Tópico: artificial neural networks
Tópico: Air quality
Autor: López Gonzales, Javier Linkolk
- A Self-Organizing Topological Multilayer Perceptron for Improving the Prediction of Extreme Values of the PM2.5
- Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector
- Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru
- Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru
- A Spatio-Temporal Visualization Approach of PM10 Concentration Data in Metropolitan Lima
- Air Quality Prediction Based on Singular Spectrum Analysis and Artificial Neural Networks
Autor: Torres, Romina
- A Self-Organizing Topological Multilayer Perceptron for Improving the Prediction of Extreme Values of the PM2.5
- Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru
- Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru
- Self-Organizing Topological Multilayer Perceptron: A Hybrid Method to Improve the Forecasting of Extreme Pollution Values
- Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University
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- Predictive Model with Machine Learning for Environmental Variables and PM2.5 in Huachac, Junín, Perú
- A predictive approach to teaching quality transition and improvement in higher education
- An exploratory analysis of PM2.5/PM10 ratio during spring 2016–2018 in Metropolitan Lima
- Linear Regression with PM2.5 and PM10 Concentration for Air Quality in East Lima, Peru
- Extreme sports
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- Electricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learning
- Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique
- Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method
- Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector