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ORCIDAkhil Garg 0002
Person information
- affiliation: Huazhong University of Science and Technology, State Key Laboratory of Digital Manufacturing Equipment and Technology, Wuhan, China
- affiliation (former): Shantou University, Department of Mechatronics Engineering, China
- affiliation (PhD 2015): Nanyang Technological University, School of Mechanical and Aerospace Engineering, Singapore
Other persons with the same name
- Akhil Garg 0001 — Cadence Design Systems, Noida, India (and 1 more)
- Akhil Garg 0003 — Indian Institute of Technology Delhi, Centre for Automotive Research and Tribology, India
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2020 – today
- 2024
[j32]Wei Li, Xiaowei Zhou, Haihong Huang, Akhil Garg, Liang Gao:
Risk-Based Design Optimization via Scenario Generation and Genetic Programming Under Hybrid Uncertainties. J. Comput. Inf. Sci. Eng. 24(10) (2024)- [i1]Nikolai K. Krivulin, Akhil Garg:
Tropical modeling of battery swapping and charging station. CoRR abs/2401.09201 (2024) - 2023
- [j31]Fan Li
, Liang Gao, Weiming Shen, Akhil Garg:
Surrogate-assisted multi-objective evolutionary optimization with a multi-offspring method and two infill criteria. Swarm Evol. Comput. 79: 101315 (2023) - [j30]Jianhui Mou, Kaizhou Gao, Peiyong Duan, Junqing Li, Akhil Garg, Rohit Sharma:
A Machine Learning Approach for Energy-Efficient Intelligent Transportation Scheduling Problem in a Real-World Dynamic Circumstances. IEEE Trans. Intell. Transp. Syst. 24(12): 15527-15539 (2023) - 2022
- [j29]Wei Li, Liang Gao, Akhil Garg, Mi Xiao:
Multidisciplinary robust design optimization considering parameter and metamodeling uncertainties. Eng. Comput. 38(1): 191-208 (2022) - [c7]Elvin Cheah Ee Sheng, Christina Chin May May, Novita Sakundarini, Akhil Garg:
Conceptualizing A Battery Swapping Station: A Case Study in Malaysia. IICAIET 2022: 1-5 - 2021
- [j28]Fan Li, Liang Gao, Akhil Garg, Weiming Shen, Shifeng Huang:
A comparative study of pre-screening strategies within a surrogate-assisted multi-objective algorithm framework for computationally expensive problems. Neural Comput. Appl. 33(9): 4387-4416 (2021) - [j27]Fan Li, Liang Gao, Akhil Garg, Weiming Shen, Shifeng Huang:
Two infill criteria driven surrogate-assisted multi-objective evolutionary algorithms for computationally expensive problems with medium dimensions. Swarm Evol. Comput. 60: 100774 (2021) - [j26]Wei Li, Mi Xiao, Akhil Garg, Liang Gao:
A New Approach to Solve Uncertain Multidisciplinary Design Optimization Based on Conditional Value at Risk. IEEE Trans Autom. Sci. Eng. 18(1): 356-368 (2021) - [j25]Li-Gang Zhang, Fang Fan, Shu-Chuan Chu, Akhil Garg, Jeng-Shyang Pan:
Hybrid Strategy of Multiple Optimization Algorithms Applied to 3-D Terrain Node Coverage of Wireless Sensor Network. Wirel. Commun. Mob. Comput. 2021: 6690824:1-6690824:21 (2021) - [c6]Thien Pham, Loi Truong, Mao Nguyen, Akhil Garg, Liang Gao, Tho Quan:
Sequence-in-Sequence Learning for SOH Estimation of Lithium-Ion Battery. CECNet 2021: 14-25 - 2020
- [b1]S. Arungalai Vendan, Rajeev Kamal, Abhinav Karan, Liang Gao, Xiaodong Niu, Akhil Garg:
Welding and Cutting Case Studies with Supervised Machine Learning. Springer 2020, ISBN 978-981-13-9381-5, pp. 1-245 - [j24]Akhil Garg, Surinder Singh, Liang Gao, Mei-Juan Xu, Chee Pin Tan:
Multi-objective optimisation framework of genetic programming for investigation of bullwhip effect and net stock amplification for three-stage supply chain systems. Int. J. Bio Inspired Comput. 16(4): 241-251 (2020) - [j23]Akhil Garg, Shaosen Su, Fan Li, Liang Gao:
Framework of model selection criteria approximated genetic programming for optimization function for renewable energy systems. Swarm Evol. Comput. 59: 100750 (2020)
2010 – 2019
- 2019
- [j22]Guorong Xiao, Akhil Garg, Dicheng Chen, Dazhi Jiang, Wanneng Shu, Xuemiao Xu:
AHE Detection With a Hybrid Intelligence Model in Smart Healthcare. IEEE Access 7: 37360-37370 (2019) - [j21]V. Vijayaraghavan, Li Shui, Akhil Garg, Xiongbin Peng, Vikas Pratap Singh:
Crash analysis of lithium-ion batteries using finite element based neural search analytical models. Eng. Comput. 35(1): 115-125 (2019) - [j20]Biranchi Narayan Panda, K. Shankhwar, Akhil Garg, M. M. Savalani:
Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing. J. Intell. Manuf. 30(2): 809-820 (2019) - [j19]Kaipu Wang, Xinyu Li, Liang Gao, Akhil Garg:
Partial disassembly line balancing for energy consumption and profit under uncertainty. Robotics Comput. Integr. Manuf. 59: 235-251 (2019) - [j18]Nivedhitha Mahendran, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Chuan-Yu Chang, Akhil Garg, Liang Gao, Daniel Gutiérrez-Reina:
Sensor-Assisted Weighted Average Ensemble Model for Detecting Major Depressive Disorder. Sensors 19(22): 4822 (2019) - [j17]Zhun Fan, Yugen You, Xinye Cai, Haodong Zheng, Guijie Zhu, Wenji Li, Akhil Garg, Kalyanmoy Deb, Erik D. Goodman:
Analysis and multi-objective optimization of a kind of teaching manipulator. Swarm Evol. Comput. 50 (2019) - 2018
- [j16]H. V. Kurugodu, Sanandam Bordoloi, Y. Hong, Ankit Garg, Akhil Garg, S. Sreedeep, Amir Hossein Gandomi:
Genetic programming for soil-fiber composite assessment. Adv. Eng. Softw. 122: 50-61 (2018) - [j15]Dazhi Jiang, Yifei Chen, Akhil Garg:
A hybrid method for overlapping speech detection in classroom environment. Comput. Appl. Eng. Educ. 26(1): 171-180 (2018) - [j14]Akhil Garg, Jasmine Siu Lee Lam, M. M. Savalani:
Laser power based surface characteristics models for 3-D printing process. J. Intell. Manuf. 29(6): 1191-1202 (2018) - [j13]Akhil Garg, K. Shankhwar, Dazhi Jiang, V. Vijayaraghavan, Biranchi Narayan Panda, Sudhansu Sekhar Panda:
An evolutionary framework in modelling of multi-output characteristics of the bone drilling process. Neural Comput. Appl. 29(11): 1233-1241 (2018) - 2017
- [j12]Akhil Garg, Jasmine Siu Lee Lam, Biranchi Narayan Panda:
A hybrid computational intelligence framework in modelling of coal-oil agglomeration phenomenon. Appl. Soft Comput. 55: 402-412 (2017) - [j11]Keqin Yan, Yi Zhang, Tao Cheng, Xianfeng Luo, Quan Zhou, Kun Fang, Ankit Garg, Akhil Garg:
Managing Information Uncertainty in Wave Height Modeling for the Offshore Structural Analysis through Random Set. Complex. 2017: 9546272:1-9546272:13 (2017) - [j10]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) - [c5]Xiongbin Peng, Biranchi Narayan Panda, Akhil Garg, H. Guan, M. M. Savalani:
Numerical Investigation of Flexural Properties of Curved Layer FDM Parts. SocProS (2) 2017: 255-263 - 2015
- [j9]Akhil Garg, V. Vijayaraghavan, Jasmine Siu Lee Lam, Pravin M. Singru, Liang Gao:
A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance. Swarm Evol. Comput. 21: 54-63 (2015) - 2014
- [j8]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) - [j7]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) - [j6]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) - [j5]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) - [j4]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) - [j3]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) - [c4]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
- [j2]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) - [j1]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) - [c3]Akhil Garg, Kang Tai:
Selection of a robust experimental design for the effective modeling of nonlinear systems using Genetic Programming. CIDM 2013: 287-292 - [c2]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 - [c1]Akhil Garg, Kang Tai:
Genetic Programming for Modeling Vibratory Finishing Process: Role of Experimental Designs and Fitness Functions. SEMCCO (2) 2013: 23-31
Coauthor Index
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