Researcher:
AKBULUT, AKHAN

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AKHAN

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AKBULUT

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2008 - 200912010 - 2019262020 - 202421

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Now showing 1 - 10 of 48
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    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.
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    Code Generator Framework for Smart TV Platforms
    (INST ENGINEERING TECHNOLOGY-IET, MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND, 2019-08) TOPRAK, SEZER; AKBULUT, AKHAN; 116056; 176006
    In recent years, smart TVs have become more common, making them need to be included as targets for the software industry. In this study, the authors developed a code generator framework and demonstrated it in an architectural view. The proposed framework converts C# programming language based projects, in a Windows Forms or a Windows Phone Application project, into native smart TV Platform applications. The selected primary smart TV platforms assigned for application conversion were Android TV, Firefox OS, and Tizen OS. The authors enabled developers to generate native codes for all three platforms from a single code base using model to model conversion, as in the model driven architecture approach with the use of the open source Roslyn C# language compiler. The need for creating projects for every single platform to make them run on different platforms will thus be eliminated and development cycles shortened. By doing so, the time required to develop an application for each platform is reduced while keeping the generated applications' quality as high as the original application. To show the functionality, the proposed approach is applied in three case studies. The success of the code conversion is satisfactory and converted applications are functional.
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    PublicationEmbargo
    Son Kullanıcı Geliştirme için Otomatik Kod Üretim Aracının Tasarımı ve Gerçeklenmesi
    (2017) AKBULUT, AKHAN; 116056
    Son kullanıcı geliştirme yaklaşımları; yazılım mühendisi olmayan kullanıcıların, yazılım çıktılarını kendilerinin oluşturabileceği, değiştirebileceği ve uyarlayabileceği teknolojiler ve yöntemlere odaklanmaktadır. Bu amaçla; birleştirme teknolojileri, örnek ile programlama, görsel programlama, model tabanlı yaklaşımlar, servis yönelimli mimariler ve otomatik kod üretimi kullanılabilmektedir. Bu çalışmada, görsel programlama ile birlikte otomatik kod üretimi tercih edilmiş ve bir son kullanıcı geliştirme aracı gerçeklenmiştir. Seçilen uygulama alanı olan rezervasyon sistemleri için otomatik kod üretimi sağlanmıştır. Bu sürecin temel faydası; geliştirme zamanın kısaltılması, son kullanıcıların geliştirmesini yapabilmesi ve sistem tasarımı ile üretilen uygulama arasındaki farklılıkların en aza indirgenerek, tutarlılığın sağlanmasıdır. Farklı rezervasyon sistemleri için kullanılması amaçlanan bu sistem, kullanıcıların iş modellerini görsel ara yüzler ile tanımlamalarına ve bu tasarımdan web tabanlı uygulamanın çalışması için gerekli tüm dosyaların üretilmesine imkan tanımaktadır. Son kullanıcılarda web teknolojilerine ait herhangi bir geliştirme tecrübesi aranmayıp, uygulamanın çalışması için gerekli ara yüzler, stil ve tasarım dosyaları, veritabanının oluşturulması otomatik kod üretimi ile gerçekleştirilmiştir. Bu çalışmada; son kullanıcı geliştirme için görsel programlama ve otomatik kod üretimi tekniklerinin, alana özel uygulanması gerektiği, jenerik yaklaşımların etkin olmayacağı sonucuna varılmıştır.
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    Native Code Generation as a Service
    (2019-02) Çatal, Çağatay; Karadeniz, Emre; Turgut, Emre; AKBULUT, AKHAN; 108363
    With the widespread use of mobile applications in daily life, it has become crucial for enterprise software companies to quickly develop these applications for multiple platforms. Cross-platform mobile application development is one of the most adopted solutions for rapid development. Since most of these solutions do not generate native code for the underlying platform, the artefacts generally do not satisfy the requirements defined at the beginning of the project. This study designed and implemented a native code generation framework called Nativator built as a cloud service. The framework, which is capable of producing native code for iOS and Android platforms using web-based user interfaces, was implemented based on an open source compiler platform called “Roslyn”. Four case studies were performed to analyze the execution performance of the applications built with the proposed framework. The experimental results demonstrated that the execution performance of the applications built with Nativator is comparable with the applications generated via the state-of-the-art mobile application development framework called Xamarin. Because this framework was implemented as a cloud service, it has several advantages over traditional approaches such as access from anywhere, no installation and flexible and more resources from cloud infrastructure.
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    PublicationOpen Access
    A 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 Halim
    Although 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.
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    PublicationOpen Access
    LWE: An Energy-Efficient Lightweight Encryption Algorithm for Medical Sensors and IoT Devices
    (Istanbul University, 2020) Toprak, Sezer; AKBULUT, AKHAN; Aydın, Muhammet Ali; Zaim, Abdul Haim
    In 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.
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    A Wearable Device for Virtual Cyber Therapy of Phantom Limb Pain
    (2018-09) Tarakçı, Ela; Aydın, Muhammed; Zaim, Abdul Halim; AKBULUT, AKHAN; AŞCI, GÜVEN; 285689; 116056; 101760; 176402; 8693
    Phantom 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.
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    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.
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    PublicationOpen Access
    Stacking-Based Ensemble Learning for Remaining Useful Life Estimation
    (Springer, 2023) Türe, Begüm Ay; AKBULUT, AKHAN; Zaim, Abdul Halim; Çatal, Çağatay
    Excessive and untimely maintenance prompts economic losses and unnecessary workload. Therefore, predictive maintenance models are developed to estimate the right time for maintenance. In this study, predictive models that estimate the remaining useful life of turbofan engines have been developed using deep learning algorithms on NASA's turbofan engine degradation simulation dataset. Before equipment failure, the proposed model presents an estimated timeline for maintenance. The experimental studies demonstrated that the stacking ensemble learning and the convolutional neural network (CNN) methods are superior to the other investigated methods. While the convolution neural network (CNN) method was superior to the other investigated methods with an accuracy of 93.93%, the stacking ensemble learning method provided the best result with an accuracy of 95.72%.
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    Enhancing Targeting in CRM Campaigns Through Explainable AI
    (Springer Science and Business Media Deutschland GmbH, 2024) Ayaz, Teoman Berkay; Özara, Muhammet Furkan; Sezer, Emrah; Çelik, Ahmet Erkan; AKBULUT, AKHAN
    Modern customer relationship management (CRM) solutions are vital to firms because they streamline the administration of customer interactions, sales processes, and marketing initiatives. To fully exploit the potential of massive volumes of customer data, these platforms need help from AI techniques to quickly evaluate and extract useful insights, personalize customer experiences, and optimize decision-making to improve business outcomes. This study delves into the use of explainable AI methods like SHAP, LIME, and ELI5 to analyze CRM campaign outcomes. The purpose of this research is to discover essential traits that serve as indications for successful targeting by analyzing a dataset that captures the results of customers’ interactions with campaign content as responder or non-responder. Using these methods improves interpretability and closes the gap between AI-driven decision-making and human understanding. The findings add to the field by offering clear rationales for consumer actions, which in turn helps companies fine-tune their targeting tactics and boost the efficiency of their campaigns. This study emphasizes the value of AI systems being transparent and interpretable in order to promote trust and enable data-driven decision-making in CRM contexts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.