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Title:Razvrščanje vzorcev z uporabo inteligentnih metod
Authors:ID Berus, Lucijano (Author)
ID Klančnik, Simon (Mentor) More about this mentor... New window
Files:.pdf MAG_Berus_Lucijano_2017.pdf (1,48 MB)
MD5: BF2C87B9E51194FA08E4FE623E874FB6
PID: 20.500.12556/dkum/695883a3-2ad3-4234-a454-8ca920a550ff
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FS - Faculty of Mechanical Engineering
Abstract:Magistrsko delo obravnava področje umetne inteligence, strojnega učenja, razvrščanja kompleksnih vzorcev in metode določitve značilk. Predstavljeno je delovanje nekaterih najpogosteje uporabljenih razvrščevalnih algoritmov. Izdelan je bil algoritem za zaznavo Parkinsonove bolezni na podlagi zajetega zvočnega signala. Meritve zvoka so bile narejene na štiridesetih posameznikih. Od tega je bila polovica zdravih in polovica z Parkinsonovo boleznijo. Namen naloge je razviti robusten sistem za zaznavo prisotnosti Parkinsonove bolezni. Za izboljšanje natančnosti razvrščanja, so bile uporabljene različne tehnike določitve značilk (Pearsonov korelacijski koeficient, Khendallov korelacijski koeficient in Samoorganizacijske gruče) in topologije nevronskih mrež. S pomočjo usmerjene nevronske mreže, je bila dosežena 86,47 % natančnost razvrščanja. Omenjena natančnost je bila dosežena z uporabo redukcije značilk na podlagi Pearsonovega korelacijskega koeficienta.
Keywords:umetna inteligenca, klasifikacija, strojno učenje, Parkinsonova bolezen, umetna nevronska mreža
Place of publishing:Maribor
Publisher:[L. Berus]
Year of publishing:2017
PID:20.500.12556/DKUM-67534 New window
UDC:004.923.021(043.2)
COBISS.SI-ID:21149974 New window
NUK URN:URN:SI:UM:DK:X6MDCTDS
Publication date in DKUM:13.09.2017
Views:2657
Downloads:322
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FS
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:22.08.2017

Secondary language

Language:English
Title:Classification of patterns with use of intelligent methods
Abstract:This Master’s thesis discusses artificial intelligence, machine learning, classification of complex patterns and feature selection procedure. Some of the most used classification algorithms are introduced. Algorithm for the detection of Parkinson’s disease based on sound measures has been made. Sound measurements of forty individuals were used as a dataset. Half of the individuals are healthy and half have the Parkinson’s disease. Purpose of this thesis is to present robust system for Parkinson’s disease detection. Few different feature selection techniques (Pearson’s correlation coefficient, Khendall’s correlation coefficient and Self-organizing maps) and neural network topologies have been used for improving classification accuracy. With the use of feed-forward neural network 86,47 % accuracy was achieved based on Pearson’s correlation coefficient.
Keywords:artificial intelligence, classification, machine learning, Parkinson’s disease, artificial neural network


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