Comment on “Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations A. Šiljić & D. Antanasijević & A. Perić-Grujić & M. Ristić & V. Pocajt. Environ Sci Pollut Res (2014) 22: 4230-4241”

Taher Rajaee; Salar Khani

doi:10.1007/s11356-015-4665-6

Comment on “Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations A. Šiljić & D. Antanasijević & A. Perić-Grujić & M. Ristić & V. Pocajt. Environ Sci Pollut Res (2014) 22: 4230-4241”

Environmental Science and Pollution Research ◽

10.1007/s11356-015-4665-6 ◽

2015 ◽

Vol 22 (23) ◽

pp. 19313-19314

Author(s):

Taher Rajaee ◽

Salar Khani

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Monte Carlo ◽

Monte Carlo Simulations ◽

National Level ◽

Oxygen Demand ◽

Biological Oxygen Demand ◽

Network Modelling ◽

Input Selection ◽

Artificial Neural Network Modelling

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Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations

Environmental Science and Pollution Research ◽

10.1007/s11356-014-3669-y ◽

2014 ◽

Vol 22 (6) ◽

pp. 4230-4241 ◽

Cited By ~ 13

Author(s):

Aleksandra Šiljić ◽

Davor Antanasijević ◽

Aleksandra Perić-Grujić ◽

Mirjana Ristić ◽

Viktor Pocajt

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Monte Carlo ◽

Monte Carlo Simulations ◽

National Level ◽

Oxygen Demand ◽

Biological Oxygen Demand ◽

Network Modelling ◽

Input Selection ◽

Artificial Neural Network Modelling

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Response to comment of Taher Rajaee and Salar Khani on “Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations” [Šiljić et al., Environ Sci Pollut Res (2015) 22: 4230-4241]

Environmental Science and Pollution Research ◽

10.1007/s11356-015-5978-1 ◽

2015 ◽

Vol 23 (4) ◽

pp. 3978-3979 ◽

Cited By ~ 1

Author(s):

Aleksandra Šiljić Tomić ◽

Davor Antanasijević ◽

Aleksandra Perić-Grujić ◽

Mirjana Ristić ◽

Viktor Pocajt

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Monte Carlo ◽

Monte Carlo Simulations ◽

National Level ◽

Oxygen Demand ◽

Biological Oxygen Demand ◽

Network Modelling ◽

Input Selection ◽

Artificial Neural Network Modelling

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Artificial Neural Network Modelling of Biochemical Oxygen Demand and Dissolved Oxygen of Rivers: Case Study of Asa River

Environmental Research Engineering and Management ◽

10.5755/j01.erem.72.3.14120 ◽

2017 ◽

Vol 72 (3) ◽

Author(s):

Kamoru Akanni Adeniran ◽

Bashir Adelodun ◽

Matthew Ogunshina

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Dissolved Oxygen ◽

Biochemical Oxygen Demand ◽

Oxygen Demand ◽

Network Modelling ◽

Neural Network Modelling ◽

Artificial Neural ◽

Artificial Neural Network Modelling

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Artificial neural network modelling for biodecolorization of Basic Violet 03 from aqueous solution by biochar derived from agro-bio waste of groundnut hull: Kinetics and thermodynamics

Chemosphere ◽

10.1016/j.chemosphere.2021.130191 ◽

2021 ◽

Vol 276 ◽

pp. 130191

Author(s):

Saravanan Praveen ◽

Josephraj Jegan ◽

Thillainayagam Bhagavathi Pushpa ◽

Ravindiran Gokulan

Keyword(s):

Neural Network ◽

Aqueous Solution ◽

Artificial Neural Network ◽

Network Modelling ◽

Kinetics And Thermodynamics ◽

Neural Network Modelling ◽

Artificial Neural ◽

Artificial Neural Network Modelling

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Support Vector Machine and Artificial Neural Network Modelling of Orogenic Gold Prospectivity Mapping in the Swayze greenstone belt, Ontario, Canada

Ore Geology Reviews ◽

10.1016/j.oregeorev.2020.103968 ◽

2020 ◽

pp. 103968

Author(s):

Francisca Maepa ◽

Richard S. Smith ◽

Abera Tessema

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Greenstone Belt ◽

Orogenic Gold ◽

Support Vector ◽

Network Modelling ◽

Neural Network Modelling ◽

Artificial Neural Network Modelling ◽

Prospectivity Mapping

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Identification of gene transcript signatures predictive for estrogen receptor and lymph node status using a stepwise forward selection artificial neural network modelling approach

Artificial Intelligence in Medicine ◽

10.1016/j.artmed.2008.03.001 ◽

2008 ◽

Vol 43 (2) ◽

pp. 99-111 ◽

Cited By ~ 36

Author(s):

Lee J. Lancashire ◽

Robert C. Rees ◽

Graham R. Ball

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Estrogen Receptor ◽

Lymph Node ◽

Lymph Node Status ◽

Gene Transcript ◽

Forward Selection ◽

Network Modelling ◽

Artificial Neural Network Modelling ◽

Modelling Approach

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Artificial Neural Network Modelling for Short Term Load Forecasting

10.1109/ict-pep53949.2021.9601105 ◽

2021 ◽

Author(s):

Mahshooq Abdul Majeed ◽

Soumya Mudgal ◽

Lalit Tak ◽

Janavi Popat ◽

Harsh Kakadiya ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Load Forecasting ◽

Short Term ◽

Network Modelling ◽

Neural Network Modelling ◽

Short Term Load Forecasting ◽

Artificial Neural ◽

Artificial Neural Network Modelling

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Artificial neural network modelling of Cr(VI) surface adsorption with NiO nanoparticles using the results obtained from optimization of response surface methodology

Neural Computing and Applications ◽

10.1007/s00521-017-3172-8 ◽

2017 ◽

Vol 29 (10) ◽

pp. 969-979 ◽

Cited By ~ 3

Author(s):

Saber Khodaei Ashan ◽

Nasim Ziaeifar ◽

Rana Khalilnezhad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Response Surface Methodology ◽

Response Surface ◽

Surface Adsorption ◽

Nio Nanoparticles ◽

Network Modelling ◽

Neural Network Modelling ◽

Artificial Neural ◽

Artificial Neural Network Modelling

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Estimations of Stress Concentration Factors (Cw/Kts) For Helical Circular/Square Cross Sectional Tension-Compression Springs And Artificial Neural Network Modelling

Journal of Polytechnic ◽

10.2339/politeknik.718550 ◽

2020 ◽

Author(s):

M. Tolga ÖZKAN ◽

İhsan TOKTAŞ ◽

Kaan DOĞANAY

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Stress Concentration ◽

Network Modelling ◽

Cross Sectional ◽

Neural Network Modelling ◽

Concentration Factors ◽

Compression Springs ◽

Stress Concentration Factors ◽

Artificial Neural Network Modelling

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Minimum fluidization velocities of binary Solid mixture – Empirical correlation and Genetic Algorithm‐Artificial Neural Network Modelling

Chemical Engineering & Technology ◽

10.1002/ceat.202100170 ◽

2021 ◽

Author(s):

Sudipta Let ◽

Nirjhar Bar ◽

Ranjan Kumar Basu ◽

Sudip Kumar Das

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Empirical Correlation ◽

Network Modelling ◽

Solid Mixture ◽

Neural Network Modelling ◽

Minimum Fluidization ◽

Artificial Neural ◽

Artificial Neural Network Modelling

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