| | SLO | ENG | Cookies and privacy

Bigger font | Smaller font

Search the digital library catalog Help

Query: search in
search in
search in
search in
* old and bologna study programme

Options:
  Reset


1 - 2 / 2
First pagePrevious page1Next pageLast page
1.
Izdelava programske rešitve za izvajanje bibliometričnih raziskav in gradnjo tezavrov iz velikih količin bibliometričnih podatkov
Boris Vezenšek, 2019, undergraduate thesis

Abstract: V diplomskem delu je obravnavana bibliometrija in bibliometrične analize, izvedene s pomočjo izdelane programske rešitve. Opisane so tehnologije, uporabljene za rešitev našega problema. Tukaj gre predvsem za delovanje Hadoopovega porazdeljenega datotečnega sistema HDFS in modela MapReduce ter sistema Apache Spark. Opisani sta tudi rešitvi Analysis Services Tabular in Power BI. Na koncu so na izbranih primerih predstavljeni rezultati različnih bibliometričnih analiz v orodju Power BI, ki se napaja iz podatkovnega modela, implementiranega v tem diplomskem delu. Rezultati so prikazani v različni obliki – z vizualizacijami, primernimi za takšno vrsto podatkov.
Keywords: bibliometrija, Hadoop, HDFS, Microsoft Academic Graph, podatki CORE, porazdeljena obdelava, Spark, velepodatki
Published: 22.11.2019; Views: 331; Downloads: 33
.pdf Full text (1,93 MB)

2.
Classification of perimetric data for supporting glaucoma diagnosis
Janja Belinc, 2018, master's thesis

Abstract: The aim of the study: Glaucoma is a chronic, progressive and asymptomatic retinal disease which results in an irreversible visual field loss. The main objective of this Master’s thesis work was to study the applicability of classification techniques for supporting glaucoma diagnosis. Research Methodology: In this study perimetric data was obtained by SPARK strategy implemented in Oculus perimeters and provided by medical experts from the Hospital Universitario de Canarias (HUC). This data was used for constructing the feature vectors for the classification problem. Feature vectors of 66 values and feature vectors of 6 values were tested in the experiments. The proposed classification study attempted to: a) demonstrate that the studied classifiers were able to distinguish between “healthy” and “glaucomatous” eyes using only perimetric data, and b) analyse which feature vector design was the most suitable to accomplish this task. Results: The classification results showed that classifiers performed better on 6 than on 66 perimetry values, which demonstrated the suitability of the 6 points selected by the SPARK strategy and supported its use in medical field. Conclusion: In this study two remarkable findings for pattern recognition in perimetric data were obtained. Firstly, that reducing the dataset improved the efficiency of the studied classifier, and secondly, that simple pattern recognition models types were more efficient than complex ones.
Keywords: Eye disease, visual field, SPARK perimetry, pattern recognition, machine learning, supervised learning, ROC analysis
Published: 27.08.2018; Views: 538; Downloads: 56
.pdf Full text (4,25 MB)

Search done in 0.05 sec.
Back to top
Logos of partners University of Maribor University of Ljubljana University of Primorska University of Nova Gorica