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Yayın Detection of Network Anomalies with Machine Learning Methods(Institute of Electrical and Electronics Engineers Inc., 2022) Kara, I.R.; Varol, A.The present study, aimed to detect cyber-attacks, and unexpected access requests on devices in the telecommunication networks, enabling the necessary measures to be taken early. With K-Nearest Neighbors (KNN) and Naive Bayes machine learning methods, predicted whether the raw data packets contain cyber-attack according to different properties of these packets using the UNSW-NB15 dataset. KNN algorithms with different K values and the Naive Bayes method were compared according to accuracy rates and the results were given in the table. As a result, changes in accuracy rates were observed according to different k neighbor values in the KNN algorithm. Higher accuracy rates than Naive Bayes were achieved in the models created with the KNN algorithm. © 2022 IEEE.Yayın Handling Rollbacks with Separated Response Control Service for Microservice Architecture(Institute of Electrical and Electronics Engineers Inc., 2022) Gordesli, M.; Nasab, A.; Varol, A.Working with large scale microservice based applications may be hard to maintain and control. The services in those applications must be isolated from each other and they must work in harmony. There must be an intelligent transaction management system to implement this kind of collaborative work of microservices. There are two main methodologies for transaction management, namely Two Phase Commit (2PC) and Saga pattern. There are two phases in 2PC that prepare phase and commit phase. In Saga pattern, there are two ways of implementing Saga pattern; choreography based, and orchestration based. When there is a problem in the whole transaction, controlling the rollback scenario may be hard because each microservice has its own rollout and rollback solutions in both ways of Saga pattern. If we separate the rollback system from the microservices, we might have simpler microservices and microservices that can run in parallel. So whole system works safer and faster. In this work we focus on parallel execution of microservices with separate rollback system that controls the whole application in error condition. © 2022 IEEE.Yayın Impact of Machine Learning in Digital Marketing Applications(Institute of Electrical and Electronics Engineers Inc., 2022) Gursoy, G.; Varol, A.; Varol, S.The devastating Covid 19 pandemic has shifted priorities in the business world to accommodate the new normal that the pandemic has caused. Digital marketing has become a necessity for corporations to keep up with the evolving needs of consumers. With the help of the emerging technologies, corporations have begun embracing tailored digital marketing applications that aim to attract and retain more consumers. The techniques or applications used by businesses to improve their marketing performance are consolidated and supported by artificial intelligence applications. In this study, we examine the significance of the marketing field and machine learning, as well as marketing techniques, artificial intelligence, and machine learning algorithms. As a result of the examinations, it has been shown how machine learning techniques and algorithms facilitate marketing processes, increase the profitability of businesses and it is very difficult to realize digital marketing without machine learning algorithms. © 2022 IEEE.Yayın Importance of Machine Learning and Deep Learning Algorithms in Earthquake Prediction: A Review(Institute of Electrical and Electronics Engineers Inc., 2023) Gursoy, G.; Varol, A.; Nasab, A.Earthquakes are the leading natural disasters that have caused loss of life and property since the formation of the world. Machine learning and deep learning are frequently used in studies for earthquake prediction. This article consists of a compilation of studies using machine learning and deep learning algorithms. In the article, studies on topics such as earthquake magnitude estimation, signal discrimination, electron density estimations in the ionosphere, examination of radon gas anomalies using machine learning and deep learning algorithms are included. The studies in this paper show that Deep Learning algorithms are used more frequently in earthquake forecasting. It is expected that Deep Learning will provide more successful results in future studies due to its ability to work with larger data sets compared to Machine Learning and its ability to improve itself from errors. © 2023 IEEE.Yayın Performance Analysis of Deep Approaches on Airbnb Sentiment Reviews(Institute of Electrical and Electronics Engineers Inc., 2022) Raza, M.R.; Hussain, W.; Varol, A.Consumer reviews in the Airbnb marketplace are one of the key attributes to measure the quality of services and the main determinant of consumer rentals decisions. Such feedback can impact both a new and repeated consumer's choice decision. The way to manage poor reviews can help to save or damage the host's reputation. Sentiment analysis enables an Airbnb host to get an insight into the business, pinpoint degradation of the specific component of compound services and assist in managing it proactively. Multiple Deep Learning algorithms have been used for Natural Language Processing (NLP). For optimal sentiment management in the Airbnb marketplace, it is crucial to identify the right algorithm. The paper uses multiple Deep Learning algorithms to identify different aspects of guest reviews and analyze their accuracies. The paper uses four accuracy measurement benchmarks - Precision, Recall, F1-score and Support to analyze results. The analysis shows that the GRU method achieves the best results with the highest classification metrics values as compared to RNN and LSTM. © 2022 IEEE.
