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Bagui,Sikha_211

Dr. Sikha Bagui

  • Position: Distinguished University Professor
  • Department: Computer Science
  • Office Location: Building 4, Room 245
  • bagui@uwf.edu
  • Campus: 850.474.3022

Biography

Dr. Sikha Bagui, Distinguished University Professor and Askew Fellow, was former Chair of the Department of Computer Science and Founding Director of the Center for Cybersecurity at the University of West Florida. Dr. Bagui is active in publishing peer reviewed journal articles in the areas of database design, data mining, Big Data analytics, Machine Learning and AI. Dr. Bagui has worked on funded as well unfunded research projects and has 85+ peer reviewed publications, some in highly selected journals and conferences. She has authored several books on database and SQL, and her books have been translated into several different languages and have international editions. Dr. Bagui also serves as Associate Editor and is on the editorial board of several journals.

Degrees & Institutions

Ed.D., Curriculum & Instruction: Math & Stat / Science/ Computer Science,
University of West Florida
M.B.A., University of Toledo
B.S., Cuttington University (Liberia)

Research

Interests: Big Data Analytics, Machine Learning, Data Mining, Database Design, Data Pre-Processing

Grant Funding:

  • NSF CSForALL: $300,000, 10/1/2021-09/30/2023
  • NSF Collaborative: RAPID, $50,000, 01/25/2021 - 04/30/2021
  • NSF Collaborative: Elements: RUI: $350,000, 01/25/2021-10/31/2022
  • NSA NCAE: $375,511, 9/22/2021-12/31/2024
  • Center for Inclusive Computing: $60,000, 1/2021 – 1/2023

Current Courses

  • Database Systems
  • Introduction to Big Data Analytics
  • Advanced Big Data Analytics
  • Introduction to Data Mining
  • Advanced Data Mining
  • Project (Special topics on Data Mining, Machine Learning and Big Data Analytics)

Outreach:

Women in Computing

Florida Panhandle and Southern Alabama Awards Ceremony 2022

For Aspiring High School Computing/Technology Students:

The 2022 NCWIT competition that recognizes outstanding students in Computing and Technology opens on September 1, 2022.

Apply for the NCWIT Award for Aspirations in Computing

Publications

Selected:

  1. Bagui, S., Xingang, F., Kalaimmanan, E., Bagui, S., and Sheehan, J. (2017). Comparison of Machine Learning Algorithms for classification of VPN and non-VPN Network Traffic Flow Using Time-Related Features, Journal of Cyber Security Technology, 1(2), 108-126.
  2. Bagui, S. and Devulapalli, K. (2018). A Comparison of Hive’s Optimization Techniques, International Journal of Big Data Intelligence (IJBDI), 5(4), 243-257.
  3. Bagui, S., and Spratlin, S. (2018). A Review of Data Mining Algorithms on Hadoop’s MapReduce, International Journal of Data Science, 3(2), 146-169.
  4. Bagui, S., John, S., Baggs, J., and Bagui, S. (2018). A Parallel Implementation of Information Gain Using Hive in conjunction with MapReduce for Continuous Features, PAKDD 2018, Lecture Notes in Artificial Intelligence 11154, 283-294.
  5. Bagui, S. & Dhar, P. (2019). Positive and Negative Association Rule Mining in Hadoop’s MapReduce Environment. Journal of Big Data, 6:75. https://doi.org/10.1186/s40537-019-0238-8.
  6. Bagui, S., Kalaimannan, E., Bagui, S., Nandi, D., & Pinto, A. (2019). Using Machine Learning Techniques to Identify Rare Cyber-Attacks on the UNSW-NB15 Dataset, Security and Privacy (Wiley), 2(6), 1-13. https://doi.org/10.1002/spy2.91
  7. Bagui, S., Mondal, A. K., & Bagui, S. (2019). Improving the Performance of kNN in the MapReduce Framework Using Locality Sensitive Hashing, International Journal of Distributed Systems and Technologies (IJDST), 10(4), 1-16.
  8. Bagui, S. Fang, X., Bagui, S., Wyatt, J., Houghton, P., Nguyen, J., Schneider, J., & Guthrie, T. (2020). An Improved Step Counting Algorithm Using Classification and Double Autocorrelation, International Journal of Computers and Applications (Francis and Taylor journal), 1-10, https://doi.org/10.1080/1206212X.2020.1726006
  9. Bagui, S., Devulapalli, K., & John, S. (2020). MapReduce Implementation of a Mixed and Multinomial Naïve Bayes Classifier, International Journal of Intelligent Information Technologies (IJIIT), 16(2), 1-23. DOI: 10.4018/IJIIT.2020040101
  10. Bagui, S., Devulapalli, K., & Coffey, J. (2020). A Heuristic Approach for Load Balancing the FP-Growth Algorithm On MapReduce. Array, 7, https://doi.org/10.1016/j.array.2020.100035
  11. Bagui, S., & Stanley, P. (2020). Mining Frequent Itemsets From Streaming Transaction Data Using Genetic Algorithms. Journal of Big Data, 7(54). https://doi.org/10.1186/s40537-020-00330-9.
  12. Bagui, S., & Li, K. (2021). Resampling Imbalanced Data for Network Intrusion Detection Datasets. Journal of Big Data, 8(6), 1-41. https://doi.org/10.1186/s40537-020-00390-x
  13. Bagui, S., Nandi, D., Bagui, S., & *White, R. J. (2021). Machine Learning and Deep Learning for Phishing Email Classification, Journal of Computer Science, 17(7), 610-623. DOI:https://doi.org/10.3844/jcssp.2021.610.623
  14. Bagui, S., Simonds, J., Plenkers, R., Bennett, T., & Bagui, S. (2021). Classifying UNSW-NB15 Network Traffic in the Big Data Framework using Random Forest in Spark. International Journal of Big Data Intelligence and Applications (IJBDIA), 2(1), 1-23. Article 17. DOI: 10.4018/IJBDIA.287617

Selected Books:

  1. Bagui, S.and Earp, R. (2006) Learning SQL Using SQL Server 2005, O’Reilly Publishers, ISBN: 0-596-10215-1.
  2. Bagui, S. and Earp, R. (2011). Essential of SQL Using SQL Server 2008, Jones and Bartlett, ISBN: 978-0-7637-8138-5.
  3. Bagui, S. and Earp, R. (2012). Database Design Using ER Diagrams, 2nd edition, Taylor and Francis. ISBN: 9781439861769.
  4. Bagui, S., and Earp, R. (2015). SQL Server 2014: A Step by Step Guide to Learning SQL, Nova Publishers. ISBN: 978-1-63463-543-1. E-book version ISBN: 978-1-63463-554-7.
  5. Earp, R. and Bagui, S. (2021). A Practical Guide to Using SQL in Oracle, 3rd edition, BVT Publishing. ISBN: 978-1-5178-1068-9 (e-Book) or ISBN: 978-1-5178-1069-6 (loose leaf version).
  6. Earp, R., and Bagui, S. (2021). Oracle SQL For Secure Relational Databases, Nova Science Publishers, New York. ISBN: 978-1-53619-436-4.

Keywords: Big Data Analytics, Machine Learning, Data Mining, Database Design, Hadoop, Spark, Hive, SQL, Entity-Relationship Modeling, Association Rule Mining, Decision Trees, Random Forest, SVM, Attribute Selection, Resampling, Load Balancing, Data Pre-Processing