Developing a tool having the capability of predicting future AAA growth rate is important in terms of surgical planning and patient management. In this study, 106 CT scan images from 25 Korean AAA patients were retrospectively obtained. 21 geometrical measurements derived from scans, their growth rates, and their pairwise correlations were analyzed, and the predictability of the growth for high-risk aneurysms was attempted to enhance. Furthermore, the prediction model was built specifically on patient characteristics using the various geometrical measurements enhanced the prediction capability of a measurement at any time-point, along with an evaluation of the associated uncertainty.
Date: 02.03.2020 / 12:40 Place: Conference Hall-1
When choosing a route in orienteering, it is important to combine physical endurance with mental processes and the ability to adapt to the environment and optimize them correctly. In this study, the components affecting route selection were investigated. For this purpose, the data obtained from athletes through GPS containing watches were examined with quantitative and qualitative research methods. Then, a model based on spatial data was created to find the shortest paths and to compare the compatibility with the behaviors of athletes, and the relation of route selection decisions with some specified cognitive paradigms was questioned.
Date: 30.01.2020 / 10:00 Place: A-108
Perceptual sound field reconstruction (PSR) is a spatial audio recording and repro-duction method based on the application of stereophonic panning laws in microphone array design.
Date: 08.01.2020 Place: SPARG LAB. (Modsimmer Building)
In this thesis, we offer a context-aware security model extending the role-based access control model in order to prevent relay attacks in NFC enabled mobile devices with both theoretical and practical approach. Within this study, we identify possible vulnerabilities and requirements then design the model. Parallel to conceptual design, we also developed a complete test-bed to deploy the model on it. Finally, we verified the model theoretically and practically.
Date: 27.01.2020 / 16:00 Place: Conference Hall-1
In this study, we propose to integrate large-scale gene/protein annotation data by using non-negative matrix factorization (NMF). Using NMF, the ultimate aim here is to predict the unknown binary relationships between these biological entities; and to represent these entities (i.e., proteins, functions and disease entries) as informative and non-redundant quantitative feature vectors (using the low-rank feature matrices generated by the factorization process), which can be used in diverse data mining and machine learning tasks in the future, such as the automated annotations of proteins or the construction of biological knowledge graphs.
Date: 30.01.2020 / 15:30 Place: A-212