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Browsing by Author "Onal, Aras Can"

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    Conference Object
    Citation - WoS: 4
    Citation - Scopus: 11
    MIS-IoT: Modular Intelligent Server Based Internet of Things Framework with Big Data and Machine Learning
    (Ieee, 2018) Onal, Aras Can; Doğdu, Erdoğan; Sezer, Omer Berat; Ozbayoglu, Murat; Dogdu, Erdogan; Bilgisayar Mühendisliği
    Internet of Things world is getting bigger everyday with new developments in all fronts. The new IoT world requires better handling of big data and better usage with more intelligence integrated in all phases. Here we present MIS-IoT (Modular Intelligent Server Based Internet of Things Framework with Big Data and Machine Learning) framework, which is "modular" and therefore open for new extensions, "intelligent" by providing machine learning and deep learning methods on "big data" coming from IoT objects, "server-based" in a service-oriented way by offering services via standart Web protocols. We present an overview of the design and implementation details of MIS-IoT along with a case study evaluation of the system, showing the intelligence capabilities in anomaly detection over real-time weather data.
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    Citation - WoS: 33
    Citation - Scopus: 56
    Weather data analysis and sensor fault detection using an extended ıot framework with semantics, big data, and machine learning
    (Ieee, 2017) Onal, Aras Can; Doğdu, Erdoğan; Sezer, Omer Berat; Ozbayoglu, Murat; Dogdu, Erdogan; Bilgisayar Mühendisliği
    In recent years, big data and Internet of Things (IoT) implementations started getting more attention. Researchers focused on developing big data analytics solutions using machine learning models. Machine learning is a rising trend in this field due to its ability to extract hidden features and patterns even in highly complex datasets. In this study, we used our Big Data IoT Framework in a weather data analysis use case. We implemented weather clustering and sensor anomaly detection using a publicly available dataset. We provided the implementation details of each framework layer (acquisition, ETL, data processing, learning and decision) for this particular use case. Our chosen learning model within the library is Scikit-Learn based k-means clustering. The data analysis results indicate that it is possible to extract meaningful information from a relatively complex dataset using our framework.