Browsing by Author "Zaim, Abdül Halim"

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Deep Learning Approaches for Phantom Movement Recognition
(2019-11-03) Güngör, Faray; Tarakçı, Ela; Aydın, Muhammed Ali; Zaim, Abdül Halim; AKBULUT, AKHAN; AŞCI, GÜVEN; 116056; 285689; 277179; 101760; 176402; 8693
Phantom limb pain has a negative effect on the life of individuals as a frequent consequence of limb amputation. The movement ability on the lost extremity can still be maintained after the amputation or deafferentation, which is called the phantom movement. The detection of these movements makes sense for cybertherapy and prosthetic control for amputees. In this paper, we employed several deep learning approaches to recognize phantom movements of the three different amputation regions including above-elbow, below-knee and above-knee. We created a dataset that contains 25 healthy and 16 amputee participants’ surface electromyography (sEMG) readings via a wearable device with 2-channel EMG sensors. We compared the results of three different deep learning methods, respectively, Multilayer Perceptron, Convolutional Neural Network, and Recurrent Neural Network with the accuracies of two well-known shallow methods, k Nearest Neighbor and Random Forest. Our experiments indicate, Convolutional Neural Network-based model achieved an accuracy of 74.48% in recognizing phantom movements of amputees.
Embargo
Deep Learning Approaches for Predictive Masquerade Detection
(Wiley-Hindawi, Adam House, 3rd Fl, 1 Fitzroy Sq, London, Wit 5He, England, 2018) Elmasry, Wisam; Zaim, Abdül Halim; AKBULUT, AKHAN; 116056; 8693
In computer security, masquerade detection is a special type of intrusion detection problem. Effective and early intrusion detection is a crucial factor for computer security. Although considerable work has been focused on masquerade detection for more than a decade, achieving a high level of accuracy and a comparatively low false alarm rate is still a big challenge. In this paper, we present a comprehensive empirical study in the area of anomaly-based masquerade detection using three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Convolutional Neural Networks (CNN). In order to surpass previous studies on this subject, we used three UNIX command line-based datasets, with six variant data configurations implemented from them. Furthermore, static and dynamic masquerade detection approaches were utilized in this study. In a static approach, DNN and LSTM-RNN models are used along with a Particle Swarm Optimization-based algorithm for their hyperparameters selection. On the other hand, a CNN model is employed in a dynamic approach. Moreover, twelve well-known evaluation metrics are used to assess model performance in each of the data configurations. Finally, intensive quantitative and ROC curves analyses of results are provided at the end of this paper. The results not only show that deep learning models outperform all traditional machine learning methods in the literature but also prove their ability to enhance masquerade detection on the used datasets significantly.
Open Access
Hayalet Uzuv Sendromu Tedavisi için Sanal Gerçeklik ve Artırılmış Gerçeklik Temelli Sistemin Geliştirilmesi
(TÜBİTAK EEEAG Proje, 2020) AKBULUT, AKHAN; Zaim, Abdül Halim; Aydın, Ali; Tarakcı, Ela
Hayalet 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.
Embargo
Mobil Cihazlarda Güvenlik Tehditler Temel Stratejiler
(Istanbul Commerce University, 2016-09-30) Zaim, Abdül Halim; AKBULUT, AKHAN; BAYDOĞMUŞ, GÖZDE KARATAŞ; 110942; 116056
The most common form of today’s consumer electronics is the use of mobile devices. The technological developments in this area are increasing in such a way as to affect all aspects of our lives and give direction to our life. The use of mobile devices, which can now have the same hardware features as computers, is not only for communication but also enriched by applications in the areas of internet use, business, hobby and health. Increased usage rate and the need for information and communication security are beginning to be needed and it is necessary to ensure the security of the information carried against the attacks against these devices. Due to security vulnerabilities in mobile devices and malicious software loaded applications by end users, there are situations that threaten personal information and communication security. This study describes security vulnerabilities, attacks in mobile applications and precautions for those problems. It summarizes not only the end-user's recommendations, but also the points to note for app developers. It is evaluated that end users can increase personal security by learning basic info for attack methods of mobile systems.
Metadata 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.
Open 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.