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Applied Artificial Intelligence Methods

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Course Unit Code450-4049/03
Number of ECTS Credits Allocated4 ECTS credits
Type of Course Unit *Optional
Level of Course Unit *Second Cycle
Year of Study *Second Year
Semester when the Course Unit is deliveredSummer Semester
Mode of DeliveryFace-to-face
Language of InstructionCzech
Prerequisites and Co-Requisites Course succeeds to compulsory courses of previous semester
Name of Lecturer(s)Personal IDName
POK40prof. Dr. Ing. Miroslav Pokorný
CER275prof. Ing. Martin Černý, Ph.D.
KUB631Ing. Jan Kubíček, Ph.D.
Subject deals with gathering of knowledge and applications of the artificial intelligence methods in the context of processing and modeling of the biomedical image data. Subject is composed from four main areas of the artificial intelligence. The first part of the subject deals with the fuzzy mathematics, fuzzy modeling, and design of the expert systems. The second part of the subject deals with the data classification with emphasis to an area of the neural network. Next area deals with optimization techniques with emphasis of an analysis of the genetic algorithms for solving of the complex mathematical problems. The last part of the subject focuses to hierarchical and non-hierarchical methods of the cluster analysis.
Learning Outcomes of the Course Unit
The subject represents the introduction to the principles of scientific field of artificial intelligence. The goal of subject is introduce students on analysis and design of artificial intelligence tolls in the field of biomedical engineering.
Students will be ready for practical use of basic artificial intelligence tools namely fuzzy expert systems, artificial neural networks and genetic algorithms in the field of BME.
Course Contents

1. Introduction to the artificial intelligence.
2. Principles and methods of the artificial intelligence. Methods of computer representation, knowledge, and linguistic modeling.
3. Basic of the fuzzy mathematics, and fuzzy logic.
4. Fuzzy expert systems.
5. Fuzzy models.
6. Data classification: basic methods, principles, and applications in the biomedicine.
7. Neural networks: basic principles, topologies, types of the neural networks, and applications for the classification and prediction of the biomedical image data.
8. Basic methods and applications of the optimization methods for processing of the biomedical image data.
9. Genetic and evolutionary algorithms for solving the complex optimization problems.
10. Hierarchical and non-hierarchical methods of the clustering analysis.

Computer Exercises:

1. Introduction to mathematical modeling in the SW MATLAB.
2. Functionalities of the artificial intelligence in the SW MATLAB.
3. Mathematical applications of the fuzzy mathematics.
4. Design and realization of the fuzzy expert systems.
5. Application of the fuzzy modeling on real biomedical examples.
6. Implementation of selected classification algorithms in a context of the biomedical applications.
7. Design and realization of the neural networks in the MATLAB for solving the classification and prediction tasks.
8. Application of the optimization techniques for solving the complex mathematical issues.
9. Implementation of the selected genetic algorithms in an area of the biomedical signal and image processing.
10. Implementation of the clustering analysis methods for segmentation and classification of the biomedical data.
Recommended or Required Reading
Required Reading:
RUSSEL,S., NORVIG,P.: Artificial Intelligence, Prentice-Hall, Inc., 2003, ISBN 0-13-080302-2
LUGER,G.F., STUBBLEFIELD,W.A.: Artificial Intelligence, The Benjamin/Cummings Publishing Company, Inc., 2009, ISBN-13: 978-0-321-54589-3 ISBN-10: 0-321-54589-3
ZIMMERMANN,H.J. Fuzzy Set Theory - and Its Applications. Kluwer Academic Publishers, 2001. ISBN-13: 978-0792374350
HUDSON, D. L. a M. E. COHEN. Neural networks and artificial intelligence for biomedical engineering. New York: Institute of Electrical and Electronics Engineers, c2000. ISBN 978-0780334045.
AGAH, Arvin. Medical applications of artificial intelligence. Boca Raton: CRC Press/Taylor & Francis Group, 2014. ISBN 9781439884331.

POKORNÝ,M.,SROVNAL,V. Systémy s umělou inteligencí - Učební text a návody do cvičení. CZ.1.07/2.2.00/15.0113. VŠB - Technická univerzita Ostrava. Ostrava. 2012
VESELÝ,A. Úvod do umělé inteligence. ČZU Praha, 2005. ISBN 80-213-1361-7
JURA, P. Základy fuzzy logiky pro řízení a modelování. Brno: Nakladatelství VUTIUM, 2003, ISBN 80-214-2261-0.
VONDRÁK, Ivo. Umělá inteligence a neuronové sítě. Ostrava: VŠB - Technická univerzita Ostrava, 1994. ISBN 80-7078-259-5.
Recommended Reading:
C. R. REEVES, J. E. ROW,. Genetic Algorithms: Principles and Perspectives. Kluwer Academic Publishers, New York, 2002.
GRAUPE,D. Principles of Artificial Neural Netvorks. World Scientific. 2013. ISBN: 978-981-4522-73-1
BEGG, Rezaul., Daniel T. H. LAI a Marimuthu. PALANISWAMI. Computational intelligence in biomedical engineering. Boca Raton: CRC Press, c2008. ISBN 9780849340802.
SHUKLA, Anupam a Ritu TIWARI. Intelligent medical technologies and biomedical engineering: tools and applications. Hershey, PA: Medical Information Science Reference, c2010. ISBN 1615209778.

VOLNÁ,E. Neuronové sítě. Ostravská univerzita, Ostrava. 2008
HYNEK,J. Genetické algoritmy a genetické programování. Grada, 2008. ISBN: 978-80-247-2695-3
Planned learning activities and teaching methods
Lectures, Tutorials, Experimental work in labs
Assesment methods and criteria
Task TitleTask TypeMaximum Number of Points
(Act. for Subtasks)
Minimum Number of Points for Task Passing
Graded creditGraded credit100 (100)51
        Protocols from laboratory exercisesLaboratory work40 20
        Written examWritten test60 30