Person: ŞENGEL, ÖZNUR
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Dr. Öğr. Üyesi
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ŞENGEL
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ÖZNUR
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Publication Metadata only Comparison of Cryptography Algorithms for Mobile Payment Systems(2018-10) Aydın, Muhammed; Sertbaş, Ahmet; ŞENGEL, ÖZNUR; 144004; 187909; 2285Mobile payment services are the newest and most popular technology that is developing according to our habits and needs. Consumer all over the world are using mobile phone for payment as well as communication. The main purpose of using mobile payment application is doing all transaction easily and quickly. Not only data security in electronic transactions, but also the speed of the system operations is becoming very important. There is a threshold value to finish all transaction in mobile payment systems. If the security algorithm is more complex and exceed threshold, it is not suitable to using in mobile payment systems. In this paper we compare cryptography algorithms and proposed two algorithms on Advanced Encryption Standards. The experiment results show that proposed algorithms is suitable cryptography algorithm for mobile system according to time and storage consumption factors.Publication Metadata only Nakit Paradan Akıllı ve Güvenli Mobil Cihaza(Herkese Bilim Teknoloji, 2017-03-31) ŞENGEL, ÖZNUR; 144004Publication Metadata only Dalgaboyu Bölmeli Çoğullama Yapıları ve Enerji Verimliliği(2015-09-02) Aydın, Muhammed Ali; ŞENGEL, ÖZNUR; 144004; 176402Publication Metadata only Assistant Assignment to Final Exams(2014-07-13) ŞENGEL, ÖZNUR; 144004Publication Metadata only A Survey on White Box Cryptography Model for Mobile Payment Systems(2017-12-28) Aydın, Muhammed Ali; Sertbaş, Ahmet; ŞENGEL, ÖZNUR; 144004; 176402; 2283The technology is showing rapid development and these developments are changing our lives, our habits, and our needs. As electronic devices, which are indispensable for our daily lives, continue to be intelligent, we are able to do our every operation through these devices. Mobile payment technologies and services are one of the innovations. Consumers all over the world and in our country have started to use their mobile devices as a means of payment as well as communication services. With rapidly developing technology, one of the most important needs of many systems such as electronic, mobile and bank is to move and store the data safely. In addition to data security in electronic transactions, the speed of the system operations is becoming very important. Developing a mobile payment system whether by installing an application or using existing hardware, the most important issue in both cases is the creation of a reliable system based on the protection of the current situation of the consumer and the confidentiality of their information.Publication Metadata only Determining the Cryptography Algorithm and Model for Mobile Payment Systems(2018-10) Aydın, Muhammed Ali; Şertbaş, Ahmet; ŞENGEL, ÖZNUR; 144004; 176402; 2283New payment applications are developed with demands and needs of people. People wants to do their transaction and shopping faster and easily than the other devices. Mobile phones are used for not only communications but also for payment. Mobile payment systems are becoming the newest and most popular technology nowadays.Publication Metadata only Path-based Connectivity for Clustering Genome Sequences(IEEE, 345 E 47Th St, New York, Ny 10017 USA, 2016) Kurşun, Olcay; ŞENGEL, ÖZNURClustering is an unsupervised data mining tool and in bioinformatics, clustering genome sequences is used to group related biological sequences when there is no additional supervision. Sequence clusters are often related with gene/protein families, which can shed some light onto determining tertiary structures. To extract such hidden and valuable structures in a data set of genome sequences can benefit from better clustering methods such as the recently popular Spectral Clustering. In this study, we apply spectral clustering and its improved variations to sequence clustering task in our efforts to develop a novel approach for improving it.Publication Metadata only Comparison of lung cancer detection algorithms(International Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT), 2019) GÜNAY, MELİKE; GÜNAYDIN, ÖZGE; ŞENGEL, ÖZNURLung cancer is a kind of difficult to diagnose and dangerous cancer. It commonly causes death both men and women so fast accurate analysis of nodules is more important for treatment. Various methods have been used for detecting cancer in early stages. In this paper, machine learning methods compared while detect lung cancer nodule. We applied Principal Component Analysis, K-Nearest Neighbors, Support Vector Machines, Naive Bayes, Decision Trees and Artificial Neural Networks machine learning methods to detect anomaly. We compared all methods both after preprocessing and without preprocessing. The experimental results show that Artificial Neural Networks gives the best result with 82,43% accuracy after image processing and Decision Tree gives the best result with 93,24% accuracy without image processing.Publication Metadata only Sentiment Analysis of Tweets on Online Education During COVID-19(Springer Science and Business Media Deutschland GmbH, 2023) YAZGAN, HARUN; ÖZBEK, ONUR; GÜNAY, AHMET CAN; AKBULUT, FATMA PATLAR; ELİF, YILDIRIM; KOCAÇINAR, BÜŞRA; ŞENGEL, ÖZNURThe global coronavirus disease (COVID-19) pandemic has devastated public health, education, and the economy worldwide. As of December 2022, more than 524 million individuals have been diagnosed with the new coronavirus, and nearly 6 million people have perished as a result of this deadly sickness, according to the World Health Organization. Universities, colleges, and schools are closed to prevent the coronavirus from spreading. Therefore, distance learning became a required method of advancing the educational system in contemporary society. Adjusting to the new educational system was challenging for both students and instructors, which resulted in a variety of complications. People began to spend more time at home; thus, social media usage rose globally throughout the epidemic. On social media channels such as Twitter, people discussed online schooling. Some individuals viewed online schooling as superior, while others viewed it as a failure. This study analyzes the attitudes of individuals toward distance education during the pandemic. Sentiment analysis was performed using natural language processing (NLP) and deep learning methods. Recurrent neural network (RNN) and one-dimensional convolutional neural network (1DCNN)-based network models were used during the experiments to classify neutral, positive, and negative contents.Publication Metadata only Ağ Kodlama ile Çoklu Durum Video İletimi(2018-05-02) Ekmekci Flierl, Sıla; ŞENGEL, ÖZNUR; 131373; 144004