Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
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Publication Analysis of the Lingering Effects of COVID-19 on Distance Education(Springer Science and Business Media Deutschland GmbH, 2023) KOCAÇINAR, BÜŞRA; QARIZADA, NASIBULLAH; DİKKAYA, CİHAN; AZGUN, EMİRHAN; ELİF, YILDIRIM; AKBULUT, FATMA PATLAREducation has been severely impacted by the spread of the COVID-19 virus. In order to prevent the spread of the COVID-19 virus and maintain education in the current climate, governments have compelled the public to adopt online platforms. Consequently, this decision has affected numerous lives in various ways. To investigate the impact of COVID-19 on students’ education, we amassed a dataset consisting of 10,000 tweets. The motivations of the study are; (i) to analyze the positive, negative, and neutral effects of COVID-19 on education; (ii) to analyze the opinions of stakeholders in their tweets about the transition from formal education to e-learning; (iii) to analyze people’s feelings and reactions to these changes; and (iv) to analyze the effects of different training methods on different groups. We constructed emotion recognition models utilizing shallow and deep techniques, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long-short Term Memory (LSTM), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Logical Regression (LR). RF algorithms with a bag-of-words model outperformed with over 80% accuracy in recognizing emotions.Publication Comparative Evaluation of Different Classification Techniques for Masquerade Attack Detection(Inderscience Enterprises Ltd., 2020) Elmasry, Wisam; AKBULUT, AKHAN; Zaim, Abdul HalimMasquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial basis for computer security. Although of considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low degree of false alarm rate is still a big challenge. In this paper, we present an extensive empirical study in the area of user behaviour profiling-based masquerade detection using six of different existed machine learning methods in Azure Machine Learning (AML) studio. In order to surpass previous studies on this subject, we used four free and publicly available datasets with seven data configurations are implemented from them. Moreover, eight well-known masquerade detection evaluation metrics are used to assess methods performance against each data configuration. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper.Publication Cyberbullying Detection Through Deep Learning: A Case Study of Turkish Celebrities on Twitter(IOS Press, 2023) Karadağ, Bulut; AKBULUT, AKHAN; Zaim, Abdul HalimOne of the ways that celebs maintain their fame in the modern era is by posting updates and photos to social media platforms like Twitter, Instagram, and Facebook. Comments left on their posts, however, expose them to cyberbullying. Cyberbullying, as a form of electronic device-based harassment, negatively impacts the lives of individuals. Thirty famous people from the fields of acting, art, music, politics, sports, and writing were chosen for this research. These notable figures include the top five Twitter followers of Turkey in each demographic. Between December 2019 and December 2020, comment responses for each celebrity were collated. Using the Deep Learning model, we were able to detect abuse content with an accuracy of 89%. Additionally, the percentage of celebrities exposed to cyberbullying by group was presented.Publication The Effect of Heuristic Methods Toward Performance of Health Data Analysis(Springer Science and Business Media Deutschland GmbH, 2022) ÖZOĞUR, HATİCE NİZAM; Orman, ZeynepAnalysis and prediction of health data make essential contributions to the detection, control, and prevention of diseases in the early stages without special examinations. In the analysis of health data, the balance of the datasets, the accuracy and completeness of the data, and the selection of features to represent the disease are very important as they affect the performance of machine learning methods. They have also become popular in various health data analysis studies such as classification of diseases, selection of features to represent the disease, imputation of missing value in dataset since heuristic methods give successful result in the optimization of many problems. In this chapter, various studies that combine heuristic methods and machine learning algorithms for health data analysis between 2010 and 2021 have been examined. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Publication Feature Selections for the Classification of Webpages to Detect Phishing Attacks: A Survey(IEEE, 2020) Korkmaz, Mehmet; ŞAHİNGÖZ, ÖZGÜR KORAY; Diri, BanuIn recent years, due to the increased number of Internet-connected devices, almost all the real-world interactions are transferred to the cyberworld. Therefore, most of the commerce (especially in the e-commerce format) are executed over webpages. The anonymous and uncontrollable structure of Internet, enables the malicious use of this cyber environment for a relatively new crime format, named as e-crime, which mainly aims some illegal financial gain by cheating the standard end-users. Phishing attacks are one of the most preferred fraudulent technique which is used for getting some confidential information (like user-id, password, credit card information, etc.) of the end-users. Therefore, security admins of the networks try to decrease the number of victims is their companies. One principal protection mechanism is the use of blacklists to detect the phishing webpages. However, it has a significant deficiency in not protection about new page attacks. Most of the security admins use some learning systems which are trained by a pre-collected a-dataset by extracting some features from the URL and content of the web pages. The performance of the used system directly related with the features used for the classification. In this work, we aimed to analyze the previously used features in the classification of the web pages by making a comparative analysis about the literature. With this study, it is aimed to produce a general survey resource for the researchers, which aim to work on the classification of webpages or the security of the networks.Publication Optimization of Waste Collection in Smart Cities with the use of Evolutionary Algorithms(IEEE, 2020) Aktemur, İlknur; Erensoy, Kübra; Koçyiğit, EmreWith the growth of population, there is an inevitable increase in solid waste especially in urban areas. For the municipalities, especially in smart cities, this becomes a major problem in nature, and it leads to many socio-economic and environmental problems. Thus, lowering our living standards. To eliminate or minimize these problems, using the Internet of Things (IOT) technology is the most advantageous solution for collecting solid wastes within this scope. In this paper, we proposed an optimal waste collection mechanism with the use of some IoT devices in the garbage cans which show the level of waste in them. For testing the proposal, we select a sample environment as a specific region of Istanbul, which is named as Bakirkoy. With the use of sensors, it is aimed to detect which cans are needed to be visited. Then with the use of an evolutionary algorithm, Genetic Algorithm, best path for visiting these cans can be planned in a very short time. By using this approach, it is aimed to effectively use the workforce/resources of the smart cities and making less traffic jam on the roads. Experimental results showed that the proposed system results very good enhancement in the waste collection operations.Publication A Review on Blockchain Applications in Fintech Ecosystem(Institute of Electrical and Electronics Engineers Inc., 2022) Karadağ, Bulut; AKBULUT, AKHAN; Zaim, Abdül HalimThe term fintech started to become popular from the 90s. With the rapid development of technology and the widespread use of the internet, Fintech has become a sector in itself, especially since 2004. Within the framework of Fintech, there have been a number of advances in ATM, credit cards, debit cards, mobile transactions, internet banking and digital banking infrastructure and transactions. The emergence of Bitcoin in 2008 caused us to hear the term blockchain frequently, and the path of blockchain technology intersected with fintech. The decentralization of the blockchain, thanks to its distributed ledger structure, made it possible to make Bitcoin transfers without intermediaries. After Bitcoin, the emergence of crypto assets like Ethereum opened the way for these transactions to be programmable as an infrastructure. Programmable blockchain infrastructures have started to be used not only in financial transactions, but also in sectors such as health, supply chain, education and insurance. There are different academic studies on applications related to these sectors. However, there is no such a review that includes them all together for finance. In this study, blockchain applications in the fintech ecosystem were investigated and included in a single study. In particular, it was explained in which business it was used and in which business there was a market volume. In addition, possible future blockchain applications were also mentioned. © 2022 IEEE.Publication Security Concepts in Smart Cities(IEEE, 2020) Karatürk, Ebru; Koçyiğit, EmreBoth the population growth in the world and the number of people living in the city are increasing day by day while the rural population decreases. However, lack of resources, limited sources or difficulties in city life also arise. In response to these situations, the Smart City concept emerges and thanks to this concept, which integrates information and communication technology, it optimizes the available resources and makes people's life more qualified, efficient and comfortable. Smart cities include the phases of transferring, storing and processing real-time data from sensors and manage variable subsystems. While the transactions are carried out successfully for the purposes, the security and privacy problems must be examined carefully. In this paper, we analyze security issues and challenges in two ways: IoT-based and cloud-based. And as a solution proposal, it is stated that we can use authentication, encrypted communication, and blockchain to ensure data privacy. It is difficult to provide security in smart city architectures due to its decentralized and distributed structure with the existing technology IoT. Blockchain offers a security approach with a decentralized and distributed structure.Publication 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 Symptom Based Health Status Prediction via Decision Tree, KNN, XGBoost, LDA, SVM, and Random Forest(Springer Science and Business Media Deutschland GmbH, 2023) MERİÇ, ELİF; Özer, ÇaǧdaşMachine learning applications in health science become more important and necessary every day. With the help of these systems, the load of the medical staff will be lessened and faults because of a missing point, or tiredness will decrease. It should not be forgotten that the last decision lies with the professionals, and these systems will only help in decision-making. Predicting diseases with the help of machine learning algorithm can lessen the load of the medical staff. This paper proposes a machine learning model that analyzes healthcare data from a variety of diseases and shows the result from the best resulting algorithm in the model. It is aimed to have a system that facilitates the diagnosis of diseases caused by the density of data in the health field by using these algorithms of previously diagnosed symptoms, thus resulting in doctors going a faster way while diagnosing the disease and have a prediction about the diseases of people who do not have the condition to go to the hospital. In this way, it can ease the burden on health systems. The disease outcome corresponding to the 11 symptoms found in the data set used is previously experienced results. During the study, different ML algorithms such as Decision Tree, Random Forest, KNN, XGBoost, SVM, LDA were tried and compatibility/performance comparisons were made on the dataset used. The results are presented in a table. As a result of these comparisons and evaluations, it was seen that Random Forest Algorithm gave the best performance. While data was being processed, input parameters were provided to each model, and disease was taken as output. Within this limited resource, our model has reached an accuracy rate of 98%.Publication Wearable Sensor-Based Evaluation of Psychosocial Stress in Patients With Metabolic Syndrome(Elsevier, 2020) AKBULUT, FATMA PATLAR; İkitimur, Barış; Akan, AydınThe prevalence of metabolic disorders has increased rapidly as such they become a major health issue recently. Despite the definition of genetic associations with obesity and cardiovascular diseases, they constitute only a small part of the incidence of disease. Environmental and physiological effects such as stress, behavioral and metabolic disturbances, infections, and nutritional deficiencies have now revealed as contributing factors to develop metabolic diseases. This study presents a multivariate methodology for the modeling of stress on metabolic syndrome (MES) patients. We have developed a supporting system to cope with MES patients' anxiety and stress by means of several biosignals such as ECG, GSR, body temperature, SpO(2), glucose level, and blood pressure that are measured by a wearable device. We employed a neural network model to classify emotions with HRV analysis in the detection of stressor moments. We have accurately recognized the stressful situations using physiological responses to stimuli by utilizing our proposed affective state detection algorithm. We evaluated our system with a dataset of 312 biosignal records from 30 participants and the results showed that our proposed method achieved an average accuracy of 92% and 89% in distinguishing stress level in MES and other groups respectively. Both being the focus of an MES group and others proved to be highly arousing experiences which were significantly reflected in the physiological signal. Exposure to the stress in MES and cardiovascular heart disease patients increases the chronic symptoms. An early stage of comprehensive intervention may reduce the risk of general cardiovascular events in these particular groups. In this context, the use of e-health applications such as our proposed system facilitates these processes.