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International Conference on Information Systems and Management Science

ISMS 2020: Information Systems and Management Science pp 160–170Cite as

Kinetic Gas Molecular Optimized (KGMO) Artificial Neural Network (ANN) Based Software Reliability Prediction for Banking Applications

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 303)

Abstract

Software Reliability is one of the major considerations in the banking sector to improve security and quality. Rapid changes in hardware and software technologies lead to creations of new techniques and necessities creating and approving the dependability foreseeing model for every strategy. In this work, the applicability of Kinetic Gas Molecule Optimization (KGMO) with Artificial Neural Network (KGMO-ANN) as a model to predict the software Reliability. The parameter of the learning process of ANN can be adjusted by using the KGMO optimization process. The model has been applied on data sets collected across several standard software projects during system testing phase with fault removal To evaluate the performance technique, Root Mean Square Error (RMSE), Average Error (AE), Mean Absolute Error (MAE), Mean absolute percent error (MAPE) and Normalized Mean Square Error (NRMSE) will be calculated. From various evaluation technique, found that high accuracy is obtained from proposed Reliability Prediction technique.

Keywords

  • Reliability prediction
  • Kinetic Gas Molecule Optimization
  • Artificial Neural Network
  • Root Mean Square Error
  • Normalized Mean Square Error

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  • DOI: 10.1007/978-3-030-86223-7_15
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Author information

Authors and Affiliations

  1. B.M.S. College of Engineering, Bangalore, Karnataka, India

    P. Lakshminarayana & T. V. Suresh Kumar

  2. M.S.R Institute of Technology, Bangalore, Karnataka, India

    T. V. Suresh Kumar

Authors
  1. P. Lakshminarayana
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  2. T. V. Suresh Kumar
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Editor information

Editors and Affiliations

  1. CIS Dept, University of Malta, Msida, Malta

    Lalit Garg

  2. Department of Computer Science, Central University of Rajasthan, Rajasthan, India

    Nishtha Kesswani

  3. CIS Dept, University of Malta, Msida, Malta

    Joseph G. Vella

  4. Faculty of ICT, University of Malta, Msida, Malta

    Peter A. Xuereb

  5. The Education University of Hong Kong, Hong Kong, Hong Kong

    Dr. Man Fung Lo

  6. Nueva Ecija University of Science and Technology, Cabanatuan City, Philippines

    Rowell Diaz

  7. Computer Engg, CUCRID Bldg, #A301, Covenant University, Ota, Nigeria

    Dr. Sanjay Misra

  8. Lm Thapar School of Management, Thapar University, Mohali, India

    Vipul Gupta

  9. Manipal University Jaipur, Jaipur, India

    Princy Randhawa

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Lakshminarayana, P., Kumar, T.V.S. (2022). Kinetic Gas Molecular Optimized (KGMO) Artificial Neural Network (ANN) Based Software Reliability Prediction for Banking Applications. In: , et al. Information Systems and Management Science. ISMS 2020. Lecture Notes in Networks and Systems, vol 303. Springer, Cham. https://doi.org/10.1007/978-3-030-86223-7_15

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