Browsing by Author "Özdemir, Ö."

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Open Access
Affect-based stock investment decision : the role of affective self-affinity
(Elsevier Inc., 2017) Usul, N.; Özdemir, Ö.; Kiessling, T.
This paper studies the role of affective self-affinity for a company in the stock investment decision by investigating the factors triggering it. Based on the social identity theory and the affect literature we hypothesize that three types of identifications, namely group related, company-people related and idea/ideal related, trigger affective self-affinity for a company which results in extra affect-based motivation to invest in the company's stock. The two ideas included in the idea/ideal related affective self-affinity are socially responsible investing and nationality related ideas. Based on the survey data of 133 active individual investors, we find that the more the investors perceive the company supports/represents a specific group or idea or employ a specific person, with which the investors identify themselves, the higher is the investors’ affective self-affinity for the company. This results in higher extra affective motivation to invest in the company's stock over and beyond financial indicators. Thus, investors’ identification with groups, people, or ideas such as socially responsible investing and nationality results in higher affect-based investment motivation through affective self-affinity aroused in the investors. Moreover, positive attitude towards the company is another factor that explains the affect-based extra investment motivation. © 2017 Elsevier Inc.
Open Access
Detection of jammers in range-doppler images generated in DTED based radar simulator using convolutional neural networks
(IEEE - Institute of Electrical and Electronics Engineers, 2023-08-28) Şahinbay, H. E.; Akyol, Ali Alp; Özdemir, Ö.
Airborne radars have a variety of air-to-air and air-to-ground missions. In both air-to-air and air-to-ground target detection missions, ground clutter reflections are received from the main beam and side lobes of the radar. The effects of this clutter can be clearly seen in the radar range-Doppler maps. In addition, there may be other sources in the environment that distort the radar's range-Doppler maps. These sources can be categorized as jammer and interference signals. They distord the range-Doppler maps, making target detection more difficult, interfering with target detection and, in some cases, leading to false target detection. The detection of jammer and interference signals, which are the source of this situation, is of critical importance for the operators controlling the platform. It is often not possible for operators to quickly detect and classify these jamming signals. Deep learning methods, which have recently been used in every field, can achieve much faster and robust target detection and classification results compared to humans. In this study, the success of a Convolutional Neural Network based technique, which is one of the deep learning methods, in detecting and classifying jammer and interference signals is investigated.