Browsing by Author "Ozbayoglu, Murat"

Now showing 1 - 4 of 4

Citation - WoS: 56
Citation - Scopus: 83
A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters
(Elsevier Science Bv, 2017) Sezer, Omer Berat; Doğdu, Erdoğan; Ozbayoglu, Murat; Dogdu, Erdogan; 142876; Bilgisayar Mühendisliği
In this study, we propose a stock trading system based on optimized technical analysis parameters for creating buy-sell points using genetic algorithms. The model is developed utilizing Apache Spark big data platform. The optimized parameters are then passed to a deep MLP neural network for buy-sell-hold predictions. Dow 30 stocks are chosen for model validation. Each Dow stock is trained separately using daily close prices between 1996-2016 and tested between 2007-2016. The results indicate that optimizing the technical indicator parameters not only enhances the stock trading performance but also provides a model that might be used as an alternative to Buy and Hold and other standard technical analysis models. (c) 2017 The Authors. Published by Elsevier B.V.
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.
Citation - WoS: 0
Citation - Scopus: 0
Perceptions, Expectations and Implementations of Big Data in Public Sector
(Ieee, 2018) Dogdu, Erdogan; Doğdu, Erdoğan; Ozbayoglu, Murat; Yazici, Ali; Karakaya, Ziya; Bilgisayar Mühendisliği
Big Data is one of the most commonly encountered buzzwords among IT professionals nowadays. Technological advancements in data acquisition, storage, telecommunications, embedded systems and sensor technologies resulted in huge inflows of streaming data coming from variety of sources, ranging from financial streaming data to social media tweets, or wearable health gadgets to drone flight logs. The processing and analysis of such data is a difficult task, but as appointed by many IT experts, it is crucial to have a Big Data Implementation plan in today's challenging industry standards. In this study, we performed a survey among IT professionals working in the public sector and tried to address some of their implementation issues and their perception of Big Data today and their expectations about how the industry will evolve. The results indicate that most of the public sector professionals are aware of the current Big Data requirements, embrace the Big Data challenge and are optimistic about the future.
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.