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ORCIDKang Tai
Publications
- 2017
[j17]Akhil Garg
, Biranchi Narayan Panda, Kang Tai:
True stress measurement of nuclear fuel rod cladding material subjected to DSA regime. Neural Comput. Appl. 28(S-1): 119-126 (2017)- 2014
- [j14]Akhil Garg, Kang Tai:
Stepwise approach for the evolution of generalized genetic programming model in prediction of surface finish of the turning process. Adv. Eng. Softw. 78: 16-27 (2014) - [j13]Akhil Garg, Ankit Garg, Kang Tai, S. Sreedeep:
An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes. Eng. Appl. Artif. Intell. 30: 30-40 (2014) - [j12]Akhil Garg, V. Vijayaraghavan, S. S. Mahapatra, Kang Tai, C. H. Wong:
Performance evaluation of microbial fuel cell by artificial intelligence methods. Expert Syst. Appl. 41(4): 1389-1399 (2014) - [j10]Akhil Garg, Kang Tai, C. H. Lee, M. M. Savalani:
A hybrid \(\text{ M}5^\prime \) -genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process. J. Intell. Manuf. 25(6): 1349-1365 (2014) - [j9]Akhil Garg, V. Vijayaraghavan, C. H. Wong, Kang Tai, Liang Gao:
An embedded simulation approach for modeling the thermal conductivity of 2D nanoscale material. Simul. Model. Pract. Theory 44: 1-13 (2014) - [j8]Akhil Garg, V. Vijayaraghavan, C. H. Wong, Kang Tai, K. Sumithra, Liang Gao, Pravin M. Singru:
Combined CI-MD approach in formulation of engineering moduli of single layer graphene sheet. Simul. Model. Pract. Theory 48: 93-111 (2014) - [c21]Akhil Garg, Kang Tai:
An Improved Multi-Gene Genetic Programming Approach for the Evolution of Generalized Model in Modelling of Rapid Prototyping Process. IEA/AIE (1) 2014: 218-226 - 2013
- [j6]Akhil Garg, Kang Tai:
Comparison of statistical and machine learning methods in modelling of data with multicollinearity. Int. J. Model. Identif. Control. 18(4): 295-312 (2013) - [j5]Akhil Garg, Yogesh Bhalerao, Kang Tai:
Review of empirical modelling techniques for modelling of turning process. Int. J. Model. Identif. Control. 20(2): 121-129 (2013) - [c19]Akhil Garg, Kang Tai:
Selection of a robust experimental design for the effective modeling of nonlinear systems using Genetic Programming. CIDM 2013: 287-292 - [c18]Akhil Garg, S. Sriram, Kang Tai:
Empirical analysis of model selection criteria for genetic programming in modeling of time series system. CIFEr 2013: 90-94 - [c17]Akhil Garg, Kang Tai:
Genetic Programming for Modeling Vibratory Finishing Process: Role of Experimental Designs and Fitness Functions. SEMCCO (2) 2013: 23-31
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