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Now showing 1 - 10 of 40
  • PublicationEmbargo
    Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales Forecasting
    (2019-01) AKBULUT, AKHAN; 116056
    — Predicting the sales amount as close as to the actual sales amount can provide many benefits to companies. Since the fashion industry is not easily predictable, it is not straightforward to make an accurate prediction of sales. In this study, we applied not only regression methods in machine learning but also time series analysis techniques to forecast the sales amount based on several features. We applied our models on Walmart sales data in Microsoft Azure Machine Learning Studio platform. The following regression techniques were applied: Linear Regression, Bayesian Regression, Neural Network Regression, Decision Forest Regression and Boosted Decision Tree Regression. In addition to these regression techniques, the following time series analysis methods were implemented: Seasonal ARIMA, Non-Seasonal ARIMA, Seasonal ETS, Non -Seasonal ETS, Naive Method, Average Method, and Drift Method. It was shown that Boosted Decision Tree Regression provides the best performance on this sales data. This project is a part of the development of a new decision support system for the retail industry.
  • PublicationMetadata only
    Comparative Evaluation of Different Classification Techniques for Masquerade Attack Detection
    (Inderscience Enterprises Ltd., 2020) Elmasry, Wisam; AKBULUT, AKHAN; Zaim, Abdul Halim
    Masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial basis for computer security. Although of considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low degree of false alarm rate is still a big challenge. In this paper, we present an extensive empirical study in the area of user behaviour profiling-based masquerade detection using six of different existed machine learning methods in Azure Machine Learning (AML) studio. In order to surpass previous studies on this subject, we used four free and publicly available datasets with seven data configurations are implemented from them. Moreover, eight well-known masquerade detection evaluation metrics are used to assess methods performance against each data configuration. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper.
  • PublicationMetadata only
    Development of a software vulnerability prediction web service based on artificial neural networks
    (2017) Çatal, Çağatay; Ekenoğlu, Ecem; Alemdaroğlu, Meltem; AKBULUT, AKHAN
    Detecting vulnerable components of a web application is an important activity to allocate verification resources effectively. Most of the studies proposed several vulnerability prediction models based on private and public datasets so far. In this study, we aimed to design and implement a software vulnerability prediction web service which will be hosted on Azure cloud computing platform. We investigated several machine learning techniques which exist in Azure Machine Learning Studio environment and observed that the best overall performance on three datasets is achieved when Multi-Layer Perceptron method is applied. Software metrics values are received from a web form and sent to the vulnerability prediction web service. Later, prediction result is computed and shown on the web form to notify the testing expert. Training models were built on datasets which include vulnerability data from Drupal, Moodle, and PHPMyAdmin projects. Experimental results showed that Artificial Neural Networks is a good alternative to build a vulnerability prediction model and building a web service for vulnerability prediction purpose is a good approach for complex systems.
  • PublicationEmbargo
    VinJect: Toolkit for Penetration Testing and Vulnerability Scanning
    (2018) AKBULUT, AKHAN; 116056
    Penetration testing plays an important role in the development of secure software products and electronic systems. Sustainability of commercial systems is ensured through the regular scans of vulnerability. In this era where quality assurance and testing organizations become increasingly widespread, the effectiveness of the used tools and methods are critical. This article describes the architecture of the software named VinJect, which is developed for efficient penetration testing and vulnerability scanning. The primary goal of this application is to detect vulnerable locations in a shorter time with running in a multi-threaded structure. Our proposed application uses Wapiti and SQLmap applications’ services in the background. With user-friendly interfaces, it is also aimed to remove the bad user experience (UX) that these applications running on the command line have. In the tests we performed, WinJect was found to be more efficient in completing the vulnerability scans in a much shorter time.
  • PublicationOpen Access
    Deep Learning-Based Defect Prediction for Mobile Applications
    (MPDI, 2022) JORAYEVA, MANZURA; AKBULUT, AKHAN; Çatal, Çağatay; Mishra, Alok
    Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.
  • PublicationMetadata only
    On the effectiveness of virtual reality in the education of software engineering
    (Wiley, 111 River St, Hoboken 07030-5774, NJ USA, 2018-07) Çatal, Çağatay; Yıldız, Burak; AKBULUT, AKHAN; 116056; 108363
    The popularity of virtual reality headsets have been rapidly increasing. With this technology, students can efficiently interact with the course content and learn the material faster than the traditional methodologies. In addition to this benefit, virtual reality devices also draw the attention of young generation and this helps to the widespread use of this technology among students. In this study, we investigate the use of virtual reality on the performance of computer engineering bachelor science (BS) students within the scope of Data Structures course and develop a software-intensive system called Virtual Reality Enhanced Interactive Teaching Environment (VR-ENITE). Specifically, we focus on the sorting algorithms such as selection sort, bubble sort, insertions sort, and merge sort which are relatively hard to be understood by the BS students at first glance. For the evaluation of VR-ENITE, students were divided into two groups: a group which uses VR-ENITE in addition to the traditional teaching material and the control group which utilizes from only the traditional material. In order to evaluate the performance of these two groups having 36 students in total, a multiple choice exam was delivered to all of them. According to the test results, students who used the VR-ENITE system got 12% more successful results in average than the students who are in the control group. This study experimentally shows that VR-ENITE which is based on virtual reality technology is effective for teaching software engineering courses and it has assistive capabilities for traditional teaching approaches.
  • PublicationEmbargo
    A cloud-based recommendation service using principle component analysis–scale-invariant feature transform algorithm
    (2017-10) Çatal, Çağatay; AKBULUT, AKHAN; AKBULUT, FATMA PATLAR; 116056; 108363; 299243
    Cloud computing delivers resources such as software, data, storage and servers over the Internet; its adaptable infrastructure facilitates on-demand access of computational resources. There are many benefits of cloud computing such as being scalable, paying only for consumption, improving accessibility, limiting investment costs and being environmentally friendly. Thus, many organizations have already started applying this technology to improve organizational efficiency. In this study, we developed a cloud-based book recommendation service that uses a principle component analysis–scale-invariant feature transform (PCA-SIFT) feature detector algorithm to recommend book(s) based on a user-uploaded image of a book or collection of books. The high dimensionality of the image is reduced with the help of a principle component analysis (PCA) pre-processing technique. When the mobile application user takes a picture of a book or a collection of books, the system recognizes the image(s) and recommends similar books. The computational task is performed via the cloud infrastructure. Experimental results show the PCA-SIFT-based cloud recommendation service is promising; additionally, the application responds faster when the pre-processing technique is integrated. The proposed generic cloud-based recommendation system is flexible and highly adaptable to new environments.
  • PublicationMetadata only
    A Review on Blockchain Applications in Fintech Ecosystem
    (Institute of Electrical and Electronics Engineers Inc., 2022) Karadağ, Bulut; AKBULUT, AKHAN; Zaim, Abdül Halim
    The term fintech started to become popular from the 90s. With the rapid development of technology and the widespread use of the internet, Fintech has become a sector in itself, especially since 2004. Within the framework of Fintech, there have been a number of advances in ATM, credit cards, debit cards, mobile transactions, internet banking and digital banking infrastructure and transactions. The emergence of Bitcoin in 2008 caused us to hear the term blockchain frequently, and the path of blockchain technology intersected with fintech. The decentralization of the blockchain, thanks to its distributed ledger structure, made it possible to make Bitcoin transfers without intermediaries. After Bitcoin, the emergence of crypto assets like Ethereum opened the way for these transactions to be programmable as an infrastructure. Programmable blockchain infrastructures have started to be used not only in financial transactions, but also in sectors such as health, supply chain, education and insurance. There are different academic studies on applications related to these sectors. However, there is no such a review that includes them all together for finance. In this study, blockchain applications in the fintech ecosystem were investigated and included in a single study. In particular, it was explained in which business it was used and in which business there was a market volume. In addition, possible future blockchain applications were also mentioned. © 2022 IEEE.
  • PublicationOpen Access
    Identification of Phantom Movements With an Ensemble Learning Approach
    (Pergamon-Elsevier Science Ltd., 2022) AKBULUT, AKHAN; Güngör, Feray; Tarakçı, Ela; Aydın, Muhammed Ali; Zaim, Abdul Halim; Çatal, Çağatay
    Phantom limb pain after amputation is a debilitating condition that negatively affects activities of daily life and the quality of life of amputees. Most amputees are able to control the movement of the missing limb, which is called the phantom limb movement. Recognition of these movements is crucial for both technology-based amputee rehabilitation and prosthetic control. The aim of the current study is to classify and recognize the phantom movements in four different amputation levels of the upper and lower extremities. In the current study, we utilized ensemble learning algorithms for the recognition and classification of phantom movements of the different amputation levels of the upper and lower extremity. In this context, sEMG signals obtained from 38 amputees and 25 healthy individuals were collected and the dataset was created. Studies of processing sEMG signals in amputees are rather limited, and studies are generally on the classification of upper extremity and hand movements. Our study demonstrated that the ensemble learning-based models resulted in higher accuracy in the detection of phantom movements. The ensemble learning-based approaches outperformed the SVM, Decision tree, and kNN methods. The accuracy of the movement pattern recognition in healthy people was up to 96.33%, this was at most 79.16% in amputees.
  • PublicationOpen Access
    Techniques for Apply Predictive Maintenance and Remaining Useful Life: A Systematic Mapping Study
    (Bilecik Şeyh Edebali Üniversitesi, 2021) Türe, Begüm Ay; AKBULUT, AKHAN; Zaim, Abdül Halim
    With prognostic activities, it is possible to predict the remaining useful life (RUL) of industrial systems with high accuracy by following the current health status of devices. In this study, we have collected 199 articles onpredictive maintenance and remaining useful life. The aim of our systematic mapping study is to determine which techniques and methods are used in the areas of predictive maintenance and remaining useful life. Another thing we aim is to give an idea about the main subject to the researchers who will work in this field. We created our article repository by searching databases such as IEEE and Science Direct with certain criteria and classified the articles we obtained. By applying the necessary inclusion and exclusion criteria in the article pool we collected,the most appropriate articles were determined and our study was carried out through these articles. When we focused on the results, it was learned that the SupportVector Machine algorithm is the most preferred predictive maintenance method. Most studies aimed at evaluating the performance and calculating the accuracy of the results used the Root Mean Square Error algorithm. In our study, every method and algorithm included in the articles are discussed. The articles were examined together with the goals and questions we determined, and results were obtained. The obtained results are explained and shown graphically in the article. According to the results, it isseen that the topics of predictive maintenance and remaining useful lifetime provide functionality and financial gain to the environment they are used in. Our study was concluded by light on many questions about the applicationof predictive maintenance.