450-4049/04 – Applied Artificial Intelligence Methods (AUI)

Gurantor departmentDepartment of Cybernetics and Biomedical EngineeringCredits4
Subject guarantordoc. Ing. Martin Černý, Ph.D.Subject version guarantordoc. Ing. Martin Černý, Ph.D.
Study levelundergraduate or graduateRequirementOptional
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
CER275 doc. Ing. Martin Černý, Ph.D.
KUB631 Ing. Jan Kubíček, Ph.D.
PET497 Ing. Lukáš Peter, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Graded credit 2+2
Part-time Graded credit 0+16

Subject aims expressed by acquired skills and competences

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.

Teaching methods

Experimental work in labs


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.

Compulsory literature:

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.

Recommended literature:

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.

Way of continuous check of knowledge in the course of semester

Control during semestr is on the basis of student participation in laboratory exercises. Conditions for awarding a classified credit: The student can achieve 40 points for protocols from laboratory exercises and a test of practical skills. The minimum number of points to be awarded is 20. To complete the course the student has to pass a written test focused on theoretical knowledge with a minimum of 30 points out of 60 possible.


Other requirements

There are not defined other requirements for students


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

Lectures: 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. Practical 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.

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Graded credit Graded credit 100 (100) 51
        Protocols from laboratory exercises Laboratory work 40  20
        Written test Written test 60  30
Mandatory attendence parzicipation: protocols from laboratory exercises and a written test

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2020/2021 (N0988A060002) Biomedical Engineering MZD P English Ostrava 2 Compulsory study plan
2020/2021 (N0988A060002) Biomedical Engineering MZD K English Ostrava 2 Compulsory study plan
2020/2021 (N0714A060007) Applied Electronics P English Ostrava 2 Optional study plan
2020/2021 (N0716A060002) Automotive Electronic Systems P English Ostrava 2 Optional study plan
2019/2020 (N0988A060002) Biomedical Engineering MZD P English Ostrava 2 Compulsory study plan
2019/2020 (N0988A060002) Biomedical Engineering MZD K English Ostrava 2 Compulsory study plan
2019/2020 (N0714A060007) Applied Electronics P English Ostrava 2 Optional study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner
V - ECTS - mgr. 2021/2022 Full-time English Optional 401 - Study Office stu. block
V - ECTS - mgr. 2020/2021 Full-time English Optional 401 - Study Office stu. block