Publications

2022

  • S.R. Islam and W. Eberle, “Domain Knowledge-Aided Explainable Artificial Intelligence”. In: Ahmed, M., Islam, S.R., Anwar, A., Moustafa, N., Pathan, AS.K. (eds) Explainable Artificial Intelligence for Cyber Security. Studies in Computational, 2022 Intelligence, vol 1025. Springer, Cham. https://doi.org/10.1007/978-3-030-96630-0_4
  • P. Lamichhane, H. Mannering, and W. Eberle, “Discovering Breach Patterns on the Internet of Health Things: A Graph and Machine Learning Anomaly Analysis”, International Conference of the Florida Artificial Intelligence Research Society (FLAIRS), May 2022.
  • S. R. Islam, I. Russell, W. Eberle, and D. Dicheva, “Incorporating the Concepts of Fairness and Bias into an Undergraduate Computer Science Course to Promote Fair Automated Decision Systems,” SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education, March 2022.

2021

  • P. Lamichhane and W. Eberle, “Anomaly Detection in Edge Streams Using Term Frequency-Inverse Graph Frequency (TF-IGF) Concept,” IEEE Big Data, December 2021.
  • R. Paudel, L. Tharp, D. Kaiser, W. Eberle, and G. Gannod, “Visualization of Anomalies using Graph-Based Anomaly Detection,” International Conference of the Florida Artificial Intelligence Research Society (FLAIRS), May 2021.
  • S. R. Islam and W. Eberle, “Implications of Combining Domain Knowledge in Explainable Artificial Intelligence,” Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE), March 2021.
  • G. Stone, D. Talbert, and W. Eberle, “Using AI/Machine Learning for Reconnaissance Activities During Network Penetration Testing,” 16th International Conference of Cyber Warfare and Security (ICCWS), Feb 24-26, 2021.

2020

  • W. Eberle and L. Holder, “Graph Filtering to Remove the “Middle Ground” for Anomaly Detection”, IEEE Big Data Conference, Workshop on High Performance Big Graph Data Management, Analysis, and Mining (BigGraphs 2020), December 2020.
  • R. Paudel and W. Eberle. “An Approach For Concept Drift Detection in a Graph Stream Using Discriminative Subgraphs.” ACM Transactions on Knowledge Discovery from Data. Article 70. September 2020. DOI:https://doi.org/10.1145/3406243.
  • R. Paudel and W. Eberle, “SNAPSKETCH: Graph Representation Approach for Intrusion Detection in a Streaming Graph,” Conference on Knowledge Discovery and Data Mining (KDD) Mining and Learning with Graphs (MLG), August 2020.
  • S. R. Islam, W. Eberle, and S. Ghafoor, “Towards Quantification of Explainability in Explainable Artificial Intelligence Methods,” International Conference of the Florida Artificial Intelligence Research Society (FLAIRS), May 2020.
  • P. Kandel and W. Eberle, “Node Similarity For Anomaly Detection in Attributed Graphs,” International Conference of the Florida Artificial Intelligence Research Society (FLAIRS), May 2020.
  • S. R. Islam, W. Eberle, S. Ghafoor, A. Siraj, and M. Rogers, “Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response,” Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE), March 2020.

2019

  • Ramesh Paudel, Prajwal Kandel, and William Eberle, “Detecting Spam Tweets in Trending Topics using Graph-Based Approach,” Proceedings of the Future Technologies Conference (FTC), October 2019.
  • Sheikh Rabiul Islam, William Eberle, Sid C. Bundy, and Sheikh Ghafoor, “Infusing Domain Knowledge in AI-based “black box” Models for Better Explainability with Application in Bankruptcy Prediction,” Workshop on Anomaly Detection in Finance, SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), August 2019.
  • A. Bhuiyan, M. B. Sharif, P. J. Tinker, W. Eberle, D. A. Talbert, S. K. Ghafoor, and L. Frey, “Gene Selection and Clustering of Breast Cancer Data,” International Conference of the Florida AI Research Society (FLAIRS), May 2019.
  • Sirisha Velampalli, Lenin Mookiah, and William Eberle, “Discovering Suspicious Patterns Using a Graph Based Approach,” International Conference of the Florida AI Research Society (FLAIRS), May 2019.
  • Ramesh Paudel, Peter Harlan, and William Eberle, “Detecting the Onset of a Network Layer DoS Attack with a Graph-Based Approach,” International Conference of the Florida AI Research Society (FLAIRS), May 2019.

2018

  • Lenin Mookiah, William Eberle, and M. Mondal, “Personalized News Recommendation using Graph-Based Approach”, Intelligent Data Analysis, an International Journal, Volume 22 (2018) 881–909.
  • Ramesh Paudel, William Eberle, and Lawrence Holder, “Anomaly Detection of Elderly Patient Activities in Smart Homes using a Graph-Based Approach,” International Conference on Data Science (ICDATA), July 2018.
  • Sheikh Rabiul Islam, William Eberle, and Sheikh Ghafoor, “Credit Default Mining Using Combined Machine Learning and Heuristic Approach,” International Conference on Data Science (ICDATA), July 2018.
  • Rina Singh, Jeffrey Graves, Douglas Talbert, and William Eberle, “Prefix and Suffix Sequential Pattern Mining”, Conference on Machine Learning and Data Mining (MLDM), July, 2018.
  • Ramesh Paudel, Kimberlyn Dunn, William Eberle, and Danielle Chaung, “Cognitive Health Prediction on the Elderly Using Sensor Data in Smart Homes,” International Conference of the Florida AI Research Society (FLAIRS), May 2018,

2017

  • Sirisha Velampalli, Lenin Mookiah, and William Eberle, “Detecting Vehicular Patterns Using a Graph-Based Approach,” IEEE Conference on Visual Analytics Science and Technology (VAST), October 2017.
  • Deon Liang, Chih-Fong Tsai, An-Jie Dai, and William Eberle, “A novel classifier ensemble approach for financial distress prediction,” Knowledge and Information Systems, An International Journal, May 2017.
  • Lenin Mookiah, Chris Dean, and William Eberle, “Graph-Based Anomaly Detection on Smart Grid Data,” International Conference of the Florida AI Research Society (FLAIRS), May 2017.
  • Ramesh Paudel, William Eberle, and Douglas Talbert “Detection of Anomalous Activity in Diabetic Patients Using Graph-Based Approach,” International Conference of the Florida AI Research Society (FLAIRS), May 2017.
  • Sirisha Velampalli and William Eberle, “Novel Graph Based Anomaly Detection Using Background Knowledge,” International Conference of the Florida AI Research Society (FLAIRS), May 2017.
  • Nishith Thakkar, Lenin Mookiah, Douglas Talbert, and William Eberle, “Anomalies in Student Enrollment Using Visualization,” International Conference of the Florida AI Research Society (FLAIRS), May 2017.

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006