Publication

Computing expert's intelligence: a case in bio-medicine and a case in musicology

Neocleous, A. 2016 [Groningen]: University of Groningen. 118 p.

Research output: ScientificDoctoral Thesis

Documents

  • Title and contents Abstract Samenvatting

    Final publisher's version, 1013 KB, PDF-document

  • Chapter 1

    Final publisher's version, 136 KB, PDF-document

  • Chapter 2

    Final publisher's version, 277 KB, PDF-document

  • Chapter 3

    Final publisher's version, 3 MB, PDF-document

  • Chapter 4

    Final publisher's version, 351 KB, PDF-document

    Embargo ends: 13/12/2017

  • Chapter 5

    Final publisher's version, 746 KB, PDF-document

    Embargo ends: 13/12/2017

  • Chapter 6

    Final publisher's version, 283 KB, PDF-document

  • Chapter 7

    Final publisher's version, 102 KB, PDF-document

  • Chapter 8

    Final publisher's version, 94 KB, PDF-document

  • Bibliography

    Final publisher's version, 177 KB, PDF-document

  • Curriculum Vitae

    Final publisher's version, 105 KB, PDF-document

  • Complete thesis

    Final publisher's version, 5 MB, PDF-document

    Embargo ends: 13/12/2017

  • Propositions

    Final publisher's version, 25 KB, PDF-document

The research work is presented here in two parts. The one comprises research on the applicability of machine learning techniques for the early identification of chromosomal abnormalities. It is shown in part I that the ANNs achieve better results than other existing methods in terms of diagnostic rate (DR) of chromosomal abnormalities (100% DR of T21) at a lower false positive rate.

The work presented in part II of the thesis has been done under a three-years research project for the analysis of the folk music of Cyprus and the Eastern Mediterranean Countries and it was funded by the Research Promotion Foundation of the Republic of Cyprus. The COSFIRE filters that have been found effective for 2D and 3D signals, had been adapted for 1D music signals and their effectiveness in different applications has been studied and the results are reported.

The ultimate objective of this thesis is the development of machine learning techniques that can be validated in real data in medicine and musicology and that can have practical value.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
Supervisors/Advisors
  • Petkov, Nicolai, Supervisor
  • Schizas, Christos N, Supervisor, External person
  • Azzopardi, George, Co-supervisor
  • Vento, M., Assessment committee, External person
  • Pattichis, Constantinos S, Assessment committee, External person
  • Biehl, Michael, Assessment committee
  • Telea, Alexandru, Assessment committee
Award date13-Dec-2016
Place of Publication[Groningen]
Publisher
Print ISBNs978-90-367-9393-3
Electronic ISBNs978-90-367-9392-6
StatePublished - 2016

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