| Course Unit Code | 450-4049/01 |
|---|
| Number of ECTS Credits Allocated | 4 ECTS credits |
|---|
| Type of Course Unit * | Optional |
|---|
| Level of Course Unit * | Second Cycle |
|---|
| Year of Study * | |
|---|
| Semester when the Course Unit is delivered | Winter Semester |
|---|
| Mode of Delivery | Face-to-face |
|---|
| Language of Instruction | Czech |
|---|
| Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
|---|
| Name of Lecturer(s) | Personal ID | Name |
|---|
| CER275 | prof. Ing. Martin Černý, Ph.D. |
| KUB631 | Ing. Jan Kubíček, Ph.D. |
| Summary |
|---|
| 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 |
|---|
Lectures:
1. Introduction on artificial intelligence scientific field
2. Principles of artificial intelligence, importace of knowledge in problem solving tasks
3. Methods of computer knowledge reprezentation
4. Mathematic and linguistc modelling
5. Vagueness formalization of knowledge in linguistic models
6. Principles of fuzzy set and fuzy logic theory
7. Fuzzy models of Mandami type
8. Fuzzy models of Takagi-Sugeno type
9. Diagnostoc expert systems
10. Fuzzy controllers
11. Topology and functions of multilayer artificial neural networks
12. Neural networks application in biomedical engineering
13. Genetic algorithms - versatil optimization methods
14. Genetic algorithms application in adaptation procedures
Laboratories:
1. Fuzzy controller using microcomputer and PLC
Computer labs:
1. Computer system MATLAB
2. Fuzzy ToolBox in MATLAB
3. Fuzzy sets and vagueness objects reprezentation in MATLAB
4. Fuzzy conditonal rules formalization in MATLAB
5. Fuzzy modelling of Mandami type in MATLAB
6. Fuzzy modelling of Takagi-Sugeno type in MATLAB
7. Diagnostic fuzzy expert systems
8. Fuzzy controllers in MATLAB
9. Pletysmogram evaluation fuzzy expert module
10. EEG evaluation fuzzy expert module
11. Neural network synthesi in Neural ToolBoxu of MATLABu
12. Neural network application in biomedical engineering
13. Genetic algorithm synthesis in MATLAB
|
| 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 Title | Task Type | Maximum Number of Points
(Act. for Subtasks) | Minimum Number of Points for Task Passing |
|---|
| Graded credit | Graded credit | 100 (100) | 51 |
| Laboratorní práce | Laboratory work | 40 | 21 |
| Písemná práce | Written test | 60 | 30 |