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Dr. Oya Deniz Beyan - Theses

Developing Data Quality Metrics for Power System Modeling - Running
Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan, 660b75904e3b7e318151bbdc353d734d
Linked data is gaining new attention in the last years because of its natural connection to knowledge-based applications. The quality of decisions depends heavily on the quality of the underlying data, for reasoning such quality reports are mandatory for each decision. The W3Cs Best Practices Working Groups "Data on the Web Best Practices: Data Quality Vocabulary" defines a vocabulary to archive linking results of data quality assessments to linked data. Also, a basic set of quality dimensions and metrics based on the work of Zaveri et al. ( are presented. This thesis aims to fill the gaps between the DQV, the definitions by Zaveri et al. and the realization of linked data quality assessments, to fulfil all requirements to link data quality assessments.
Unsupervised Anomaly Detection in Medical Time Series Data - Finished
Completed by Brockhoff, Tobias in 2019; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
Deep Learning-based Knee Osteoarthritis Diagnosis from Radiographs and Magnetic Resonance Images - Finished
Completed by Jiao, Jiao in 2019; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan, Dr. Michael Cochez, Md. Rezaul Karim, M.Eng.
In this thesis, the student investigates the use of deep learning techniques (especially computer vision) to perform diagnosis of osteoarthritis. The input to the system are both radiographs (X-RAY) and magnetic resonance images (MRI).
Classification of Cancer with methylation aware motifs - Finished
Completed by Gehrmann, Julia in 2019; Supervised by Prof. Dr. Stefan Decker, Ivan Gesteira Costa Filho; Advisor(s): Dr. Oya Deniz Beyan, Md. Rezaul Karim, M.Eng.
This dissertation addresses the problem of classification of cancer patients from DNA methylation. Mrs. Gehrman explores here the use of scores of transcription factors binding sites around DNA methylation as surrogate markers for DNA methylation. An innovative aspect is the fact binding site motifs take into consideration of the DNA methylation status of a given locus. Next, this work compared the performance of machine learning classifiers either using classical DNA methylation levels vs. TF binding affinity scores. For this, several classical machine learning methods (SVM, inductive trees, random forests) were used. Performance accuracy was similar with both data representations, however the computational time of training classifiers with TF binding site affinity scores were at least 10 times fasters, due to the lower dimensionality of the new space. This works supports promising features of DNA methylation aware TF binding scores.
Developing a machine-actionable process model for ethical approval workflows - Finished
Completed by Schwaiger, Verena in 2019; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan
Patterns zur Integration regel- und prozessbasierter Modellkomponenten bei der Operationalisierung klinischer Leitlinien - Finished
Completed by Lammers, Florian in 2019; Supervised by Prof. Dr. Stefan Decker, Dr. rer. nat. Cord Spreckelsen; Advisor(s): Dr. Oya Deniz Beyan
An Explainable and Fully Automated Localization and Semantic Segmentation Technique for Biomedical Imaging - Finished
Completed by Shafin, Nazmi in 2019; Supervised by Prof. Dr. Stefan Decker; Advisor(s): Dr. Oya Deniz Beyan, Md. Rezaul Karim, M.Eng.
Timely and correct intervention with surgical procedures by physicians is the most critical factor in reducing the morbidity rate of patients with chronic brain ailments, in particular patients with brain Tumor. Deep learning techniques in computer vision is making rapid progress in diagnosing and thereby aiding the physician in taking timely appropriate steps, and thus it has the potential to save many lives. However, often the deep learning models are like a black box that merely generates the outcome without giving any reason or explanation. The field of Artificial Intelligence needs models that have a capability to include explainability of the decisions that it is making. The US Food and Drug Administration requires any clinical decision support software to explain the rationale or support for its decisions to enable the users to independently review the basis of their recommendations. To meet this requirement and gain trust from medical practitioners, medical deep-learning systems should provide explanations for their outputs. This thesis work is an endeavour in that direction of explainable artificial intelligence whereby the results are generated and supplemented with an explanation of the decision that can be interpreted with visualizations generated by localization and semantic segmentation techniques which will ultimately be of significant help in aiding physicians for treating critical ailments such as brain tumor.