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A

Y.Huang, M. Georgiopoulos, R. DeMara,, and G.C. Anagnostopoulos, M. Georgiopoulos, “Accelerated Learning of Generalized Sammon Mappings”, in International Joint Conference on Neural Networks (IJCNN), San Jose, CA, 2011.

A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., “AG-ART: An Adaptive Method of Evolving ART Architectures”, Neurocomputing, vol. 72, pp. 2079-2092, 2009.

L. Merakos and Georgiopoulos, M., “Analysis of a multi-hop CDMA packet radio network”, in the 26th IEEE Conference on Decision and Control, Los Angeles, California, 1987.

M. Mollaghasemi, LeCroy, K., and Georgiopoulos, M., “Application of neural networks and simulation modeling in manufacturing system design”, INTERFACES, vol. 28, pp. 100-114, 1998.

M. Mollaghasemi, LeCroy, K., and Georgiopoulos, M., “Applications of neural networks and simulation modeling in manufacturing system design”, in Southcon 96, 1996.

M. Georgiopoulos, Gelenbe, E., DeMara, R., Gonzalez, A., Mollaghasemi, M., Wu, A., Russell, I., Anagnostopoulos, G., and Secretan, J., “Assessing and Evaluating our progress on the CRCD Experiences at the University of Central Florida: An NSF Project”, in 2006 ASEE, Chicago, Illinois, 2006.

M. Georgiopoulos, Gelenbe, E., DeMara, R., Gonzalez, A., Mollaghasemi, M., Wu, A., Russell, I., Anagnostopoulos, G., and Secretan, J., “Assessing and Evaluating our progress on the CRCD Experiences at the University of Central Florida: An NSF Project”, 2006.

C

F. Miwakeichi, Ramirez-Padron, R., and P. Valdes-Sosa, O. T., “A comparison of Non-linear Non-parametric Models for Epilepsy Data”, Computers in Biology and Medicine, vol. 31.1, pp. 41–57, 2001.

I. Russel, Georgiopoulos, M., Castro, J., Neller, T., McCracken, D., and Bouvier, D., “Condensing the CC-2001 core in an Integrated Curriculum”, in CCSCNE (Consortium for Computing in Small Colleges in the Northeast), Providence, RI, 2003.

M. Georgiopoulos, Gelenbe, E., DeMara, R., Gonzalez, A., Kysilka, M., Mollaghasemi, M., Wu, A., Russell, I., Anagnostopoulos, G., and Secretan, J., “CRCD Experiences at the University of Central Florida: An NSF Project”, in the ASEE 2005 Annual Conference and Exposition, Portland Oregon, 2005.

E

M. Mollaghasemi, Georgiopoulos, M., Cope, D., Donnelly, A., and Steele, M., “Educating Middle and High School Students in Space Operations”, in the 2004 Winter Simulation Conference, Washington, DC, 2004.

A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., “Efficient Evolution of ART Neural Networks”, in 2008 IEEE Congress on Evolutionary Computation (IEEE CEC 2008), 2008.

L. Massi, Lancey, P., Nair, U., Straney, R., Georgiopoulos, M., and Young, C., “Engineering and Computer Science Community College Transfers and Native Freshmen Students: Relationships Among Participation in Extra-Curricular and Co-Curricular Activities, Connecting to the University Campus, and Academic Success”, in Frontiers in Education Conference, Seattle, WA, October 3-6, 2012.

M. Zhong, Rosander, B., Georgiopoulos, M., Anagnostopoulos, G., Mollaghasemi, M., and Richie, S., “Experiments with Micro-ARTMAP: Effect of the Network Parameters on the Network Performance”, in 2005 Artificial Neural Networks in Engineerin, St. Louis, MI, 2005.

M. Zhong, Rosander, B., Georgiopoulos, M., Anagnostopoulos, G. C., Mollaghasemi, M., and Richie, S., “Experiments with Safe ARTMAP and Comparisons to Other ART Networks”, Proceedings of the 2006 IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2006), pp. 720–727, 2006.

M. Zhong, Rosander, B., Georgiopoulos, M., Anagnostopoulos, G. C., Mollaghasemi, M., and Richie, S., “Experiments with Safe micro-ARTMAP: Effect of the Network Parameters on the Network Performance”, Neural Networks, vol. 20.2, pp. 245–259, 2007.

M. Zhong, Rosander, B., Georgiopoulos, M., Anagnostopoulos, G., Mollaghasemi, M., and Richie, S., “Experiments with Safe Micro-ARTMAP: Effect of the Network Parameters on the Network Performance”, in WCCI 2006, Vancouver, Canada, 2006.

F

A. Meyer-Base, Jancke, K., Wissmuller, A., and Georgiopoulos, M., “Fast K-dimensional tree-structured vector quantization encoding method for image compression”, Optical Engineering Letters, vol. 43, pp. 1012-1013, 2004.

G

M. Zhong, Coggeshall, D., Georgiopoulos, M., Anagnostopoulos, G. C., Mollaghasemi, M., and Richie, S., “Gap-Based Estimation: Choosing the Smoothing Parameters for Probabilistic and General Regression Neural Networks”, Proceedings of the 2006 IEEE-INNS-ENNS International Joint Conference on Neural Networks, pp. 1870–1877, 2006.

M. Zhong, Georgiopoulos, M., Anagnostopoulos, G. C., and Mollaghasemi, M., “Generalized Entropy for Splitting Numerical Attributes in Decision Tree Classifiers”, Proceedings of the 19th International Florida Artificial Intelligence Research Society (FLAIRS) Conference (FLAIRS 2006), pp. 604–609, 2006.

A. Kaylani, Al-Daraiseh, A., Georgiopoulos, M., Mollaghasemi, M., Anagnostopoulos, G. C., and Wu, A. S., “Genetic Optimization of ART Neural Network Architectures”, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2007), pp. 379–384, 2007.

A. Al-Daraiseh, Kaylani, A., Georgiopoulos, M., Mollaghasemi, M., Anagnostopoulos, G. C., and Kaburlasos, V. G., “Genetically Engineered ART Architectures”, Heidelberg: Springer-Verlag,in Computational Intelligence Based on Lattice Theory,Studies in Computational Intelligence, vol. 67, pp. 233–262, 2007.

R. Miguez, Georgiopoulos, M., and Kaylani, A., “A Genetically Engineered Probabilistic Neural Network”, Nonlinear Analysis: Theory, Methods & Applications, vol. 73, pp. 1783-1791, 2010.

A. Al-Dairaseh, Georgiopoulos, M., Wu, A. S., Anagnostopoulos, G., and Mollaghasemi, M., “GFAM: A genetic algorithm optimization of Fuzzy ARTMAP”, in WCCI 2006 (Fuzzy Systems), Vancouver, Canada, 2006.

A. Al-Dairaiseh, Kaylani, A., Georgiopoulos, M., Mollaghasemi, M., Wu, A. S., and Anagnostopoulos, G., “GFAM Evolving Fuzzy ARTMAP Neural Networks”, Neural Networks journal, vol. 20, pp. 874-892, 2007.

A. Al-Daraiseh, Kaylani, A., Georgiopoulos, M., Wu, A. S., Mollaghasemi, M., and Anagnostopoulos, G. C., “GFAM:Evolving Fuzzy ARTMAP Neural Networks”, vol. 20.8, pp. 874–892, 2007.

H

G. Rabadi, Anagnostopoulos, G. C., and Mollaghasemi, M., “A heuristic algorithm for the just-in-time single machine scheduling problem with setups: A comparison with simulated annealing”, vol. 32, pp. 326–335, 2007.

J. Reeder, Georgiopoulos, M., Castro, J., Burns, S., Anagnostopoulos, G., and Mollaghasemi, M., “Hilbert Space Filling Curve Nearest Neighbor”, in The ISAS and CITSA 2005, Orlando, FL, 2005.

I

M. J. Johnson, Georgiopoulos, M., Mollaghasemi, M., and McGinnis, M., “Improving human behavior in simulations – OneSAF tested baseline intelligent computer generated objects”, in SCS, Military, Government and Aerospace Simulation Symposium 2001 (MGA 2001) Conference, Seattle, Washington, 2001.

M. J. Johnson, Georgiopoulos, M., Mollaghasemi, M., and McGinnis, M., “Improving human behavior in simulations – OneSAF tested baseline intelligent computer generated objects”, in SCS, Military, Government and Aerospace Simulation Symposium 2001 (MGA 2001) Conference, Seattle, Washington, 2001.

J. Reeder, Miguez, R., Sparks, J., Georgiopoulos, M., and Anagnostopoulos, G. C., “Interactively Evolved Modular Neural Networks for Game Agent Control”, IEEE Symposium on Computational Intelligence and Games (CIG 08, pp. 167–174, 2008.

L. Massi, Georgiopoulos, M., Young, C. Y., Ford, C. M., Lancey, P., Bhati, D., and Small, K. A., “Internships and Undergraduate Research: Impact, Support, and Institutionalization of an NSF S-STEM Program through Partnerships with Industry and Funding from Federal and Local Workforce Agencies”, Proceedings of the 120th ASEE Conference and Exposition. Atlanta, GA, June 23-26, 2013.

K

C.Li, M. Georgiopoulos, R. DeMara,, and G.C. Anagnostopoulos, M. Georgiopoulos, “Kernel Principal Subspace Mahalanobis Distances for Outlier Detection”, in International Joint Conference on Neural Networks (IJCNN), San Jose, CA, 2011.

M

M. J. Johnson, Mollaghasemi, M., Georgiopoulos, M., and McGinnis, M., “A methodology for human behavior modeling in computer generated forces”, in Industrial Engineering Research Conference 2001 (IERC 2001), Dallas, TX, 2001.

M. J. Johnson, Mollaghasemi, M., Georgiopoulos, M., and McGinnis, M., “A methodology for human behavior modeling in computer generated forces”, in Industrial Engineering Research Conference 2001 (IERC 2001), Dallas, TX, 2001.

J. Secretan, Georgiopoulos, M., Maidhof, I., Shibly, P., and Hecker, J., “Methods for Parallelizing the Probabilistic Neural Network on a Beowulf Cluster Computer”, 2006.

A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G. C., “M-GFAM:An Elegant Approach to Genetically Optimize Fuzzy ARTMAP Neural Network Architectures”, Proceedings of the 8th International Conference on Natural Computing (ICNC 2007),part of the 10th Joint Conference on Information Sciences (JCIS 2007), pp. 1617-1623, 2007.

A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., “MO-GART: An adaptive multi-objective approach to evolving ART architectures”, IEEE Transactions on Neural Networks, vol. 21, pp. 529-550, 2010.

A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., “MO-GART: An adaptive multi-objective approach to evolving ART architectures”, IEEE Transactions on Neural Networks, vol. 21, pp. 529-550, 2010.

A. Kaylani, Georgiopoulos, M., Mollaghasemi, M., and Anagnostopoulos, G., “MO-GART: Multi-Objective Optimization of ART Architectures”, in 2008 IEEE Congress on Evolutionary Computation (IEEE CEC 2008), Hong Kong, 2008.

T. Zhang, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., “Multi-Objective Model Selection via Racing”, IEEE Transactions on Cybernetics, vol. 46, no. 8, pp. 1863-1876, 2015.

C. Li, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., “Multi-Task Classification Hypothesis Space with Improved Generalization Bounds”, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 7, pp. 1468-1479, 2015.

P

T. Zhang, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., “Pareto Optimal Selection via SPRINT-Race”, IEEE Transactions on Cybernetics, vol. 48, no. 2, pp. 596-610, 2018.

C. Li, M. Georgiopoulos, R. DeMara,, and Anagnostopoulos, G. C., “Pareto-Path Multi-Task Multiple Kernel Learning”, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 1, pp. 51-61, 2015.

J. J. Vargas, DeMara, R. F., Gonzalez, A. J., Georgiopoulos, M., and Marshall, H., “PDU Bundling and Replication for Reduction of Distributed Simulation Communication Traffic”, Journal of Defense Modeling and Simulation, vol. 1.3, pp. 171–183, 2004.

M. Zhong, Hecker, J., Maidhoff, I., Shibly, P., Georgiopoulos, M., Anagnostopoulos, G., and Mollaghasemi, M., “Probabilistic Neural Network: Comparisons of the Cross-Validation Approach and a Fast Heuristic to choose the Smoothing Parameters”, in 2005 Artificial Neural Networks in Engineering, St. Louis, MI, 2005.

M. Zhong, Hecker, J., Maidhoff, I., Shibly, P., Georgiopoulos, M., Anagnostopoulos, G., and Mollaghasemi, M., “Probabilistic Neural Network: Comparisons of the Cross-Validation Approach and a Fast Heuristic to choose the Smoothing Parameters”, in 2005 Artificial Neural Networks in Engineering, St. Louis, MI, 2005.

S

R. Ramirez-Padron, Foregger, D., Manuel, J., Georgiopoulos, M., and Mederos, B., “Similarity Kernels for Nearest Neighbor-based Outlier Detection”, Lecture Notes in Computer Science, vol. 6065/2010, pp. 159-170, 2010.

R. Ramirez-Padron, Foregger, D., Manuel, J., Georgiopoulos, M., and Mederos, B., “Similarity Kernels for Nearest Neighbor-based Outlier Detection”, Lecture Notes in Computer Science, vol. 6065/2010, pp. 159-170, 2010.

C. Sentelle, Anagnostopoulos, G. C., and M. Georgiopoulos, R. DeMara, “A Simple Method for Solving the SVM Regularization Path for Semi-Definite Kernels”, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 4, pp. 709-722, 2016.

M. Georgiopoulos, DeMara, R. F., Gonzalez, A. J., Wu, A. S., Mollaghasemi, M., Gelenbe, E., Kysilka, M., Secretan, J., Sharma, C. A., and Alnsour, A. J., “A Sustainable Model for Integrating Current Topics in Machine Learning Research in the Undergraduate Curriculum”, IEEE Transactions on Education, vol. 52, pp. 503-512, 2009.