Publications of Professor Vassilis G. Kaburlasos
Research Monographs (RM)
Edited Volumes (EV)
[EV#3] Kaburlasos VG (Guest Editor), Special Issue on: Information Engineering Applications Based on Lattices, Information Sciences. vol. 181, iss. 10, pp. 1771-1773, 2011 (16 papers, pp. 1774-2060).
[EV#2] Kaburlasos V, Priss U, and Graña M (eds.), LBM 2008 (CLA 2008), Proceedings of the Lattice-Based Modeling Workshop, in conjunction with The Sixth International Conference on Concept Lattices and Their Applications. Olomouc, Czech Republic: Palacký University, 2008, ISBN: 978-80-244-2112-4.
[EV#1] Kaburlasos VG, and Ritter GX (eds.) Computational Intelligence Based on Lattice Theory. Heidelberg, Germany: Springer, series: Studies in Computational Intelligence, vol. 67, 2007, ISBN: 3-540-72686-9.
Scientific Journal Publications (SJ)
[SJ#33] Kaburlasos VG and Papakostas GA, “Learning distributions of image features by interactive fuzzy lattice reasoning (FLR) in pattern recognition applications”, IEEE Computational Intelligence Magazine, (to be published).
[SJ#32] Papakostas GA, Savio A, Graña M, and Kaburlasos VG, “A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data”, Neurocomputing, vol. 150, part A, pp. 37-42, 2015.
[SJ#31] Jamshidi Y and Kaburlasos VG, “gsaINknn: A GSA optimized, lattice computing knn classifier”, Engineering Applications of Artificial Intelligence, vol. 35, pp. 277-285, 2014.
[SJ#30] Kaburlasos VG and Kehagias A, “Fuzzy inference system (FIS) extensions based on lattice theory”, IEEE Transactions on Fuzzy Systems, vol. 22, no. 3, pp. 531-546, 2014.
[SJ#29] Papadakis SE, Kaburlasos VG, and Papakostas GA, “Two fuzzy lattice reasoning (FLR) classifiers and their application for human facial expression recognition”, Journal of Multiple Valued Logic and Soft Computing, vol. 22, no. 4-6, pp. 561-579, 2014.
[SJ#28] Kaburlasos VG and Pachidis T, “A lattice-computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application”, Information Fusion, vol. 16, pp. 68-83, 2014.
[SJ#27] Kaburlasos VG and Moussiades L, “Induction of formal concepts by lattice computing techniques for tunable classification”, Journal of Engineering Science and Technology Review, vol. 7, no. 1, pp. 1-8, 2014.
[SJ#26] Kaburlasos VG, Papadakis SE, and Papakostas GA, “A lattice computing extension of the FAM neural classifier for human facial expression recognition”, IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 10, pp. 1526-1538, 2013.
[SJ#25] Papakostas GA, Hatzimichailidis AG, and Kaburlasos VG, “Distance and similarity measures between intuitionistic fuzzy sets: a comparative analysis from a pattern recognition point of view”, Pattern Recognition Letters, vol. 34, no. 14, pp. 1609-1622, 2013.
[SJ#24] Hatzimichailidis AG, Papakostas GA, and Kaburlasos VG, “A novel distance measure of intuitionistic fuzzy sets and its application to pattern recognition applications”, International Journal of Intelligent Systems, vol. 27, no. 4, pp. 396-409, 2012.
[SJ#23] Kaburlasos VG, Papadakis SE and Amanatiadis A, “Binary image 2D shape learning and recognition based on lattice computing (LC) techniques”, Journal of Mathematical Imaging and Vision, vol. 42, no. 2-3, pp. 118-133, 2012 (Special Issue on Hybrid Artificial Intelligent Systems. Guest Editors: M. Graña, E. Corchado, M. Wozniak).
[SJ#22] Amanatiadis A, Kaburlasos VG, Gasteratos A and Papadakis SE, “Evaluation of shape descriptors for shape-based image retrieval”, IET Image Processing, vol. 5, iss. 5, pp. 493-499, 2011.
[SJ#21] Papadakis SE and Kaburlasos VG, “Piecewise-linear approximation of nonlinear models based on probabilistically/possibilistically interpreted Intervals’ Numbers (INs)”, Information Sciences, vol. 180, iss. 24, pp. 5060-5076, 2010.
[SJ#20] Kaburlasos VG, Moussiades L, and Vakali A, “Fuzzy lattice reasoning (FLR) type neural computation for weighted graph partitioning”, Neurocomputing, vol. 72, no. 10-12, pp. 2121-2133, 2009 (Special Section on Lattice Computing and Natural Computing. Guest Editor: Manuel Graña).
[SJ#19] Kaburlasos VG and Papadakis SE, “A granular extension of the fuzzy-ARTMAP (FAM) neural classifier based on fuzzy lattice reasoning (FLR)”, Neurocomputing, vol. 72, no. 10-12, pp. 2067-2078, 2009 (Special Section on Lattice Computing and Natural Computing. Guest Editor: Manuel Graña).
[SJ#18] Kaburlasos VG, Marinagi CC, and Tsoukalas VT, “Personalized multi-student improvement based on Bayesian cybernetics”, Computers & Education, vol. 51, no. 4, pp. 1430-1449, 2008.
[SJ#17] Kaburlasos VG, Athanasiadis IN, and Mitkas PA, “Fuzzy Lattice Reasoning (FLR) classifier and its application for ambient ozone estimation”, International Journal of Approximate Reasoning, vol. 45, no. 1, pp. 152-188, 2007.
[SJ#16] Kaburlasos VG, and Kehagias A, “Novel Fuzzy Inference System (FIS) Analysis and Design Based on Lattice Theory”, IEEE Transactions on Fuzzy Systems, vol. 15, no. 2, pp. 243-260, 2007.
[SJ#15] Kaburlasos VG, Papadakis SE, “Granular Self-Organizing Map (grSOM) for Structure Identification”, Neural Networks, vol. 19, no. 5, pp. 623-643, 2006.
[SJ#14] Kaburlasos VG, and Kehagias A. “Novel Fuzzy Inference System (FIS) Analysis and Design Based on Lattice Theory. Part I: Working Principles”, International Journal of General Systems, vol. 35, no. 1, pp. 45-67, 2006.
[SJ#13] Papadakis SE, Tzionas P, Kaburlasos VG, and Theocharis JB, “A Genetic Based Approach to the Type I Structure Identification Problem”, Informatica, vol. 16, no. 3, pp. 365-382, 2005.
[SJ#12] Kaburlasos VG, “FINs: Lattice Theoretic Tools for Improving Prediction of Sugar Production from Populations of Measurements”, IEEE Transactions on Systems, Man and Cybernetics – Part B, vol. 34, no. 2, pp. 1017-1030, 2004.
[SJ#11] Kehagias A, Petridis V, Kaburlasos VG, and Fragkou P, “A Comparison of Word- and Sense-based Text Categorization Using Several Classification Algorithms”, Journal of Intelligent Information Systems, vol. 21, no. 3, pp. 227-247, 2003.
[SJ#10] Petridis V, and Kaburlasos VG, “FINkNN: A Fuzzy Interval Number k-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples”, Journal of Machine Learning Research, vol. 4(Apr), pp. 17-37, 2003.
[SJ#09] Petridis V, Kazarlis S, and Kaburlasos VG, “ACES: An Interactive Software Platform for Self-Instruction and Self- Evaluation in Automatic Control Systems”, IEEE Transactions on Education, vol. 46, no. 1, pp. 102-110, 2003.
[SJ#08] Kaburlasos VG, Spais V, Petridis V, Petrou L, Kazarlis S, Maslaris N, and Kallinakis A, “Intelligent Clustering Techniques for Prediction of Sugar Production”, Mathematics and Computers in Simulation, vol 60, iss. 3-5, pp. 159- 168, 2002 (Special Issue on Intelligent Forecasting, Fault Diagnosis, Scheduling, and Control. Guest Editors: Spyros G. Tzafestas, Elpida S. Tzafestas).
[SJ#07] Petridis V, and Kaburlasos VG, “Clustering and Classification in Structured Data Domains Using Fuzzy Lattice Neurocomputing (FLN)”, IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 2, pp. 245-260, 2001 (Special Section on Connectionist Models for Learning in Structured Domains. Guest Editors: Paolo Frasconi, Marco Gori, Alessandro Sperduti).
[SJ#06] Kaburlasos VG, and Petridis V, “Fuzzy Lattice Neurocomputing (FLN) Models”, Neural Networks, vol. 13, no. 10, pp. 1145-1170, 2000.
[SJ#05] Kaburlasos VG, Petridis V, Brett P, and Baker D, “Estimation of the Stapes-Bone Thickness in Stapedotomy Surgical Procedure Using a Machine-Learning Technique”, IEEE Transactions on Information Technology in Biomedicine, vol. 3, no. 4, pp. 268-277, 1999.
[SJ#04] Petridis V, and Kaburlasos VG, “Learning in the Framework of Fuzzy Lattices”, IEEE Transactions on Fuzzy Systems, vol. 7, no. 4, pp. 422-440, 1999. Errata in IEEE Transactions on Fuzzy Systems, vol. 8, no. 2, p. 236, 2000.
[SJ#03] Petridis V, and Kaburlasos VG, “Fuzzy Lattice Neural Network (FLNN): A Hybrid Model for Learning”, IEEE Transactions on Neural Networks, vol. 9, no. 5, pp. 877-890, 1998 (Special Issue on Neural Networks and Hybrid Intelligent Models: Foundations, Theory, and Applications. Guest Editors: C. Lee Giles, Ron Sun).
[SJ#02] Kaburlasos VG, and Petridis V, “Fuzzy Lattice Neurocomputing (FLN): A Novel Connectionist Scheme for Versatile Learning and Decision Making by Clustering”, International Journal of Computers and Their Applications, vol. 4, no. 2, pp. 31-43, 1997.
[SJ#01] Egbert DD, Goodman PH, Kaburlasos VG, and Whitchey JH, “Generalization Capabilities of Subtle Image Pattern Classifiers”, IEEE Transactions on Knowledge and Data Engineering, vol. 4, no. 2, pp. 172-177, 1992.
Book Chapters (BC) [BC#5] Amanatiadis A, Gasteratos A, Papadakis S, and Kaburlasos V, Image Stabilization in Active Robot Vision. In: Robot Vision, Aleš Ude (ed.), pp. 261-274, 2010. Vukovar, Croatia: In-Teh, ISBN: 978-953-307-077-3. [BC#4] Kaburlasos VG, “Neural/Fuzzy Computing Based on Lattice Theory”. In: Encyclopedia of Artificial Intelligence, Juan Ramón Rabuñal Dopico, Julián Dorado de la Calle, Alejandro Pazos Sierra (eds.), pp. 1238-1243, 2009. Information Science Reference, IGI Global publication. [BC#3] Kaburlasos VG, “Unified Analysis and Design of ART/SOM Neural Networks and Fuzzy Inference Systems Based on Lattice Theory”. In: Computational and Ambient Intelligence, F. Sandoval, A. Prieto, J. Cabestany, M. Graña (eds.), pp. 80-93, 2007. Springer-Verlag, series: Lecture Notes Computer Science (LNCS), vol. 4507, ISBN: 3-540-73006-0. [BC#2] Kaburlasos VG, “Granular Enhancement of Fuzzy-ART/SOM Neural Classifers Based on Lattice Theory”. In Computational Intelligence Based on Lattice Theory, V.G. Kaburlasos and G.X. Ritter (eds.), pp. 3-23, 2007. Heidelberg, Germany: Springer, Series: Studies in Computational Intelligence, vol. 67, ISBN: 3-540-72686-9. (http://www.springer.com/3-540-72686-9). [BC#1] Kaburlasos VG, and Petridis V, “Learning and Decision-Making in the Framework of Fuzzy Lattices”. In New Learning Paradigms in Soft Computing, L.C. Jain and J. Kacprzyk (eds.), pp. 55-96, 2002. Heidelberg, Germany: Physica-Verlag GmbH, Studies in Fuzziness and Soft Computing series, vol. 84, ISBN: 3-7908-1436-9. Other Journal Publications (OJ) [OJ#1] Kaburlasos V, “The Engineering of Scientific Induction”, Journal of Liberal Arts, vol. 4, no. 2, pp. 41-57, 1998. Conference Publications (C) [C#79] Papakostas GA, Papageorgiou EI, Kaburlasos VG, “Granular training of linguistic fuzzy cognitive maps (LFCM) pattern recognition”, Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), Istanbul, Turkey, 2-5 August 2015. [C#78] Maiora J, Papakostas GA, Kaburlasos VG, Graña M, “A proposal of texture features for interactive CTA segmentation by active learning”, KES International Conference on Innovation in Medicine and Healthcare (InMed-14), San Sebastian, Spain, 9-11 July 2014. [C#77] Kaburlasos VG, Tsoukalas V, Moussiades L, “FCknn: a granular knn classifier based on formal concepts”, Proceedings of the World Congress on Computational Intelligence (WCCI) 2014, FUZZ-IEEE Program, Beijing, China, 6-11 July 2014, pp. 61-68. [C#76] Papakostas GA, Kaburlasos VG, “Lattice Computing (LC) meta-representation for pattern classification”, Proceedings of the World Congress on Computational Intelligence (WCCI) 2014, FUZZ-IEEE Program, Beijing, China, 6-11 July 2014, pp. 39-44. [C#75] Tsoukalas VTh, Kaburlasos VG and Skourlas C, “A granular, parametric KNN classifier”, 17th Panhellenic Conference on Informatics (PCI 2013), Thessaloniki, Greece, 19-21 September 2013, pp. 319-326. [C#74] Papakostas GA, Kaburlasos VG and Pachidis Th, “Thermal infrared face recognition based on lattice computing (LC) techniques”, Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013), Hyderabad, India, 7-10 July 2013. [C#73] Kaburlasos VG, Papakostas GA, Pachidis Th and Athinelis A, “Intervals’ numbers (INs) interpolation /extrapolation”, Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013), Hyderabad, India, 7-10 July 2013. [C#72] Kaburlasos VG, “Fuzzy lattice reasoning (FLR) extensions to lattice-valued logic”, 16th Panhellenic Conference on Informatics (PCI 2012), Piraeus, Greece, 5-7 October 2012. IEEE 2012 Copyright, Dimitrios D. Vergados, Costas Lambrinoudakis (Eds.), pp. 445-448. [C#71] Pachidis Th and Kaburlasos VG, “Person identification based on lattice computing k-nearest-neighbor fingerprint classification”, 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES-2012), San Sebastián, Spain, 10-12 September 2012, Advances in Knowledge-Based and Intelligent Information and Engineering Systems. IOS Press, 2012, Manuel Graña, Carlos Toro, Jorge Posada, R. J. Howlett, L. C. Jain (Eds.), pp. 1720-1729. [C#70] Hatzimichailidis AG, Papakostas GA and Kaburlasos VG, “A study on fuzzy D-implications”, Proceedings of the 10th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making (FLINS 2012), Istanbul, Turkey, 26-29 August 2012. World Scientific Proceedings Series on Computer Engineering and Information Science, vol. 7, pp. 708-713.
[C#66] Kaburlasos VG, Amanatiadis A, and Papadakis SE, “2-D shape representation and recognition by lattice computing techniques”, In: Emilio Corchado, Manuel Graña, Alexandre Manhaes Savio (Eds.), Hybrid Artificial Intelligence Systems, Proceedings, Part II of the 5th International Conference (HAIS ‘10), San Sebastián, Spain, 23-25 June 2010, pp. 391-398. Springer-Verlag, series: Lecture Notes in Artificial Intelligence (LNAI), vol. 6077.
[C#65] Amanatiadis A, Kaburlasos VG, Gasteratos A, and Papadakis SE, “A comparative study of invariant descriptors for shape retrieval”, Proceedings of the 2009 IEEE International Workshop on Imaging Systems & Techniques (IST 2009), Shenzhen, China, 11-12 May 2009, pp. 391-394.
[C#64] Hatzimichailidis AG and Kaburlasos VG, “A novel fuzzy implication stemming from a fuzzy lattice inclusion measure”, Proceedings of the Lattice-Based Modeling (LBM 2008) Workshop, in conjunction with The Sixth International Conference on Concept Lattices and their Applications (CLA 2008), Olomouc, Czech Republic, 21-23 October 2008, pp. 59-66.
[C#63] Papadakis SE and Kaburlasos VG, “Computation of a sufficient condition for system input redundancy”, Proceedings of the Lattice-Based Modeling (LBM 2008) Workshop, in conjunction with The Sixth International Conference on Concept Lattices and their Applications (CLA 2008), Olomouc, Czech Republic, 21-23 October 2008, pp. 23-31.
[C#62] Kaburlasos VG and Papadakis SE, “Piecewise-linear approximation of nonlinear models based on Interval Numbers (INs)”, Proceedings of the Lattice-Based Modeling (LBM 2008) Workshop, in conjunction with The Sixth International Conference on Concept Lattices and their Applications (CLA 2008), Olomouc, Czech Republic, 21-23 October 2008, pp. 13-22.
[C#61] Marinagi CC and Kaburlasos VG, “Bayesian Decision Theory for Multi-category Adaptive Testing”, in American Institute of Physics Conference Proceedings 1048, T.E. Simos, G. Psihoyios, Ch. Tsitouras (eds.), pp. 376-379 (International Conference on Numerical Analysis and Applied Mathematics (ICNAAM) 2008, Kos, Greece, 16-20 Sept. 2008).
[C#60] Marinagi CC, Tsoukalas VT, and Kaburlasos VG, “Modifying a client/server architecture to a Web-based architecture for adaptive assessment”, Proceedings entitled “Operations Research and Tourism Development (in Greek)” of the 20th National Conference of the Greek Operations Research Society, Spetses island, Greece, 19-21 June 2008, vol. B, pp. 873-884.
[C#59] Marinagi CC, Kaburlasos VG, and Tsoukalas VT, “An architecture for an adaptive assessment tool”, Proceedings of the 37th ASEE/IEEE Frontiers in Education Conference (FIE 2007), Milwaukee, Wisconsin, 10-13 October 2007, session T3D: Distance Learning Assessment Tools, pp. 11-16.
[C#58] Skourlas C, Alevizos T, Belsis P, Fragos K, Kaburlasos VG, Papadakis S, “Fuzzy Interval Numbers (FINs) techniques and its applications in natural language queries processing and documents classification”, Proceedings of the 3rd Balkan Conference in Informatics (BCI 2007), Sofia, Bulgaria, 27-29 September 2007, pp. 17-28.
[C#57] Kaburlasos VG, Moussiades L, and Vakali A, “Granular graph clustering in the Web”, Joint Conference on Information Sciences (JCIS 2007), Proceedings of the 8th International Conference on Natural Computing (NC 2007), Salt Lake City &, Utah, 18-24 July 2007, pp. 1639-1645.
[C#56] Kaburlasos VG and Papadakis S, “Fuzzy lattice reasoning (FLR) implies a granular enhancement of the fuzzy-ARTMAP classifier”, Joint Conference on Information Sciences (JCIS 2007), Proceedings of the 8th International Conference on Natural Computing (NC 2007), Salt Lake City, Utah, 18-24 July 2007, pp. 1610-1616.
[C#55] Papadakis S and Kaburlasos VG, “Induction of classification rules from histograms”, Joint Conference on Information Sciences (JCIS 2007), Proceedings of the 8th International Conference on Natural Computing (NC 2007), Salt Lake City Utah, 18-24 July 2007, pp. 1646-1652.
[C#54] Alevizos T, Kaburlasos VG, Papadakis S, Skourlas C, and Belsis P, “Fuzzy interval number (FIN) techniques for multilingual and cross language information retrieval”, Proceedings of the 9th International Conference on Enterprise Information Systems (ICEIS 2007), Funchal, Madeira - Portugal, 12-16 June 2007, pp. 348-355.
[C#53] Alevizos T, Kaburlasos VG, Papadakis S, and Skourlas C, “Fuzzy interval numbers (FINs) techniques and applications”, Proceedings of the 11th Panhellenic Conference in Informatics (PCI 2007), Patras, Greece, 18-20 May 2007, vol. B, pp. 255-264.
[C#52] Marinagi CC, and Kaburlasos VG, “Work in Progress - Practical Computerized Adaptive Assessment based on Bayesian decision theory”, Proceedings of the 36th ASEE/IEEE Frontiers in Education Conference (FIE 2006), San Diego, CA, 28- 31 October 2006.
[C#51] Kaburlasos VG, Christoforidis A, “Granular auto-regressive moving average (grARMA) model for predicting a distribution from other distributions. Real-world applications”, Proceedings of the World Congress on Computational Intelligence (WCCI) 2006, FUZZ-IEEE Program, Vancouver, BC, Canada, 16-21 July 2006, pp. 791-796.
[C#50] Athanasiadis IN, and Kaburlasos V, “Air quality assessment using Fuzzy Lattice Reasoning (FLR)”, Proceedings of the World Congress on Computational Intelligence (WCCI) 2006, FUZZ-IEEE Program, Vancouver, BC, Canada, 16-21 July 2006, pp. 231-236.
[C#49] Hatzimichailidis A, Kaburlasos V, Papadopoulos B, “An Implication in Fuzzy Sets”, Proceedings of the World Congress on Computational Intelligence (WCCI) 2006, FUZZ-IEEE Program, Vancouver, BC, Canada, 16-21 July 2006, pp. 203- 208.
[C#48] Marinagi C, Alevizos T, Kaburlasos VG, and Skourlas C, “Fuzzy interval number (FIN) techniques for cross language information retrieval”, Proceedings of the 8th International Conference on Enterprise Information Systems (ICEIS 2006), Paphos, Cyprus, 23-27 May 2006.
[C#47] Marinagi CC, Tsoukalas VT, Kaburlasos VG, “PARES: A software platform for adaptive evaluation and self-evaluation of students”, Proceedings of the 3rd International Conference on Open and Distance Learning (ICODL 2005) – Applications of Pedagogy and Technology, Patras, Greece, 11-13 November 2005, vol. A, pp. 638-650.
[C#46] Chatzis V, Kaburlasos VG, and Theodorides M, “An Image Processing Method for Particle Size and Shape Estimation”, Proceedings of the 2rd International Scientific Conference on Computer Science, Chalkidiki, Greece, 30 September - 2 October 2005.
[C#45] Papadakis SE, and Kaburlasos VG, “mass-grSOM: A Flexible Rule Extraction for Classification”, 5th Workshop on Self- Organizing Maps (WSOM 2005), Paris, France, 5-8 September 2005, pp. 553-560.
[C#44] Kaburlasos VG, Chatzis V, Tsiantos V, and Theodorides M, “granular Self-Organizing Map (grSOM) neural network for industrial quality control”, SPIE Symposium on Optics & Photonics (SPIE 2005) International Conference on Fuzzy Set Theory and Neural Network Methods in Image Analysis and Pattern Recognition, San Diego, California, 31 July - 4 August 2005.
[C#43] Marinagi CC, Tsoukalas VT, and Kaburlasos VG, “Development and use of a software tool for improving the average student performance in the Greek higher education system”, Frontiers in Education Conference (FIE 2004), Savannah, GA, 20-23 October 2004.
[C#42] Kaburlasos VG, Marinagi CC, and Tsoukalas VT, “PARES : A software tool for computer-based testing and evaluation used in the Greek higher education system”, Proceedings of the 4th IEEE International Conference on Advanced Learning Technologies (ICALT 2004), Joensuu, Finland, 30 August – 1 September 2004, pp. 771-773.
[C#41] Kaburlasos VG, and Kehagias A, “Novel analysis and design of fuzzy inference systems based on lattice theory”, Proceedings IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2004), Budapest, Hungary, 25-29 July 2004, pp. 281-286.
[C#40] Kaburlasos VG, Papadakis SE, “grSOM: A granular extension of the self-organizing map for structure identification applications”, Proceedings IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2004), Budapest, Hungary, 25- 29 July 2004, pp. 789-794.
[C#39] Papadakis SE, Marinagi CC, Kaburlasos VG, and Theodorides MK, “Estimation of industrial production using the granular Self-Organizing Map (grSOM)”, Proceedings of the 12th Mediterranean Conference on Control and Automation (MED’04), Kusadasi, Turkey, 6-9 June 2004, session TuM2-D.
[C#38] Kaburlasos VG, “A device for linking brain to mind based on lattice theory”, Proceedings of the 8th International Conference on Cognitive and Neural Systems (ICCNS 2004), Boston University, Boston, MA, 19-22 May 2004, p. 58.
[C#37] Kaburlasos VG, Papadakis SE, and Kazarlis S, “A Genetically Optimized Ensemble of s-FLNMAP Neural Classifiers Based on Non-Parametric Probability Distributions Functions”, Proceedings of the 2003 International Joint Conference on Neural Networks (IJCNN 2003), Portland, OR, 20-24 July 2003, vol. 1, pp. 426-431.
[C#36] Kaburlasos VG, “Improved Fuzzy Lattice Neurocomputing (FLN) for Semantic Neural Computing”, Proceedings of the 2003 International Joint Conference on Neural Networks (IJCNN 2003), Portland, OR, 20-24 July 2003, vol. 3, pp. 1850-1855.
[C#35] Cripps A, Nguyen N, and Kaburlasos VG, “Three Improved Fuzzy Lattice Neurocomputing (FLN) Classifiers”, Proceedings of the 2003 International Joint Conference on Neural Networks (IJCNN 2003), Portland, OR, 20-24 July 2003, vol. 3, pp. 1957-1962.
[C#34] Kaburlasos VG, Moussiadis L, Tsoukalas V, Iliopoulou A, and Alevizos T, “Adaptive Technological Education Delivery and Student Examination Based on Machine-Learning Tools”, 13th International Conference on Artificial Neural Networks & 10th International Conference Neural Information Processing (ICANN/ICONIP 2003), Istanbul, Turkey, 26 – 29 June 2003, (invited paper in Special Session SS05: Machine Learning Advances for Engineering Education).
[C#33] Athanasiadis IN, Kaburlasos VG, Mitkas PA, and Petridis V. “Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support”, Proceedings 1st Intl. NAISO Symposium on Information Technologies in Environmental Engineering (ITEE’2003), Gdansk, Poland, 24-27 June 2003. Technical Session 2: Practical Applications and Experiences. Abstract in ICSC-NAISO Academic Press, Canada (ISBN:3906454339), p.51.
[C#32] Cripps A, Kaburlasos VG, Nguyen N, and Papadakis SE, “Improved Experimental Results Using Fuzzy Lattice Neurocomputing (FLN) Classifiers”, International Conference on Machine Learning; Models, Technologies and Applications (MLMTA’03), Las Vegas, NV, 23-26 June 2003, pp. 161-166.
[C#31] Kaburlasos VG, and Petridis V, “Improved Prediction of Industrial Yield Based on Tools from a Normed Linear Space of Fuzzy Interval Numbers (FINs)”, Proceedings 11th Mediterranean Conference on Control and Automation (MED’03), Rhodes, Greece, 18-20 June 2003, session FM1-B.
[C#30] Kaburlasos VG, and Kazarlis S, “σ-FLNMAP with Voting (σFLNMAPwV): A Genetically Optimized Ensemble of Classifiers With the Capacity to Deal With Partially-Ordered, Disparate Types of Data. Application to Financial Problems”, Proceedings 4th Intl. Conference on Technology & Automation, Thessaloniki, Greece, 5-6 October 2002, pp. 276-281.
[C#29] Kaburlasos VG, “Novel Fuzzy System Modeling for Automatic Control Applications”, Proceedings 4th Intl. Conference on Technology & Automation, Thessaloniki, Greece, 5-6 October 2002, pp. 268-275.
[C#28] Petridis V, Kaburlasos VG, Fragkou P, and Kehagias A, “Text Classification Using the σ-FLNMAP Neural Network”, Proceedings of the 2001 International Joint Conference on Neural Networks (IJCNN’2001), Washington D.C., 14-19 July 2001, vol. 2, pp. 1362-1367.
[C#27] Petridis V, Petrou L, Kaburlasos VG, Spais V, and Kazarlis S, “Models for predicting sugar production in Greece,” Proceedings of the Panhellenic Conference on Automation, Robotics and Industrial Production – The Role of Information Technologies, Santorini, Greece, 28-30 June 2001.
[C#26] Kaburlasos VG, Spais V, Petridis V, Petrou L, Kazarlis S, Maslaris N, and Kallinakis A, “Intelligent Clustering Techniques for Prediction of Sugar Production”, Proceedings of the European Workshop on Intelligent Forecasting, Diagnosis and Control, Santorini, Greece, 24-28 June 2001.
[C#25] Petridis V, Kaburlasos VG, Kazarlis S, Petrou L and Hassapis G, “Simulation and hypertext: Real time educational software systems” (in Greek), Proceedings of the Panhellenic Conference on Greek Education Research, Athens, Greece, 21-23 September 2000, pp. 200-206.
[C#24] Petridis V, and Kaburlasos VG, “An Intelligent Mechatronics Solution for Automated Tool Guidance in the Epidural Surgical Procedure”, Proceedings of the 7th Annual Conference on Mechatronics and Machine Vision in Practice (M2VIP’00), Hervey Bay, Australia, 19-21 September 2000, pp. 201-206.
[C#23] Petridis V, and Kaburlasos VG, “Modeling of Systems Using Heterogeneous Data”, Proceedings of the 1999 IEEE International Conference Systems, Man & Cybernetics (IEEE SMC’99), Tokyo, Japan, 12-15 October 1999, session FQ04, pp. V308-V313.
[C#22] Kaburlasos VG, and Petridis V, “Regression on Heterogeneous Fuzzy Data”, Proceedings of the 7th European Congress on Intelligent Techniques and Soft Computing (EUFIT’99), Aachen, Germany, 13-16 September 1999, session CC2.
[C#21] Petridis V, Kaburlasos V, and Kehagias A, “Application of Intelligent Control Techniques in Surgical Operations” (in Greek), Proceedings of the 2nd Conference on Technology and Automation, Thessaloniki, Greece, 2-3 October 1998, pp. 182-187.
[C#20] Kaburlasos VG, and Petridis V, “A Unifying Framework for Hybrid Information Processing”, Proceedings of the ISCA 7th International Conference on Intelligent Systems (ICIS’98), Paris, France, 1-3 July 1998, pp. 68-71.
[C#19] Kaburlasos VG, Petridis V, Brett P, and Baker D, “Learning a Linear Association of Drilling Profiles in Stapedotomy Surgery”, Proceedings of the IEEE 1998 International Conference on Robotics & Automation (ICRA’98), Leuven, Belgium, 16-20 May 1998, vol.1, pp. 705-710.
[C#18] Kaburlasos VG, Petridis V, Brett P, and Baker D, “On-line Estimation of the Stapes-Bone Thickness in Stapedotomy by Learning a Linear Association of the Force and Torque Drilling Profiles”, Proceedings of the IASTED 1997 International Conference on Intelligent Information Systems (ISS’97), Grand Bahama Island, Bahamas, 8-10 December 1997, pp. 80- 84.
[C#17] Kaburlasos V, Petridis V, Allotta B, and Dario P, “Automatic Detection of Bone Breakthrough in Orthopedics by Fuzzy Lattice Reasoning (FLR) : The Case of Drilling in the Osteosynthesis of Long Bones”, Proceedings of the Mechatronical Computer Systems for Perception and Action (MCPA’97), Pisa Italy, 10-12 February 1997, pp. 33-40.
[C#16] Petridis V, and Kaburlasos VG, “FLN: A Fuzzy Lattice Neurocomputing Scheme for Clustering”, Proceedings of the 1996 World Congress on Neural Networks, San Diego CA, 15-20 September 1996, pp. 942-945.
[C#15] Kaburlasos VG, and Petridis V, “Fuzzy Lattice Neurocomputing (FLN)”, Proceedings of the Fifth International Conference on Intelligent Systems, Reno NV, 19-21 June 1996, pp. 56-60.
[C#14] Petridis V, Kaburlasos VG, Brett P, Parker T, and Day JCC, “Two Level Fuzzy Lattice (2L-FL) Supervised Clustering : A New Method for Soft Tissue Identification in Surgery”, Proceedings of the CESA / IMACS 1996 Multiconference, Lille France, 9-12 July 1996, pp. 232-237.
[C#13] Petridis V, Kaburlasos V, Paterakis E., and Kehagias A, “Fuzzy, Neural, and Genetic Methods for Intelligent Control” (in Greek), Proceedings of the 1995 Conference on Intelligent Control Systems, Athens Greece, 14-15 December 1995, pp. 93-97.
[C#12] Goodman PH, Kaburlasos VG, Egbert DD, Carpenter GA, Grossberg S, Reynolds JH, Rosen DB, and Hartz AJ, “Fuzzy ARTMAP neural network compared to linear discriminant analysis prediction of the length of hospital stay in patients with pneumonia,” in Fuzzy Logic Technology & Applications, R.J. Marks II (ed.), chapter 11 Bioengineering, 1994. New York, NY: IEEE Press (Proceedings of the IEEE 1992 Intl. Conf. on Systems, Man and Cybernetics, Chicago IL, 18- 21 October 1992, vol. 1, pp. 748-753).
[C#11] Kelly AJ, Goodman PH, Kaburlasos VG, Egbert DD, and Hardin ME, “Neural Network Prediction of Child Sexual Abuse”, Clinical Research, vol. 40, iss. 1, pp. A99, 1992.
[C#10] Goodman PH, Kaburlasos VG, Egbert DD, Carpenter GA, Grossberg S, Reynolds JH, Hammermeister K, Marshall G, and Grover F, “Fuzzy ARTMAP Neural Network Prediction of Heart Surgery Mortality”, Proc. of the Wang Conference on Neural Networks Learning, Recognition, and Control, 1992, pp. 48.
[C#09] Whitchey JH, Egbert DD, Kaburlasos VG, and Goodman PH, “Unsupervised Neural Network Discrimination of Subtle Image Patterns”, Proceedings of the 1991 Golden West Conference on Intelligent Systems, Reno NV, 3-5 June 1991, pp. 1-8.
[C#08] Kaburlasos VG, Egbert DD, and Rao M, “A Hardware Implementation of the Adaptive Resonance Theory Neural Network”, Proceedings of the 1991 Golden West Conference on Intelligent Systems, Reno NV, 3-5 June 1991, pp. 21- 28.
[C#07] Kaburlasos VG, Publicover NG, Egbert DD, Liu G, and Burbey IE, “Monitoring the Propagation of Electrical Excitation in Smooth Muscle Tissue: A B-Spline Approach”, Proceedings of the IASTED 1990 International Conference on Artificial Intelligence Applications and Neural Networks, Zurich Switzerland, 25-27 June 1990.
[C#06] Egbert DD, Kaburlasos VG, and Goodman PH, “Neural Network Discrimination of Subtle Image Patterns”, Proceedings of the IEEE 1990 International Joint Conference on Neural Networks (IJCNN’90), San-Diego CA, 14-17 June 1990, vol. 1, pp. 517-524.
[C#05] Kaburlasos VG, Tacker EC, and Egbert DD, “A Plastic Self-Adaptive Learning Machine for Pattern Recognition”, Proceedings of the 1989 IEEE International Conference on Systems, Man and Cybernetics, Cambridge MA, 14-17 November 1989, vol. 2, pp. 824-827.
[C#04] Egbert DD, Kaburlasos VG, and Goodman PH, “Invariant Feature Extraction for Neurocomputer Analysis of Biomedical Images”, Proceedings of the Second Annual IEEE Symposium on Computer-Based Medical Systems, Univ. of Minnesota, 26-27 June 1989, pp. 69-73.
[C#03] Kaburlasos VG, Egbert DD, and Tacker EC, “Self-Adaptive Multidimensional Euclidean Neural Networks for Pattern Recognition”, Proceedings of the IEEE 1989 International Joint Conference on Neural Networks (IJCNN’89), Washington DC, 18-22 June 1989, vol. 2, pp. 595.
[C#02] Goodman PH, Egbert DD, and Kaburlasos VG, “Whiplash Detection using Neural Network Processing of Infrared Thermograms”, Proceedings of the 18th Annual Meetings American Academy of Thermology, Johns Hopkins, 17-19 May 1989, and an abstract in The Journal of the American Academy of Thermology and The Intl College of Thermology, Vol. 3, No. 2, 1989, pp. 139.
[C#01] Kaburlasos VG, Egbert DD, and Goodman PH, “Neurocomputing Classification of Biomedical Image Patterns”, Proceedings of the International Society for Mini and Microcomputers (ISMM) International Conference on Computer Applications in Design Simulation and Analysis, Reno NV, 22-24 Feb. 1989.