Cybersecurity Department Applications of Transfer from M.S. Non-Thesis to M.S. Thesis

Cybersecurity Department Applications of Transfer from MSc Non-Thesis to MScThesis are evaluated  and the names of the candidates who are eligible for the transfer to the Thesis Master program are given below:

Surname Name
AYDIN KIVANÇ
AYDUMAN NUSRET ONUR
BOLEL ONUR
DURDAĞI EMRE
LEBLEBİCİ MEHMET
ÖZKAN ABDULLAH

Announcement Category

Tuğçe Gölgeli, A Case Study on The Effect of Route Characteristics on Decision Making in the Sport of Orienteering

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

English

Davut Çavdar, Design of a Context Aware Security Model for Preventing Relay Attacks Using Nfc Enabled Mobile Devices

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


English

Gökçe Abay, Biological Data Integration and Relation Prediction by Matrix Factorization

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

English

Fatma Cankara, Prediction of the Effects of Single Amino Acid Variations on Protein Functionality with Structural and Annotation Centric Modeling

Studies showed that single nucleotide variations that alter the protein sequence, structure and function are associated with many diseases in humans. However, the current rate of manually annotating reported nsSNPs cannot catch up with the rate of producing new sequencing data. To aid this process, automated computational approaches are being developed and applied on the unknown data. In this study, we propose a new methodology to collect and organize the information related to the effects of nsSNPs at the amino acid sequence level from various biological databases and to utilize this information in a supervised machine-learning based system to predict the function disrupting capacities of mutations with unknown consequences.

Date: 30.01.2020 / 14:00 Place: A-212

English

Eray Üstün, Acoustic Source Localization Using Quaternion Fourier Transforms with Dırect-Path Dominance Test

M.S. Candidate: Eray Üstün
Program: Multimedia Informatics
Date: 23.01.2020 / 11:00
Place: A-212

Abstract: Acoustic source localization is an important and broadly researched topic since it can be used in robotics, scene analysis and surveillance fields. Acoustic scene analysis can be defined by acoustic pressure and particle velocity. The former one is a scalar value; whereas, the latter one is a vector quantity. Numerous approaches for DOA have been developed using those properties of acoustic waves. There is a 4-dimensional number system, called quaternions, which is an extension of classical complex number system. Quaternions are composed of one scalar component and 3-dimensional vector component. The quaternion Fourier transforms (QFTs) are useful in the sense that they can process components of multi-component signals concurrently. In this thesis, a novel signal model using quaternions is constructed and a novel approach for acoustic source localization are presented. The acoustical sound field is represented by quaternions and it is processed by using QFTs. Direct-path dominance (DPD) test is applied to the result of QFTs and localization task is performed by utilizing geometrical orientation of DOA estimation vectors.

Announcement Category

Rumeysa Fayetörbay, Network-Based Discovery of Molecular Targeted Agent Treatments in Hepatocellular Carcinoma

Sorafenib is one of FDA approved targeted agents in HCC treatment. PI3K/AKT pathway is altered in 50% of hepatoma, hence understanding how Sorafenib and PI3K/AKT pathway inhibitors act at signalling level is crucial for targeted therapies and to reveal their off-target effects. In this work, we use gene expression profiles of HCC cells treated with seven different drugs/inhibitors and combination. Our aim is to reveal the important targets and modulators in a drug treatment by inferring the dysregulation of Interactome. In other words, we search for the mechanism of action of drugs in a network context beyond gene list.

Date: 13.01.2020 / 14:00 Place: A-108

English

Mustafa Teke, Multi-Year Time Series Crop Mapping

Vector Dynamic Time Warping (VDTW), a novel multi-year classification approach based on warping of angular distances between phenological vectors was developed. The results prove that the proposed VDTW method is robust to temporal and spectral variations compensating for different farming practices, climate and atmospheric effects, and measurement errors between years. We also describe a method for determining the most discriminative time window that allows high classification accuracies with limited data. We carried out tests of our approach with Landsat 8 time-series imagery from years 2013 to 2015 for classification of corn and cotton in the Harran Plain, and corn, cotton, and soybean in the Bismil Plain of Southeastern Turkey. In addition, VDTW was tested with corn and soybean in Kansas, the US for 2017 and 2018 with the Harmonized Landsat Sentinel data.

Date: 15.01.2020 / 14:00 Place: Conference Hall-1

English

Pages

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