Browsing by Author "Al-Bayati, Taha A."

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Anomaly Detection in Time Series
(Kadir Has Üniversitesi, 2019) Al-Bayati, Taha A.; Öğrenci, Arif Selçuk
The concept of "Internet of Things" is based on connecting any physical object through the internet. This will facilitate our daily lives by dedicating technology in our will. In such a world, the number other interconnected devices is enormous, hence, the need for high performance processing in real-time is huge. This research shines light on the importance of the event processing and machine learning in the time series. A multiple of machine learning algorithms such as support vector machine, decision tree, autoencoder, and K-mean clustering are used for training a time series. A comparison of different methods is analyzed to obtain a robust conclusion about the data. The time series data is used to distinguish the state of emotions for a group of people (15 in total) who participated in an experiment. The state of the emotion may be in one of the four states: stressed, amused, natural, and sad. In this work, we compared the performance of algorithms in terms of their accuracy of predicting the emotions.