Utilization of Gene Expression Programming for Modeling of Mechanical Performance of Titanium/Carbonated Hydroxyapatite Nanobiocomposites: The Combination of Artificial Intelligence and Material Science

doi:10.5829/ije.2021.34.04a.21

Utilization of Gene Expression Programming for Modeling of Mechanical Performance of Titanium/Carbonated Hydroxyapatite Nanobiocomposites: The Combination of Artificial Intelligence and Material Science

International Journal of Engineering ◽

10.5829/ije.2021.34.04a.21 ◽

2021 ◽

Vol 34 (4) ◽

Keyword(s):

Gene Expression ◽

Artificial Intelligence ◽

Material Science ◽

Mechanical Performance ◽

Gene Expression Programming ◽

Carbonated Hydroxyapatite

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Defined an Optimized Molding for Physical and Mechanical Properties of W–Cu Nanocomposite Through Spark Plasma Sintering Using Gene Expression Programming: The Combination of Artificial Intelligence and Material Science

SN Computer Science ◽

10.1007/s42979-021-00901-4 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Mohammdreza Shojaei ◽

Gholam Reza Khayati

Keyword(s):

Gene Expression ◽

Artificial Intelligence ◽

Mechanical Properties ◽

Spark Plasma Sintering ◽

Material Science ◽

Gene Expression Programming ◽

Physical And Mechanical Properties ◽

Plasma Sintering ◽

Spark Plasma

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Development and validation of an artificial intelligence platform for characterization of the exergy-emission-stability profiles of the PPCI-RCCI regimes in a diesel-methanol operation under varying injection phasing strategies: A Gene Expression Programming approach

Fuel ◽

10.1016/j.fuel.2021.120864 ◽

2021 ◽

Vol 299 ◽

pp. 120864

Author(s):

Dipankar Kakati ◽

Sumit Roy ◽

Rahul Banerjee

Keyword(s):

Gene Expression ◽

Artificial Intelligence ◽

Gene Expression Programming ◽

Programming Approach ◽

Emission Stability ◽

Development And Validation

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Artificial intelligence based gene expression programming (GEP) model prediction of Diesel engine performances and exhaust emissions under Diesosenol fuel strategies

Fuel ◽

10.1016/j.fuel.2018.07.116 ◽

2019 ◽

Vol 235 ◽

pp. 317-325 ◽

Cited By ~ 12

Author(s):

Subrata Bhowmik ◽

Abhishek Paul ◽

Rajsekhar Panua ◽

Subrata Kumar Ghosh ◽

Durbadal Debroy

Keyword(s):

Gene Expression ◽

Artificial Intelligence ◽

Diesel Engine ◽

Model Prediction ◽

Gene Expression Programming ◽

Exhaust Emissions

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Artificial intelligence‐based model prediction of biodiesel‐fueled engine performance and emission characteristics: A comparative evaluation of gene expression programming and artificial neural network

Heat Transfer ◽

10.1002/htj.22138 ◽

2021 ◽

Author(s):

Prabhakar Sharma

Keyword(s):

Neural Network ◽

Gene Expression ◽

Artificial Intelligence ◽

Artificial Neural Network ◽

Model Prediction ◽

Comparative Evaluation ◽

Gene Expression Programming ◽

Engine Performance ◽

Emission Characteristics ◽

Performance And Emission

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Gene expression programming-based model prediction of performance and emission characteristics of a diesel engine fueled with linseed oil biodiesel/diesel blends: An artificial intelligence approach

Energy Sources Part A Recovery Utilization and Environmental Effects ◽

10.1080/15567036.2020.1829204 ◽

2020 ◽

pp. 1-15

Author(s):

Prabhakar Sharma

Keyword(s):

Gene Expression ◽

Artificial Intelligence ◽

Diesel Engine ◽

Model Prediction ◽

Linseed Oil ◽

Gene Expression Programming ◽

Emission Characteristics ◽

Performance And Emission ◽

Intelligence Approach ◽

Artificial Intelligence Approach

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Closure to “Gene-Expression Programming, Evolutionary Polynomial Regression, and Model Tree to Evaluate Local Scour Depth at Culvert Outlets” by Mohammad Najafzadeh and Ali Reza Kargar

Journal of Pipeline Systems Engineering and Practice ◽

10.1061/(asce)ps.1949-1204.0000533 ◽

2021 ◽

Vol 12 (2) ◽

pp. 07021002

Author(s):

Mohammad Najafzadeh ◽

Ali Reza Kargar

Keyword(s):

Gene Expression ◽

Polynomial Regression ◽

Gene Expression Programming ◽

Local Scour ◽

Scour Depth ◽

Evolutionary Polynomial Regression ◽

Model Tree

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Discussion of “Gene-Expression Programming, Evolutionary Polynomial Regression, and Model Tree to Evaluate Local Scour Depth at Culvert Outlets” by Mohammad Najafzadeh and Ali Reza Kargar

Journal of Pipeline Systems Engineering and Practice ◽

10.1061/(asce)ps.1949-1204.0000532 ◽

2021 ◽

Vol 12 (2) ◽

pp. 07021001

Author(s):

Manish Pandey ◽

H. Md Azamathulla

Keyword(s):

Gene Expression ◽

Polynomial Regression ◽

Gene Expression Programming ◽

Local Scour ◽

Scour Depth ◽

Evolutionary Polynomial Regression ◽

Model Tree

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APPLICATION OF GENE EXPRESSION PROGRAMMING FOR ESTIMATING TOTAL EMISSIVITY OF H2O-CO2 MIXTURES IN AIR-FUEL COMBUSTION WITHOUT SOOT FORMATION

Computational Thermal Sciences An International Journal ◽

10.1615/computthermalscien.2015011633 ◽

2015 ◽

Vol 7 (3) ◽

pp. 191-215

Author(s):

Babak Maghbooli ◽

Hamidreza Najafi ◽

Mohammad Amin Sobati

Keyword(s):

Gene Expression ◽

Soot Formation ◽

Gene Expression Programming ◽

Fuel Combustion ◽

Total Emissivity

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Gene Expression Programming Evolution Difficulty Prediction Based on Posture Model

Journal of Software ◽

10.3724/sp.j.1001.2011.03768 ◽

2011 ◽

Vol 22 (5) ◽

pp. 899-913 ◽

Cited By ~ 5

Author(s):

Jiao-Ling ZHENG ◽

Chang-Jie TANG ◽

Kai-Kuo XU ◽

Ning YANG ◽

Lei DUAN ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Programming

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Modeling Self-reported Instrument Data with Gene Expression Programming

Acta Psychologica Sinica ◽

10.3724/sp.j.1041.2013.00704 ◽

2013 ◽

Vol 45 (6) ◽

pp. 704-714

Author(s):

Jinxin QIAN ◽

Jiayuan YU

Keyword(s):

Gene Expression ◽

Gene Expression Programming

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