Browsing by Author "Dogdu, Erdogan"

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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
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.
An Artificial Neural Network-Based Stock Trading System Using Technical Analysis And Big Data Framework
(Assoc Computing Machinery, 2017) Sezer, Omer Berat; Doğdu, Erdoğan; Ozbayoglu, A. Murat; Dogdu, Erdogan
In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.
Classification of Linked Data Sources Using Semantic Scoring
(Ieice-inst Electronics information Communication Engineers, 2018) Yumusak, Semih; Doğdu, Erdoğan; Dogdu, Erdogan; Kodaz, Halife; 142876
Linked data sets are created using semantic Web technologies and they are usually big and the number of such datasets is growing. The query execution is therefore costly, and knowing the content of data in such datasets should help in targeted querying. Our aim in this paper is to classify linked data sets by their knowledge content. Earlier projects such as LOD Cloud, LODStats, and SPARQLES analyze linked data sources in terms of content, availability and infrastructure. In these projects, linked data sets are classified and tagged principally using VoID vocabulary and analyzed according to their content, availability and infrastructure. Although all linked data sources listed in these projects appear to be classified or tagged, there are a limited number of studies on automated tagging and classification of newly arriving linked data sets. Here, we focus on automated classification of linked data sets using semantic scoring methods. We have collected the SPARQL endpoints of 1,328 unique linked datasets from Datahub, LOD Cloud, LODStats, SPARQLES, and SpEnD projects. We have then queried textual descriptions of resources in these data sets using their rdfs: comment and rdfs: label property values. We analyzed these texts in a similar manner with document analysis techniques by assuming every SPARQL endpoint as a separate document. In this regard, we have used WordNet semantic relations library combined with an adapted term frequency-inverted document frequency (tfidf) analysis on the words and their semantic neighbours. In WordNet database, we have extracted information about comment/label objects in linked data sources by using hypernym, hyponym, homonym, meronym, region, topic and usage semantic relations. We obtained some significant results on hypernym and topic semantic relations; we can find words that identify data sets and this can be used in automatic classification and tagging of linked data sources. By using these words, we experimented different classifiers with different scoring methods, which results in better classification accuracy results.
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey
(Ieee-inst Electrical Electronics Engineers inc, 2018) Sezer, Omer Berat; Doğdu, Erdoğan; Dogdu, Erdogan; Ozbayoglu, Ahmet Murat
Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Meanwhile, with this revolutionary transformation, researchers, implementers, deployers, and users are faced with many challenges. IoT is a complicated, crowded, and complex field; there are various types of devices, protocols, communication channels, architectures, middleware, and more. Standardization efforts are plenty, and this chaos will continue for quite some time. What is clear, on the other hand, is that IoT deployments are increasing with accelerating speed, and this trend will not stop in the near future. As the field grows in numbers and heterogeneity, "intelligence" becomes a focal point in IoT. Since data now becomes "big data," understanding, learning, and reasoning with big data is paramount for the future success of IoT. One of the major problems in the path to intelligent IoT is understanding "context," or making sense of the environment, situation, or status using data from sensors, and then acting accordingly in autonomous ways. This is called "context-aware computing," and it now requires both sensing and, increasingly, learning, as IoT systems get more data and better learning from this big data. In this survey, we review the field, first, from a historical perspective, covering ubiquitous and pervasive computing, ambient intelligence, and wireless sensor networks, and then, move to context-aware computing studies. Finally, we review learning and big data studies related to IoT. We also identify the open issues and provide an insight for future study areas for IoT researchers.
Sentiment Analysis for the Social Media: A Case Study for Turkish General Elections
(Assoc Computing Machinery, 2017) Uysal, Elif; Doğdu, Erdoğan; Yumusak, Semih; Oztoprak, Kasim; Dogdu, Erdogan
The ideas expressed in social media are not always compliant with natural language rules, and the mood and emotion indicators are mostly highlighted by emoticons and emotion specific keywords. There are language independent emotion keywords (e.g. love, hate, good, bad), besides every language has its own particular emotion specific keywords. These keywords can be used for polarity analysis for a particular sentence. In this study, we first created a Turkish dictionary containing emotion specific keywords. Then, we used this dictionary to detect the polarity of tweets that are collected by querying political keywords right before the Turkish general election in 2015. The tweets were collected based on their relatedness with three main categories: the political leaders, ideologies, and political parties. The polarity of these tweets are analyzed in comparison with the election results.
The impact of incapacitation of multiple critical sensor nodes on wireless sensor network lifetime
(Ieee-inst Electrical Electronics Engineers inc, 2017) Yildiz, Huseyin Ugur; Doğdu, Erdoğan; Tavli, Bulent; Kahjogh, Behnam Ojaghi; Dogdu, Erdogan
Wireless sensor networks (WSNs) are envisioned to be utilized in many application areas, such as critical infrastructure monitoring, and therefore, WSN nodes are potential targets for adversaries. Network lifetime is one of the most important performance indicators in WSNs. The possibility of reducing the network lifetime significantly by eliminating a certain subset of nodes through various attacks will create the opportunity for the adversaries to hamper the performance of WSNs with a low risk of detection. However, the extent of reduction in network lifetime due to elimination of a group of critical sensor nodes has never been investigated in the literature. Therefore, in this letter, we create two novel algorithms based on a linear programming framework to model and analyze the impact of critical node elimination attacks on WSNs and explore the parameter space through numerical evaluations of the algorithms. Our results show that critical node elimination attacks can significantly shorten the network lifetime.
Topic Distribution Constant Diameter Overlay Design Algorithm (TD-CD-ODA)
(Ieee, 2017) Layazali, Sina; Doğdu, Erdoğan; Oztoprak, Kasim; Dogdu, Erdogan; 129828
Publish/subscribe communication systems, where nodes subscribe to many different topics of interest, are becoming increasingly more common in application domains such as social networks, Internet of Things, etc. Designing overlay networks that connect the nodes subscribed to each distinct topic is hence a fundamental problem in these systems. For scalability and efficiency, it is important to keep the maximum node degree of the overlay in the publish/subscribe system low. Ideally one would like to be able not only to keep the maximum node degree of the overlay low, but also to ensure that the network has low diameter. We address this problem by presenting Topic Distribution Constant Diameter Overlay Design Algorithm (TD-CD-ODA) that achieves a minimal maximum node degree in a low-diameter setting. We have shown experimentally that the algorithm performs well in both targets in comparison to the other overlay design algorithms.