Publications by authors named "Utku Köse"

2 Publications

  • Page 1 of 1

Use of social networking in the Middle East: student perspectives in higher education.

Heliyon 2021 Apr 8;7(4):e06676. Epub 2021 Apr 8.

School of Management and Marketing, Curtin University, Perth, WA, Australia.

This study aims to determine the benefits, risks, awareness, cultural factors, and sustainability, allied to social networking (SN) use in the higher education (HE) sector in Middle Eastern countries, namely Jordan, Saudi Arabia, and Turkey. Using an online survey, 1180 complete responses were collected and analyzed using the statistical confirmatory factor analysis method. The use of SN in the Middle Eastern HE sector has the capacity to promote and motivate students to acquire professional and personal skills for their studies and future workplace; however, the use of SN by tertiary students is also associated with several risks: isolation, depression, privacy, and security. Furthermore, culture is influenced by using SN use, since some countries shifted from one dimension to another based on Hofstede's cultural framework. The study new findings are based on a sample at a specific point in time within a culture. The study findings encourage academics to include SN in unit activities and assessments to reap the benefits of SN, while taking steps to mitigate any risks that SN poses to students. Although other studies in the Middle East examined the use of Learning Management System and Facebook in, HE as a means of engaging students in discussions and communications, however, this study contributes a better understanding of the benefits and risks, awareness, culture, and sustainability, associated with the use of SN in the HE sector in the Middle East. Finally, the paper concludes with an acknowledgment of the study limitations and suggestions for future research.
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http://dx.doi.org/10.1016/j.heliyon.2021.e06676DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055565PMC
April 2021

Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network.

Comput Commun 2020 Oct 19;162:31-50. Epub 2020 Aug 19.

School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India.

Objective of this study is to introduce a secure IoHT system, which acts as a clinical decision support system with the diagnosis of cardiovascular diseases. In this sense, it was emphasized that the accuracy rate of diagnosis (classification) can be improved via deep learning algorithms, by needing no hybrid-complex models, and a secure data processing can be achieved with a multi-authentication and Tangle based approach. In detail, heart sounds were classified with Autoencoder Neural Networks (AEN) and the IoHT system was built for supporting doctors in real-time. For developing the diagnosis infrastructure by the AEN, PASCAL B-Training and Physiobank-PhysioNet A-Training heart sound datasets were used accordingly. For the PASCAL dataset, the AEN provided a diagnosis-classification performance with the accuracy of 100%, sensitivity of 100%, and the specificity of 100% whereas the rates were respectively 99.8%, 99.65%, and 99.13% for the PhysioNet dataset. It was seen that the findings by the developed AEN based solution were better than the alternative solutions from the literature. Additionally, usability of the whole IoHT system was found positive by the doctors, and according to the 479 real-case applications, the system was able to achieve accuracy rates of 96.03% for normal heart sounds, 91.91% for extrasystole, and 90.11% for murmur. In terms of security approach, the system was also robust against several attacking methods including synthetic data impute as well as trying to penetrating to the system via central system or mobile devices.
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http://dx.doi.org/10.1016/j.comcom.2020.08.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7434639PMC
October 2020