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Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/253

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Browsing Bilgisayar Mühendisliği Bölümü Yayın Koleksiyonu by Author "142876"

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    Citation Count: Sezer, Omer Berat; Ozbayoglu, Murat; Dogdu, Erdogan (2017). A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters, Conference: Complex Adaptive Systems Conference on Engineering Cyber Physical Systems (CAS) Location: Chicago, IL Date: OCT 30-NOV 01, 2017, Complex Adaptive Systems Conference With Theme: Engineering Cyber Physical Systems, Cas, 114, 473-480.
    A Deep Neural-Network Based Stock Trading System Based on Evolutionary Optimized Technical Analysis Parameters
    (Elsevier Science BV, 2017) Sezer, Ömer Berat; Özbayoğlu, Murat; Doğdu, Erdoğan; 142876
    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.
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    Citation Count: Kasnesis, Panagiotis; Tatlas, Nicolaos-Alexandros; Mitilineos, Stelios A.; et al., "Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding", Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding, Vol. 19, No. 7, pp. 99-107, (2018).
    Classification of Linked Data Sources Using Semantic Scoring
    (Ieice-Inst Electronics Information Communications Eng, 2018) Yumuşak, Semih; Doğdu, Erdoğan; Kodaz, Halife; 142876
    Cultural heritage sites, apart from being the tangible link to a country's history and culture, actively contribute to the national economy, offering a foundation upon which cultural tourism can develop. This importance at the cultural and economic level, advocates for the need for preservation of cultural heritage sites for the future generations. To this end, advanced monitoring systems harnessing the power of sensors are deployed near the sites to collect data which can fuel systems and processes aimed at protection and preservation. In this paper we present the use of acoustic sensors for safeguarding cultural sites located in rural or urban areas, based on a novel data flow framework. We developed and deployed Wireless Acoustic Sensors Networks that record audio signals, which are transferred to a modular cloud platform to be processed using an efficient deep learning algorithm (f1-score: 0.838) to identify audio sources of interest for each site, taking into account the materials the assets are made of. The extracted information is presented exploiting the designed STORM Audio Signal ontology and then fused with spatiotemporal information using semantic rules. The results of this work give valuable insight to the cultural experts and are publicly available using the Linked Open Data format.