A comparative study of the feed forward back propagation (FFBP) and layer recurrent (LR) neural network model for forecasting ground level ozone concentration

2016 ◽  
Vol 10 (2) ◽  
pp. 213-223 ◽  
Author(s):  
A. K. Gorai ◽  
Gargi Mitra
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Network Model ◽  
Neural Network Model ◽  
Ozone Concentration ◽  
Back Propagation ◽  
Ground Level ◽  
Feed Forward ◽  
Ground Level Ozone ◽  
Feed Forward Back Propagation

scholarly journals Prediction of Ozone Concentration using Feed Forward Back Propagation Neural Network (FFBP-NN)

2019 ◽  
Vol 8 (4) ◽  
pp. 9257-9260
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Ozone Concentration ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
City Council ◽  
Mitigation Measures ◽  
Coefficient Of Determination ◽  
Feed Forward ◽  
Feed Forward Back Propagation

Air pollution has been an ongoing problem in Malaysia. One of the major air quality issue in Malaysia is high concentrations of ozone in urban area. Rapid increase in vehicles number and fossil fuel consumption in Malaysia cause the emission of ozone and their precursors especially nitrogen oxides increasing sharply. This research focus on daytime and nighttime ozone concentration at Kuala Terengganu, Malaysia. The aim of this study is to predict ozone concentration using feed forward back propagation neural network (FFBP-NN) with two hidden layers. Five performance indicators were used to evaluate the models performances which are normalized absolute error (NAE), root mean squared error (RMSE), index of agreement (IA), prediction accuracy (PA) and coefficient of determination (R2 ). Result show that FFBP-NN with 2 hidden layers model gives good performance for prediction of ozone concentration with high accuracy measures (IA=0.9551, PA=0.8453, R2 =0.8402) and small error measures (NAE=0.1642, RMSE=4.4958) for daytime and nighttime (IA=0.9541, PA=0.8429, R2 =0.8358, NAE=0.2160, RMSE=3.2485). The result from this study provides a reference for city council to improve the existing guidelines and to plan an effective mitigation measures to monitor the status of air quality towards a sustainable environment.

Detection of DDoS attacks with feed forward based deep neural network model

2021 ◽  
Vol 169 ◽  
pp. 114520
Author(s):  
Abdullah Emir Cil ◽  
Kazim Yildiz ◽  
Ali Buldu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Ddos Attacks ◽  
Feed Forward

Two-layer tree-connected feed-forward neural network model for neural cryptography

2013 ◽  
Vol 87 (3) ◽  
Author(s):  
Xinyu Lei ◽  
Xiaofeng Liao ◽  
Fei Chen ◽  
Tingwen Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Feed Forward Neural Network ◽  
Feed Forward
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