Modeling and Prediction of Hourly Ambient Ozone (O3) and Oxides of Nitrogen (NOx) Concentrations Using Artificial Neural Network and Decision Tree Algorithms for an Urban Intersection in India

Decision Tree ◽

Oxides Of Nitrogen ◽

Ambient Ozone ◽

Modeling And Prediction ◽

Tree Algorithms ◽

Discussion of “Modeling and Prediction of Hourly Ambient Ozone (O3) and Oxides of Nitrogen (NOx) Concentrations Using Artificial Neural Network and Decision Tree Algorithms for an Urban Intersection in India” by Chandrra Sekar, C. S. P. Ojha, B. R. Gurjar, and Manish Kumar Goyal

Journal of Hazardous Toxic and Radioactive Waste ◽

10.1061/(asce)hz.2153-5515.0000313 ◽

2016 ◽

Vol 20 (3) ◽

pp. 07016001

Author(s):

Taher Rajaee ◽

Salar Khani

Keyword(s):

Neural Network ◽

Decision Tree ◽

Oxides Of Nitrogen ◽

Ambient Ozone ◽

Modeling And Prediction ◽

Tree Algorithms ◽

An artificial neural network supported stochastic process for degradation modeling and prediction

Reliability Engineering & System Safety ◽

10.1016/j.ress.2021.107738 ◽

2021 ◽

pp. 107738

Author(s):

Di Liu ◽

Shaoping Wang

Keyword(s):

Neural Network ◽

Stochastic Process ◽

Degradation Modeling ◽

Modeling And Prediction ◽

Modeling and prediction of activity coefficient ratio of electrolytes in aqueous electrolyte solution containing amino acids using artificial neural network

Fluid Phase Equilibria ◽

10.1016/j.fluid.2006.04.003 ◽

2006 ◽

Vol 244 (2) ◽

pp. 153-159 ◽

Cited By ~ 19

Author(s):

M.R. Dehghani ◽

H. Modarress ◽

A. Bakhshi

Keyword(s):

Neural Network ◽

Amino Acids ◽

Activity Coefficient ◽

Electrolyte Solution ◽

Aqueous Electrolyte ◽

Aqueous Electrolyte Solution ◽

Modeling And Prediction ◽

Activity Coefficient Ratio ◽

Applicability of artificial neural network for modeling and prediction of the laser polished surface quality

10.1364/cleo_at.2021.ath4p.3 ◽

2021 ◽

Author(s):

Honghe Wu ◽

Evgueni V. Bordatchev

Keyword(s):

Neural Network ◽

Surface Quality ◽

Polished Surface ◽

Modeling And Prediction ◽

Rising Threats in Expert Applications and Solutions - Advances in Intelligent Systems and Computing ◽

Composite Framework of Artificial Neural Network and Decision Tree Algorithm for Prediction of Fraudulent Firm Using Audit Dataset

10.1007/978-981-15-6014-9_64 ◽

2020 ◽

pp. 543-547

Author(s):

Monika Rathore ◽

Shruti Gupta

Keyword(s):

Neural Network ◽

Decision Tree ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Artificial Neural Networks and Statistical Pattern Recognition - Old and New Connections - Machine Intelligence and Pattern Recognition ◽

Decision tree performance enhancement using an artificial neural network implementation1 1This work was supported in part by NSF grant IRI-9002087

10.1016/b978-0-444-88740-5.50010-4 ◽

1991 ◽

pp. 71-88 ◽

Cited By ~ 11

Author(s):

Ishwar K. Sethi

Keyword(s):

Neural Network ◽

Decision Tree ◽

Performance Enhancement ◽

Artificial Neural ◽

Tree Performance

Importance of feature selection in decision-tree and artificial-neural-network ecological applications. Alburnus alburnus alborella: A practical example

Ecological Informatics ◽

10.1016/j.ecoinf.2010.11.001 ◽

2010 ◽

Cited By ~ 7

Author(s):

Tina Tirelli ◽

Daniela Pessani

Keyword(s):

Neural Network ◽

Feature Selection ◽

Decision Tree ◽

Ecological Applications ◽

Prediction of variation of oxides of nitrogen in plasma-based diesel exhaust treatment using artificial neural network

International Journal of Environmental Science and Technology ◽

10.1007/s13762-019-02242-5 ◽

2019 ◽

Vol 16 (10) ◽

pp. 6315-6328 ◽

Cited By ~ 2

Author(s):

D. S. Mukherjee ◽

B. S. Rajanikanth

Keyword(s):

Neural Network ◽

Diesel Exhaust ◽

Oxides Of Nitrogen ◽

Exhaust Treatment ◽

Hydrogen solubility in heavy n-alkanes; modeling and prediction by artificial neural network

Fluid Phase Equilibria ◽

10.1016/j.fluid.2011.08.004 ◽

2011 ◽

Vol 310 (1-2) ◽

pp. 150-155 ◽

Cited By ~ 16

Author(s):

Mani Safamirzaei ◽

Hamid Modarress

Keyword(s):

Neural Network ◽

Hydrogen Solubility ◽

Modeling And Prediction ◽

Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method

Expert Systems with Applications ◽

10.1016/j.eswa.2010.11.041 ◽

2011 ◽

Vol 38 (5) ◽

pp. 5826-5832 ◽

Cited By ~ 183

Author(s):

İlhan Asiltürk ◽

Mehmet Çunkaş

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Multiple Regression ◽

Regression Method ◽

Turning Operations ◽

Modeling And Prediction ◽

Multiple Regression Method ◽