The operation of a machine in the “physical” environment is typically supported by arithmetic models. However, when a human is involved there might emerge non-numerical data…
Our orientation is toward the development of machines with a capacity to interact with humans in various applications including education, precision farming, patrolling in the physical environment and other.
Our orientation is toward the conversion of our laboratory prototypes in commercial products.
A.G. Hatzimichailidis, G.A. Papakostas, V.G. Kaburlasos, “A distance measure based on fuzzy D-implications: application in pattern recognition”, British Journal of Mathematics & Computer Science, vol. 14, no. 3, pp. 1-14, 2016.
V.G. Kaburlasos, G.A. Papakostas, “Learning distributions of image features by interactive fuzzy lattice reasoning (FLR) in pattern recognition applications”, IEEE Computational Intelligence Magazine, vol. 10, no. 3, pp. 42–51, 2015 (Special Issue on New Trends of Learning in Computational Intelligence. Guest Editors: Guang-Bin Huang, Erik Cambria, Kar-Ann Toh, Bernard Widrow, Zongben Xu).
G.A. Papakostas, A. Savio, M. Graña, V.G. Kaburlasos, “A lattice computing approach to Alzheimer’s disease computer assisted diagnosis based on MRI data”, Neurocomputing, vol. 150, part A, pp. 37-42, 2015 (Special Issue on Bioinspired and knowledge based techniques and applications. Guest Editors: Manuel Graña and Bogdan Raducabu).
V.G. Kaburlasos, T. Pachidis, “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 (Special Issue on Information Fusion in Hybrid Intelligent Fusion Systems. Guest Editors: Michal Wozniak, Emilio Corchado and Manuel Graña).
V.G. Kaburlasos, A. Kehagias, “Fuzzy inference system (FIS) extensions based on lattice theory”, IEEE Transactions on Fuzzy Systems, vol. 22, no. 3, pp. 531-546, 2014.
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