Grade "A+" Accredited by NAAC with a CGPA of 3.46
Placements
Grade "A+" Accredited by NAAC with a CGPA of 3.46
Placements

Shikha Gupta, Ph.D.

shikhagupta@sscbsdu.ac.in

Areas of Expertise

  • Bibliometric Analysis
  • Social Network Analysis
  • Quantum-inspired Computing
  • Evolutionary algorithms
  • Parallel computing
  • Deep learning
  • Process mining

Shikha Gupta, Ph.D.

Associate Professor

What is the purpose of life? If one were to express this in just a word, it would have to be “happiness”. The purpose of education must therefore accord with the purpose of life.

Biography

Dr. Shikha Gupta started her career in 1997 with HCL-Technologies and subsequently worked as a software analyst with XRT-CERG, Inc., USA. Teaching in SSCBS since 2002, she has taught subjects of B.Sc. (H) Computer Science, B.Tech (Computer Science), BIT, PGDCA, MCA. Her research interests include social network analysis, evolutionary algorithms, quantum-inspired computing, parallel computing, process mining, and bioinformatics. Have published and presented research papers in international conferences and journals. Author and editor of books, she has been invited as a reviewer, member of editorial board and technical committee of journals and international conferences.

Education

  • Ph.D. “Community Detection in Social Networks: A Quantum-inspired Evolutionary Approach”. Computer Science, University of Delhi, Delhi, India, 2017.
  • M.Phil. “Rule Mining Using Formal Concept Analysis”. Computer Science, Madurai Kamraj University, India, 2009.
  • M.C.A., University of Delhi, Delhi, India, 1997.

Publications

  • S. Deshmukh, S. Gupta, N. Kumar (2021). GA-ProM: A Genetic Algorithm for Discovery of Complete Process Models from Unbalanced logs. 9th International Conference Data Models and New Query Languages in Big Data Analytics, Japan, December 7-9, 2021 (accepted). Scopus indexed.
  • Deshmukh, S., Gupta, S., Varshney, S., Kumar, N. (2021). A binary differential evolution approach to extract business process models. Soft Computing for Problem Solving. Springer, Singapore. 279-290.
  • Deshmukh, S., Agarwal, M., Gupta, S., Kumar, N. (2020). MOEA for discovering Pareto-optimal process models: an experimental comparison. International Journal of Computational Science and Engineering, 21(3), 446-456. Scopus, ESCI indexed.
  • Sonia, Agarwal, M., Gupta, S., Kumar, N. (2018). Discovering Pareto-optimal process models: a comparison of MOEA techniques. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 286-287). Scopus indexed, H5-index 30, CORE-A ranking.
  • Gupta, S., Mittal, S., Gupta, T., Singhal, I., Khatri, B., Gupta, A. K., & Kumar, N. (2017). Parallel quantum-inspired evolutionary algorithms for community detection in social networks. In Applied Soft Computing, Volume 61, 2017, Pages 331-353, ISSN 1568-4946. H-index 97. SJR 1.2. IF 3.9. Scopus, SCI Indexed.
  • Gupta, S., Kumar, N., & Bhalla, S. (2023). Citation metrics and evaluation of journals and conferences. Journal of Information Science Online First Feb. 2023). https://doi.org/10.1177/01655515231151411. Draft version is available here