Impact of Decision-Region Based Classification Mining Algorithms on Database Security. - Tom Johnsten; Vijay V. Raghavan 001 (DBSec, 1999)

Security Procedures for Classification Mining Algorithms. - Tom Johnsten; Vijay V. Raghavan 001 (DBSec, 2001)

On Security and Privacy Risks in Association Mining Algorithms. - Tom Johnsten; Vijay V. Raghavan 001; Kevin Hill (DBSec, 2002)

A Methodology for Hiding Knowledge in XML Document Collections. - Tom Johnsten; Robert B. Sweeney; Vijay V. Raghavan 001 (COMPSAC, 2003)

On discovering "potentially useful" patterns from databases. - Ying Xie 001; Tom Johnsten; Vijay V. Raghavan 001; Karthik Ramachandran (GrC, 2006)

An efficient algorithm for discovering positive and negative patterns. - Raj Singh; Tom Johnsten; Vijay V. Raghavan 001; Ying Xie 001 (GrC, 2009)

Efficient Algorithm for Discovering Potential Interesting Patterns with Closed Itemsets. - Raj Singh; Tom Johnsten; Vijay V. Raghavan 001; Ying Xie 001 (GrC, 2010)

A Threat Tree for Health Information Security and Privacy. - Jeffrey P. Landry; John Harold Pardue; Tom Johnsten; Matt Campbell; Priya Patidar (AMCIS, 2011)

Multi-layered Vector Spaces for Classifying and Analyzing Biological Sequences. - Can Akkoç; Tom Johnsten; Ryan G. Benton (BICoB, 2011)

FAARM: Frequent Association Action Rules Mining Using FP-Tree. - Djellel Eddine Difallah; Ryan G. Benton; Vijay V. Raghavan 001; Tom Johnsten (ICDM Workshops, 2011)

Algorithms for discovering potentially interesting patterns. - Raj Singh; Tom Johnsten; Vijay V. Raghavan 001; Ying Xie 001 (Int. J. Granul. Comput. Rough Sets Intell. Syst., 2011)

Granular Approach for Protein Sequence Analysis. - Ying Xie 001; Jonathan Fisher; Vijay V. Raghavan 001; Tom Johnsten; Can Akkoç (RSCTC, 2012)

Diagnosis and Grading of Alzheimer's Disease via Automatic Classification of FDG-PET Scans. - Ryan G. Benton; Suresh K. Choubey; David G. Clark; Tom Johnsten; Vijay V. Raghavan 001 (Brain and Health Informatics, 2013)

Targeted Action Rule Discovery. - Tom Johnsten; Samy Alihamad; Ashwin Kannalath; Ryan G. Benton (ICMLA (1), 2013)

Representations for Large-Scale Sequence Data Mining: A Tale of Two Vector Space Models. - Vijay V. Raghavan 001; Ryan G. Benton; Tom Johnsten; Ying Xie 001 (RSFDGrC, 2013)

Exploiting multi-layered vector spaces for signal peptide detection. - Tom Johnsten; Laura Fain; Leanna Fain; Ryan G. Benton; Ethan Butler; Lewis Pannell; Ming Tan (Int. J. Data Min. Bioinform., 2015)

A Multi-Objective Evolutionary Action Rule Mining Method. - Grant T. Daly; Ryan Benton; Tom Johnsten (CEC, 2018)

Android Malware Detection Using Step-Size Based Multi-layered Vector Space Models. - Colby Parker; Jeffrey Todd McDonald; Tom Johnsten; Ryan G. Benton (MALWARE, 2018)

Prescriptive Equipment Maintenance: A Framework. - Suresh K. Choubey; Ryan G. Benton; Tom Johnsten (IEEE BigData, 2019)

Discovery of Action Rules for Continuously Valued Data. - Tom Johnsten; Wyatt Green; Lowell Crook; Ho Yin Chan; Ryan G. Benton; David M. Bourrie (CogMI, 2019)

Dynamic Thresholding Leading to Optimal Inventory Maintenance. - Suresh K. Choubey; Ryan Benton; Tom Johnsten (IEEE BigData, 2020)

TADS: Transformation of Anomalies in Data Streams. - Wyatt Green; Tom Johnsten; Ryan G. Benton (IEEE BigData, 2020)

Towards Poisoning the Neural Collaborative Filtering-Based Recommender Systems. - Yihe Zhang 001; Jiadong Lou; Li Chen 019; Xu Yuan 001; Jin Li 002; Tom Johnsten; Nian-Feng Tzeng (ESORICS (1), 2020)

RARE: Rare Action Rule Exploration. - Blake A. Johns; Ryan G. Benton; Tom Johnsten; David M. Bourrie (IEEE BigData, 2021)

Precise Weather Parameter Predictions for Target Regions via Neural Networks. - Yihe Zhang 001; Xu Yuan 001; Sytske K. Kimball; Eric Rappin; Li Chen 019; Paul J. Darby III; Tom Johnsten; Lu Peng 001; Boisy Pitre; David Bourrie; Nian-Feng Tzeng (ECML/PKDD (5), 2021)

Computational framework for generating synthetic signal peptides. - Tom Johnsten; Aishwarya Prakash; Grant T. Daly; Ryan G. Benton; Tristan Clark (BCB, 2022)