Publications

@article{Pavlova2019c,
title = {Comparative analysis of methods for ascertainment the similarity between reflected spectra obtained from skin lesions},
author = {Petya Em. Pavlova and Nikola G Shakev and Ekaterina G Borisova},
doi = {10.1016/j.ifacol.2019.12.552},
issn = {2405-8963},
year = {2019},
date = {2019-01-01},
journal = {IFAC PAPERSONLINE},
volume = {52},
number = {25},
pages = {365--369},
publisher = {ELSEVIER},
address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
institution = {Int Federat Automat Control; Int Federat Automat Control Tech Comm 9 5 Technol Culture & Int Stabil; Int Federat Automat Control Tech Comm 3 2 Computat Intelligence Control; Int Federat Automat Control Tech Comm 4 2 Mechatron Syst; Int Federat Automat Con},
abstract = {The presented investigation demonstrates the results of tests of abilities for classification of reflected spectra from human cutaneous lesions. Typical peculiarity for these spectra is big deviation for collections obtained from healthy skin areas and from lesions with same diagnoses. Two different approaches are used to ascertain similarity: calculation of the distance to the point of full identity in area of chromaticity and neural network identification. The test is provided on data from patients with different histologically fixed diagnoses. Each diagnose is presented by etalon distribution. The test samples are preliminary processed transforming the distributions to normalized spectra, which are ratio between spectra measured from healthy and damaged skin. The etalon distributions are extracted by averaging the ratio-spectra. Methods are compared by criterion of number coincidence of histological and classified diagnoses. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{ISI000505060100014,
title = {Smart Shopping Cart Learning Agents Development},
author = {Dilyana V Budakova and Lyudmil G Dakovski and Veselka S Petrova-Dimitrova},
doi = {10.1016/j.ifacol.2019.12.447},
issn = {2405-8963},
year = {2019},
date = {2019-01-01},
journal = {IFAC PAPERSONLINE},
volume = {52},
number = {25},
pages = {64--69},
publisher = {ELSEVIER},
address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
abstract = {The paper describes the design, implementation and user evaluation of utility and goal-based intelligent learning agents for smart shopping cart. In keeping user's shopping habits rules and shopping list, they guide visitors through the shops and the goods in the shopping center or according to new promotions in the shops, respectively. It is envisaged that concrete implementation of the shopping agents will be running on each shopping cart in the shopping centers. The k-d decision tree, the smallest identification tree and reinforcement-learning algorithm are used for agents learning. Some initial user opinions of the shopping cart agents are presented. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Boeva2019204,
title = {Bipartite Split-Merge Evolutionary Clustering},
author = {V Boeva and M Angelova and V M Devagiri and E Tsiporkova},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077496461&doi=10.1007%2f978-3-030-37494-5_11&partnerID=40&md5=c14d95b85212f47ed1b43c71463b0cd1},
doi = {10.1007/978-3-030-37494-5_11},
issn = {03029743},
year = {2019},
date = {2019-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {11978 LNAI},
pages = {204-223},
note = {cited By 0},
keywords = {},
pubstate = {published},
tppubtype = {article}
}