International Journal of Intelligence Science

International Journal of Intelligence Science

ISSN Print: 2163-0283
ISSN Online: 2163-0356
www.scirp.org/journal/ijis
E-mail: ijis@scirp.org
"Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach"
written by Artemio Sotomayor-Olmedo, Marco A. Aceves-Fernández, Efrén Gorrostieta-Hurtado, Carlos Pedraza-Ortega, Juan M. Ramos-Arreguín, J. Emilio Vargas-Soto,
published by International Journal of Intelligence Science, Vol.3 No.3, 2013
has been cited by the following article(s):
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[6] Use of Biomass Fuels for Cooking and Improved Biomass Stoves in Mexico
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[15] Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives
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[16] The Environmental Story During the COVID-19 Lockdown: How Human Activities Affect PM2. 5 Concentration in China?
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[17] Chemometrics for environmental monitoring: a review
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[18] Source number estimation based on a novel multi-view meta-hierarchical classification framework
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[19] A Machine Learning Approach to Predict Air Quality in California
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[20] Fundamental research
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[22] Propuesta de red neuronal convolutiva para la predicción de partículas contaminantes PM10.
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[24] Support Vector Machine (SVM) aggregation modelling for spatio-temporal air pollution analysis
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[25] Support vector regression for time-series: a machine learning approach to predict the air quality
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[27] Multi-Level Air Quality Classification in China Using Information Gain and Support Vector Machine Hybrid Model.
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[29] Air Pollution Prediction Using Machine Learning
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[30] The Hybrid Neural Networks-ARIMA/X Models and ANFIS Model for PM-10 Forecasting: A Case Study of Chiang Mai, Thailand's High Season
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[31] Multi-Level Air Quality Classification in China Using Information Gain and Support Vector Machine
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[32] The Forecasting Technique Using SSA-SVM Applied to Foreign Tourist Arrivals to Bali.
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[33] Improving air quality management using gradient boosting based hierarchical temporal memory neural networks and fuzzy based classification based …
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[34] The Forecasting Technique Using SSA-SVM Applied to Foreign Tourist Arrivals to Bali
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[35] Implementación de un modelo del comportamiento de los niveles de concentración de PM10 utilizando herramientas de aprendizaje de máquina en la ciudad …
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[37] Classification trees and PM10 dynamics in Bogotá, Colombia
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[38] A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States
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[39] ONLINE SCALABLE SVM ENSEMBLE LEARNING METHOD (OSSELM) FOR SPATIO-TEMPORAL AIR POLLUTION ANALYSIS
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[40] Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies
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[41] Using geosocial search for urban air pollution monitoring
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[42] Statistical Modeling Approaches for PM10 Prediction in Urban Areas
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[43] Pervasive and Mobile Computing
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[44] Modelling atmospheric ozone concentration using machine learning algorithms
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[45] Enhancement of a Neuro-Fuzzy Models Using Ant Colony Optimization for the Prediction level of CO Pollution
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[46] Comparison of passive microwave brightness temperature prediction sensitivities over snow-covered land in North America using machine learning algorithms and …
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[47] Comparison of passive microwave brightness temperature prediction sensitivities over snow-covered land in North America using machine learning algorithms and …
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[48] Prediction models for ozone in metropolitan area of Mexico City based on artificial intelligence techniques
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[50] Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms
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[51] PM10 Parametresinin Makine Öğrenmesi Algoritmalari ile Mekânsal Analizi, Kayseri İli Örneği
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