Person: AKBULUT, AKHAN
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AKBULUT
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- PublicationOpen AccessLWE: An Energy-Efficient Lightweight Encryption Algorithm for Medical Sensors and IoT Devices(Istanbul University, 2020) Toprak, Sezer; AKBULUT, AKHAN; Aydın, Muhammet Ali; Zaim, Abdul HaimIn today's world, systems generate and exchange digital data frequently and face a much broader range of threats than in the past. Within the context of this unsafe ecosystem, it is crucial to protect the data in a quick and secure way. In this paper, it is proposed that a lightweight block cipher algorithm called LWE in the purpose of having an encryption algorithm that is light enough for restricted/limited hardware environments and secure enough to endure primal cryptanalysis attacks. The length of blocks to be encrypted is set to 64 bits and the key length is defined as 64 bits. It is targeted for IoT systems with low-end microcontrollers and body sensor area devices. The performance and security aspects of LWE are evaluated with well-known algorithms and it is observed that LWE can establish a basic security baseline for transmitting raw data without creating a heavy load on the network infrastructure.
- PublicationOpen AccessHayalet Uzuv Sendromu Tedavisi için Sanal Gerçeklik ve Artırılmış Gerçeklik Temelli Sistemin Geliştirilmesi(TÜBİTAK EEEAG Proje, 2020) AKBULUT, AKHAN; Zaim, Abdül Halim; Aydın, Ali; Tarakcı, ElaHayalet uzuv sendromu (Fantom ekstremite ağrısı-FEA), ampütasyon sonrasında bireylerin birçoğunda görülen ve yaşam kalitesini azaltarak hayatlarını olumsuz yönde etkileyen yaygın bir ampütasyon sekelidir. Kaybedilen uzvun beyinde temsil edildiği kortikal alanların, uzuv kaybından dolayı duyusal girdiden yoksun kalması ve komşu duyusal girdilere açık hale gelmesinin kayıp uzuv ile ilgili ağrılı temsillere neden olduğu öne sürülmektedir. FEA'yı tedavi etmeye yönelik birçok farklı uygulama bulunmakla birlikte; etkinliği en fazla gösterilen ve en yaygın kullanılan terapötik yaklaşım ayna terapisidir. Ayna terapisi, sağlam ekstremite ile yapılan hareketlerin yansıma aldatmacasını kullanarak kayıp uzvu beyine varmış gibi gösterip, ağrının azaltılarak bireyin rahatlatılmasını hedeflemektedir. Proje kapsamında, benzer bakış açısıyla ve ayna terapisinin limitasyonlarını ortadan kaldırarak FEA'nın rehabilitasyonunda kullanılmak üzere, 4 farklı ampütasyon bölgesi için sanal gerçeklik ve artırılmış gerçeklik teknolojilerinin yer aldığı 7 (4 SG, 3 AG) oyun geliştirilmiş; katılımcıların ampüte bölgelerinden ölçülen EMG sinyallerinin karşılığı olan fantom hareketler belirlenerek interaktif oyunlar içerisindeki modellere yansıtılması yöntemi ile rehabilitasyon seansları gerçekleştirilmiştir. Fantom hareketlerin yüksek doğrulukla sınıflandırılması için 71 kişiden toplanan özgün bir veriseti oluşturulmuş ve sistemin kullandığı yapay öğrenme modelinin eğitilmesinde kullanılmıştır. Farklı yapay öğrenme algoritmaları ile yapılan deneylerde en yüksek başarımı sunan modeller, ilk örnekleme entegre edilmiş ve sistem %88,94?e varan doğrulukla hareketleri sınıflandırmıştır. Proje konusu, fizyolojik sinyallerin ölçümünde gündelik hareketleri etkilemeden kullanılabilecek giyilebilir bir sensör cihazın geliştirilmesi, sağlık verisinin az kaynak tüketerek güvenli bir şekilde aktarılması ve fizyoterapistlerin hasta takibini yapabileceği web uygulamalarının geliştirmesini de kapsamaktadır. Önerilen sistemin ilk örneklemi 12 hasta ile test edilmiş; yapılan kullanım analizi ve geribildirimler neticesinde sistemin ampüte bireyler için kontrol edilebilir, doğal, eğlenceli, dalma seviyesi yüksek, fantom ekstremiteyi hareket ettirmelerini sağlayan, kalan uzuvdaki kasların çalışmasına katkıda bulunan ve kassal yorgunluk oluşturmayan bir rehabilitasyon aracı olabileceği, iyi bir değerlendirme sonrasında sistemin kullanımı ile ilgili herhangi bir şikayeti olmayan ampüte bireyler tarafından rahatlıkla kullanılabileceği ve yüksek memnuniyet düzeyine sahip olduğu görülmüştür.
- PublicationOpen AccessA Design of an Integrated Cloud-Based Intrusion Detection System with Third Party Cloud Service(Walter de Gruyter GmbH, 2021) Elmasry, Wisam; AKBULUT, AKHAN; Zaim, Abdul HalimAlthough cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature. © 2021 Wisam Elmasry et al., published by De Gruyter 2021.
- PublicationOpen AccessTechniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study(MDPI, 2021) Mishra, Alok; Shatnawi, Raed; Çatal, Çağatay; AKBULUT, AKHANSeveral aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation.
- PublicationOpen AccessA Bayesian Deep Neural Network Approach to Seven-Point Thermal Sensation Perception(IEEE-Inst Electrical Electronics Engineers Inc., 2022) ÇAKIR, MUSTAFA; AKBULUT, AKHANTo create and maintain comfortable indoor environments, predicting occupant thermal sensation is an important goal for architects, engineers, and facility managers. The link between thermal comfort, productivity, and health is common knowledge, and researchers have developed many state-of-the-art thermal-sensation models from dozens of research projects over the last 50 years. In addition to these, the use of intelligent data-analysis techniques, such as black-box artificial neural networks (ANNs), is receiving research attention with the aim of designing building thermal-behavior models from collected data. With the convergence of the internet of things (IoT), cloud computing, and artificial intelligence (AI), smart buildings now protect us and keep us comfortable while saving energy and cutting emissions. These types of smart buildings play a vital role in building smart cities of the future. The aim of this study is to help facility managers predict the thermal sensation of the occupants under the given circumstances. To achieve this, we applied a data-driven approach to predict the thermal sensation of occupants of an indoor environment using previously collected data. Our main contribution is to design and evaluate a deep neural network (DNN) for predicting thermal sensations with a high degree of accuracy regardless of building type, climate zone, or a building's heating and/or ventilation methods. We used the second version of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database to train our model. The hyperparameter-tuning process of the proposed model is optimized using the Bayesian strategy and predicts the thermal sensation of occupants with 78% accuracy, which is much higher than the traditional predicted mean vote (PMV) model and the other shallow and deep networks compared.
- PublicationOpen AccessTechniques for Apply Predictive Maintenance and Remaining Useful Life: A Systematic Mapping Study(Bilecik Şeyh Edebali Üniversitesi, 2021) Türe, Begüm Ay; AKBULUT, AKHAN; Zaim, Abdül HalimWith 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.
- PublicationMetadata onlyOn the effectiveness of virtual reality in the education of software engineering(Wiley, 111 River St, Hoboken 07030-5774, NJ USA, 2018-07) Akbulut, Akhan; Çatal, Çağatay; Yıldız, Burak; AKBULUT, AKHAN; 116056; 108363The 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.
- PublicationMetadata onlyDevelopment of a software vulnerability prediction web service based on artificial neural networks(2017) Çatal, Çağatay; Akbulut, Akhan ; Ekenoğlu, Ecem; Alemdaroğlu, Meltem; AKBULUT, AKHANDetecting 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.
- PublicationMetadata onlyA Wearable Device for Virtual Cyber Therapy of Phantom Limb Pain(2018-09) Aşcı, Güven; Akbulut, Akhan ; Tarakçı, Ela; Aydın, Muhammed; Zaim, Abdul Halim; AKBULUT, AKHAN; 285689; 116056; 101760; 176402; 8693Phantom limb pain (PLP) is the condition most often occurs in people who have had a limb amputated and it is may affect their life severely. When the brain sends movement signals to the phantom limb, it returns and causes a pain. Many medical approaches aim to treat the PLP, however the mirror therapy still considered as the base therapy method. The aim of this research is to develop a wearable device that measures the EMG signals from PLP patients to classify movements on the amputated limb. These signals can be used in virtual reality and augmented reality environments to realize the movements in order to reduce pain. A data set was generated with measurements taken from 8 different subjects and the classification accuracy achieved as 90% with Neural Networks method that can be used in cyber therapies.This type of therapy provides strong visuals which make the patient feel he/she really have the limb. The patient will have great therapy session time with comparison to the other classical therapy methods that can be used in home environments.
- PublicationOpen AccessAn architectural model for content management in e-commerce applications using intelligent agents(İstanbul Kültür Üniversitesi / Fen Bilimleri Enstitüsü / Bilgisayar Mühendisliği Anabilim Dalı, 2008-07) AKBULUT, AKHAN; Yılmaz, GürayBu çalışmanın amacı e-ticaret (e-commerce) uygulamalarının içerik toplama ve yönetimi konularına değinerek, bu alanlardaki problemlere bir çözüm önerisi sunmaktır. Ortak bir çatı altında birleşmiş pek çok e-mağaza`nın (e-shop) oluşturduğu e-Alışveriş Merkezi (e-Mall) sitelerde; üye mağazaların gönderdiği ürün bilgilerinin sisteme uygun bir şekilde dâhil edilmesi ve yönetimsel işlevlerin otonom bir yapı ile sunulması gerekmektedir. Bu mimari organizasyon için çoklu-ajan (multi-agent) destekli bir platform kullanılması öngörülmektedir. Çoklu-ajan platformunda farklı görevlerde çalışacak olan ajanlar sırasıyla; gönderilen içeriğin barındırdığı her bir ürün için açıklamalarında geçen kritik kelimeleri belirleyecek, anahtarlama (hashing) fonksiyonları ve kümeleme (clustering) teknikleri kullanarak sistem dâhilindeki en uygun ürün kategorisi altına yerleştirilmesi sağlanacaktır. Anahtar Kelimeler ? Akıllı ajanlar, e-ticaret, e-mağaza, içerik yönetimi, anahtarlama, kümeleme