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.
Y. Jamshidi, V.G. Kaburlasos, “gsaINknn: A GSA optimized, lattice computing knn classifier”, Engineering Applications of Artificial Intelligence, vol. 35, pp. 277-285, 2014.
G.A. Papakostas, V.G. Kaburlasos, “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.
J. Maiora, G.A. Papakostas, V.G. Kaburlasos, M. Graña, “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, pp. 311-320.
G.A. Papakostas, A.G. Hatzimichailidis, V.G. Kaburlasos, “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.
V.G. Kaburlasos, S.E. Papadakis, G.A. Papakostas, “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.
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