Bilgisayar Mühendisliği Bölümü

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Citation - WoS: 11
Citation - Scopus: 12
Adopting Virtual Reality as a Medium for Software Development Process Education
(Assoc Computing Machinery, 2018) Isler, Veysi; O'Connor, Rory, V; Clarke, Paul; Gulec, Ulas; Yilmaz, Murat
Software development is a complex process of collaborative endeavour which requires hands-on experience starting from requirement analysis through to software testing and ultimately demands continuous maintenance so as to mitigate risks and uncertainty. Therefore, training experienced software practitioners is a challenging task. To address this gap, we propose an interactive virtual reality training environment for software practitioners to gain virtual experience based on the tasks of software development. The goal is to transport participants to a virtual software development organization where they experience simulated development process problems and conflicting situations, where they will interact virtually with distinctive personalities, roles and characters borrowed from real software development organizations. This PhD in progress paper investigates the literature and proposes a novel approach where participants can acquire important new process knowledge. Our preliminary observations suggest that a complementary VR-based training tool is likely to improve the experience of novice software developers and ultimately it has a great potential for training activities in software development organizations.
Analysis of Neurooncological Data To Predict Success of Operation Through Classification
(Assoc Computing Machinery, 2016) Tokdemir, Gul; Cagiltay, Nergiz; Maras, H. Hakan; Bagherzadi, Negin; Borcek, Alp Ozgun
Data mining algorithms have been applied in various fields of medicine to get insights about diagnosis and treatment of certain diseases. This gives rise to more research on personalized medicine as patient data can be utilized to predict outcomes of certain treatment procedures. Accordingly, this study aims to create a model to provide decision support for surgeons in Neurooncology surgery. For this purpose, we have analyzed clinical pathology records of Neurooncology patients through various classification algorithms, namely Support Vector Machine, Multi Perceptron and Naive Bayes methods, and compared their performances with the aim of predicting surgery complication. A large number of factors have been considered to classify and predict percentage of patient's complication in surgery. Some of the factors found to be predictive were age, sex, clinical presentation, previous surgery type etc. For classification models built up using Support Vector Machine, Naive Bayes and Multi Perceptron, Classification trials for Support Vector Machine have shown %77.47 generalization accuracy, which was established by 5-fold cross-validation.
Citation - WoS: 30
Citation - Scopus: 51
An Artificial Neural Network-Based Stock Trading System Using Technical Analysis and Big Data Framework
(Assoc Computing Machinery, 2017) Ozbayoglu, A. Murat; Dogdu, Erdogan; Sezer, Omer Berat
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.
Citation - Scopus: 2
A Discovery and Analysis Engine for Semantic Web
(Assoc Computing Machinery, 2018) Kamilaris, Andreas; Dogdu, Erdogan; Kodaz, Halife; Uysal, Elif; Aras, Riza Emre; Yumusak, Semih
The Semantic Web promotes common data formats and exchange protocols on the web towards better interoperability among systems and machines. Although Semantic Web technologies are being used to semantically annotate data and resources for easier reuse, the ad hoc discovery of these data sources remains an open issue. Popular Semantic Web endpoint repositories such as SPARQLES, Linking Open Data Project (LOD Cloud), and LODStats do not include recently published datasets and are not updated frequently by the publishers. Hence, there is a need for a web-based dynamic search engine that discovers these endpoints and datasets at frequent intervals. To address this need, a novel web meta-crawling method is proposed for discovering Linked Data sources on the Web. We implemented the method in a prototype system named SPARQL Endpoints Discovery (SpEnD). In this paper, we describe the design and implementation of SpEnD, together with an analysis and evaluation of its operation, in comparison to the aforementioned static endpoint repositories in terms of time performance, availability, and size. Findings indicate that SpEnD outperforms existing Linked Data resource discovery methods.
Citation - WoS: 139
Citation - Scopus: 209
Intrusion Detection Using Big Data and Deep Learning Techniques
(Assoc Computing Machinery, 2019) Dogdu, Erdogan; Faker, Osama
In this paper, Big Data and Deep Learning Techniques are integrated to improve the performance of intrusion detection systems. Three classifiers are used to classify network traffic datasets, and these are Deep Feed-Forward Neural Network (DNN) and two ensemble techniques, Random Forest and Gradient Boosting Tree (GBT). To select the most relevant attributes from the datasets, we use a homogeneity metric to evaluate features. Two recently published datasets UNSW NB15 and CICIDS2017 are used to evaluate the proposed method. 5-fold cross validation is used in this work to evaluate the machine learning models. We implemented the method using the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library to implement the deep learning technique while the ensemble techniques are implemented using Apache Spark Machine Learning Library. The results show a high accuracy with DNN for binary and multiclass classification on UNSW NB15 dataset with accuracies at 99.16% for binary classification and 97.01% for multiclass classification. While GBT classifier achieved the best accuracy for binary classification with the CICIDS2017 dataset at 99.99%, for multiclass classification DNN has the highest accuracy with 99.56%.
Citation - WoS: 40
Citation - Scopus: 77
Malware Classification Using Deep Learning Methods
(Assoc Computing Machinery, 2018) Dogdu, Erdogan; Cakir, Bugra
Malware, short for Malicious Software, is growing continuously in numbers and sophistication as our digital world continuous to grow. It is a very serious problem and many efforts are devoted to malware detection in today's cybersecurity world. Many machine learning algorithms are used for the automatic detection of malware in recent years. Most recently, deep learning is being used with better performance. Deep learning models are shown to work much better in the analysis of long sequences of system calls. In this paper a shallow deep learning-based feature extraction method (word2vec) is used for representing any given malware based on its opcodes. Gradient Boosting algorithm is used for the classification task. Then, k-fold cross-validation is used to validate the model performance without sacrificing a validation split. Evaluation results show up to 96% accuracy with limited sample data.
Citation - WoS: 7
Phishing E-Mail Detection by Using Deep Learning Algorithms
(Assoc Computing Machinery, 2018) Hassanpour, Reza; Dogdu, Erdogan; Choupani, Roya; Goker, Onur; Nazli, Nazli
Citation - WoS: 8
Citation - Scopus: 11
Sentiment Analysis for the Social Media: a Case Study for Turkish General Elections
(Assoc Computing Machinery, 2017) Yumusak, Semih; Oztoprak, Kasim; Dogdu, Erdogan; Uysal, Elif
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.

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