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.
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.
V.G. Kaburlasos, G.A. Papakostas, T. Pachidis, A. Athinellis, “Intervals’ numbers (INs) interpolation /extrapolation”, Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2013), Hyderabad, India, 7-10 July 2013.
G.A. Papakostas, V.G. Kaburlasos, T. Pachidis, “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.
V.T. Tsoukalas, V.G. Kaburlasos, C. Skourlas, “A granular, parametric KNN classifier”, 17th Panhellenic Conference on Informatics (PCI 2013), Thessaloniki, Greece, 19-21 September 2013, pp. 319-326.
V.G. Kaburlasos, S.E. Papadakis, A. Amanatiadis, “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: Manuel Graña, Emilio Corchado, Michal Wozniak).
Greek
