinical decision support systems. Machine learning techniques with …
Air quality early-warning system for cities in China
Y Xu, W Yang, J Wang - Atmospheric Environment, 2017 - Elsevier
Air pollution has become a serious issue in many develo** countries, especially in China,
and could generate adverse effects on human beings. Air quality early-warning systems play …
and could generate adverse effects on human beings. Air quality early-warning systems play …
Machine learning for microalgae detection and utilization
H Ning, R Li, T Zhou - Frontiers in Marine Science, 2022 - frontiersin.org
Microalgae are essential parts of marine ecology, and they play a key role in species
balance. Microalgae also have significant economic value. However, microalgae are too …
balance. Microalgae also have significant economic value. However, microalgae are too …
Modeling stage–discharge–sediment using support vector machine and artificial neural network coupled with wavelet transform
Many real water issues involve rivers' sediment load or the load that rivers can bring without
degrading the fluvial ecosystem. Therefore, the assessment of sediments carried by a river is …
degrading the fluvial ecosystem. Therefore, the assessment of sediments carried by a river is …
[HTML][HTML] Machine learning methods to predict cadmium (Cd) concentration in rice grain and support soil management at a regional scale
Rice is a major dietary source of the toxic metal cadmium (Cd). Concentration of Cd in rice
grain varies widely at the regional scale, and it is challenging to predict grain Cd …
grain varies widely at the regional scale, and it is challenging to predict grain Cd …