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Innovations in Computer Science and Engineering pp 41–46Cite as

Performance Analysis on the Basis of Learning Rate

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 103))

Abstract

In this research paper, a detailed analysis is done on effect of learning rate on machine. Subtle changes in learning rate can bring great impact and can simplify complex research methodologies to a great extent. Our main focus will be on trade-off between learning rate and convergence rate toward an optimal solution. In range of this research, it will be made sure that local optimal solution will not be skipped and the most optimal global solution will be achieved. Keeping all other factors like number of iterations, dataset and algorithm static, we would be verifying our results using experimental analysis done with the help of graphical and statistical observations.

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References

  1. Choudhary S, Agarwal M, Jailia M (2019) Design framework for facial gender recognition using MCNN. Int J Eng Adv Technol (IJEAT) 8(3)

    Google Scholar 

  2. Ahmad AS (2017) Brain inspired cognitive artificial intelligence for knowledge extraction and intelligent instrumentation system. In: 2017 International symposium on electronics and smart devices (ISESD). IEEE, pp 352–356

    Google Scholar 

  3. Ming J, Zhang L, Sun J, Zhang Y (2018) Analysis models of technical and economic data of mining enterprises based on big data analysis. In: 2018 IEEE 3rd international conference on cloud computing and big data analysis (ICCCBDA). IEEE, pp 224–227

    Google Scholar 

  4. Chen X, Zhai G, Wang J, Hu C, Chen Y (2016) Color guided thermal image super resolution. In: Visual communications and image processing (VCIP). IEEE, pp 1–4

    Google Scholar 

  5. Seng KP, Ang LM, Schmidtke LM, Rogiers SY (2018) Computer vision and machine learning for viticulture technology. IEEE Access

    Google Scholar 

  6. Gupta P, Goswami A, Koul S, Sartape K (2017) IQS-intelligent querying system using natural language processing. In: 2017 international conference of electronics, communication and aerospace technology (ICECA), vol 2. IEEE, pp 410–413

    Google Scholar 

  7. Wang J, Zhang J, Wang X (2018) Bilateral LSTM: a two-dimensional long short-term memory model with multiply memory units for short-term cycle time forecasting in re-entrant manufacturing systems. IEEE Trans Ind Inform 14(2):748–758

    Article  Google Scholar 

  8. Zarandi MF, Tarimoradi M, Shirazi MA, Turksan IB (2015) Fuzzy intelligent agent-based expert system to keep information systems aligned with the strategy plans: a novel approach toward SISP. In: 2015 annual conference of the North American fuzzy information processing society (NAFIPS) held jointly with 2015 5th world conference on soft computing (WConSC). IEEE, pp 1–5

    Google Scholar 

  9. Zhang D, Han X, Deng C (2018) Review on the research and practice of deep learning and reinforcement learning in smart grids. CSEE J Power Energy Syst 4(3):362–370

    Article  Google Scholar 

  10. Kim DE, Gofman M (2018) Comparison of shallow and deep neural networks for network intrusion detection. In: 2018 IEEE 8th annual computing and communication workshop and conference (CCWC). IEEE, pp 204–208

    Google Scholar 

  11. Sharma V, Rai S, Dev A (2012) A comprehensive study of artificial neural networks. Int J Adv Res Comput Sci Softw Eng 2(10)

    Google Scholar 

  12. Sharma V, Dev A, Rai S (2012) A comprehensive study of cellular automata. Int J Adv Res Comput Sci Softw Eng 2(10)

    Google Scholar 

  13. Weng WT, Lin DT (2018) Crowd density estimation based on a modified multicolumn convolutional neural network. In: 2018 International joint conference on neural networks (IJCNN). IEEE, pp 1–7

    Google Scholar 

  14. Nijhawan R, Srivastava I, Shukla P (2017) Land cover classification using super-vised and unsupervised learning techniques. In: 2017 international conference on computational intelligence in data science (ICCIDS). IEEE, pp. 1–6

    Google Scholar 

  15. Yang Q, He Z, Ge F, Zhang Y (2017) Sequence-to-sequence prediction of personal computer software by recurrent neural network. In: 2017 International joint conference on neural networks (IJCNN). IEEE, pp 934–940

    Google Scholar 

  16. Wang Q, Huang Y, Mo X, Lai W, Dong H, Yang Y, Liu J, Huang J, Sun Y (2016) The study of the improved multilayer perceptron algorithm on hydrophobicity detection research of composite insulator bushing. In: 2016 8th International conference on intelligent human-machine systems and cybernetics (IHMSC), vol 1. IEEE, pp 438–441

    Google Scholar 

  17. Yuan J, Yu J (2016) Semi-supervised learning with bidirectional adaptive pairwise encoding. In: 2016 15th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 677–681

    Google Scholar 

  18. Heck J, Salem FM (2017) Simplified minimal gated unit variations for recurrent neural networks. arXiv preprint, arXiv:1701.03452

  19. Mohapatra RK, Majhi B, Jena SK (2015) Classification performance analysis of MNIST Dataset utilizing a multi-resolution technique. In: 2015 international conference on computing, communication and security (ICCCS). IEEE, pp 1–5

    Google Scholar 

  20. Wang J, Zhang J, Wang X (2017) A two-dimensional long-short-term memory model with multiply memory units for short-term cycle time forecasting in re-entrant manufacturing systems. IEEE Trans Ind Inf 14(2):748–758

    Article  Google Scholar 

  21. Chu CT, Chiang HK, Lin TC, Kung CT (2014) Recursive wavelet elman neural network for a synchronous reluctance motor. In: 2014 International symposium on next-generation electronics (ISNE). IEEE, pp 1–4

    Google Scholar 

  22. Reddy KS, Singh U, Uttam PK (2017) Effect of image colourspace on performance of convolution neural networks. In: 2017 2nd IEEE international conference on recent trends in electronics, information & communication technology (RTEICT). IEEE, pp 2001–2005

    Google Scholar 

  23. Chen S, Peng C, Cai L, Guo L (2018) A deep neural network model for target-based sentiment analysis. In: 2018 International joint conference on neural networks (IJCNN). IEEE, pp 1–7

    Google Scholar 

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Authors and Affiliations

  1. Banasthali Vidyapith, Computer Science and Engineering, Jaipur, Rajasthan, 304022, India

    Vidushi & Manisha Agarwal

Authors
  1. Vidushi
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  2. Manisha Agarwal
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Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Hyderabad, Telangana, India

    Harvinder Singh Saini

  2. Guru Nanak Institutions, Hyderabad, Telangana, India

    Rishi Sayal

  3. School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC, Australia

    Rajkumar Buyya

  4. Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Hyderabad, Hyderabad, Telangana, India

    Govardhan Aliseri

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Vidushi, Agarwal, M. (2020). Performance Analysis on the Basis of Learning Rate. In: Saini, H., Sayal, R., Buyya, R., Aliseri, G. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 103. Springer, Singapore. https://doi.org/10.1007/978-981-15-2043-3_6

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  • DOI: https://doi.org/10.1007/978-981-15-2043-3_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2042-6

  • Online ISBN: 978-981-15-2043-3

  • eBook Packages: EngineeringEngineering (R0)

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