450-4049/05 – Applied Artificial Intelligence Methods (AUI)

Gurantor departmentDepartment of Cybernetics and Biomedical EngineeringCredits5
Subject guarantorprof. Ing. Martin Černý, Ph.D.Subject version guarantorprof. Ing. Martin Černý, Ph.D.
Study levelundergraduate or graduateRequirementCompulsory
Study languageCzech
Year of introduction2022/2023Year of cancellation
Intended for the facultiesEKFIntended for study typesFollow-up Master
Instruction secured by
LoginNameTuitorTeacher giving lectures
CER275 prof. Ing. Martin Černý, Ph.D.
KUB631 Ing. Jan Kubíček, Ph.D.
LAN177 RNDr. Miroslav Langer, Ph.D.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Credit and Examination 2+2

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

Podmínky zápočtu: (celkem 40 bodů, minimum pro získání zápočtu 21) 20b Realizace fuzzy modelu (protokol nebo SW) 20b Vytvoření vlastního pravidlového systému v prostředí Clips Podmínky vykonání zkoušky (celkem 60 bodů) 40 bodů písemná/praktická část - realizace fuzzy modelu nebo pravidlového systému v prostředí Clips dle zadání 20 bodů ústní zkouška na teoretické znalosti.


Other requirements

Compulsory participation in seminars 80% of seminars.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

Lectures 1. Principles and methods of artificial intelligence. Methods of computer representation of knowledge and language modeling. 2. Basics of fuzzy mathematics and fuzzy logic. 3. Fuzzy expert systems. 4. Fuzzy models. 5. Methods for verifying the design of fuzzy models 6. Basics of graph theory, definitions, graph search methods, problem, state space 7. Introduction to knowledge systems - definition, brief history, applications 8. Architecture of knowledge systems, knowledge base and fact base 9. Inference mechanism 10. Problems of the "select" function, quantitative and qualitative heuristics 11. Other modules of knowledge systems 12. Introduction to knowledge engineering, life cycle of knowledge system Computer exercises 1. Introduction to mathematical modeling in MATLAB. 2.Methodology of fuzzy model design in MATLAB environment 3. Design of a fuzzy model focused on economics 4. Debugging fuzzy custom fuzzy models 5. Presentation of created fuzzy models 6. Shell Expert System Clips - introduction to working with the system 7. Variable, definition of facts and rules 8. Lists 9. Working with facts 10. Lists and multivalued variables 11. Auxiliary facts and priority of rules 12. Templates, subsetp command

Conditions for subject completion

Full-time form (validity from: 2022/2023 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of pointsMax. počet pokusů
Credit and Examination Credit and Examination 100 (100) 51
        Credit Credit 40 (40) 21
                Návrh vlastního fuzzy modelu Project 20  5
                Vytvoření vlastního pravidlového systému v prostředí Clips Project 20  5
        Examination Examination 60 (60) 30 3
                Realizace fuzzy modelu nebo pravidlového systému v prostředí Clips dle zadání Other task type 40  20
                Ústní zkouška Oral examination 20  5
Mandatory attendence participation: Credit conditions: (total 40 points, minimum for obtaining credit 21) 20b Implementation of fuzzy model (protocol or SW) 20b Creating your own rule system in the Clips environment Exam conditions (total 60 points) 40 points written / practical part - implementation of fuzzy model or rule system in Clips environment according to assignment 20 points oral exam for theoretical knowledge. the maximum leave of absence is 20 %

Show history

Conditions for subject completion and attendance at the exercises within ISP: Splnění všech povinných úkolů v individuálně dohodnutých termínech./Completion of all mandatory tasks within individually agreed deadlines

Show history

Occurrence in study plans

Academic yearProgrammeBranch/spec.Spec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2024/2025 (N0688A050001) Information and Knowledge Management DZ P Czech Ostrava 1 Compulsory study plan
2023/2024 (N0688A050001) Information and Knowledge Management DZ P Czech Ostrava 1 Compulsory study plan
2022/2023 (N0688A050001) Information and Knowledge Management DZ P Czech Ostrava 1 Compulsory study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner

Assessment of instruction

2023/2024 Summer