Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11413/6817
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Browsing Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering by Author "AKBULUT, AKHAN"
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Publication Open Access A 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.Publication Metadata only 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; 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.Publication Embargo Automated testing for distributed databases with fuzzy fragment reallocation(TUBİTAK Scientific & Technical Research Council Turkey, Ataturk Bulvarı No 221, Kavaklıdere, Ankara, 00000, Turkey, 2018) AKBULUT, AKHAN; BAYDOĞMUŞ, GÖZDE KARATAŞ; 116056; 110942As the Internet and big data become more widespread, relational database management systems (RDBMSs) become increasingly inadequate for web applications. To provide what RDBMSs cannot, during the past 15 years distributed database systems (DDBSs) have thus emerged. However, given the complicated structure of these systems, the methods used to validate the efficiency of databases, all towards ensuring quality and security, have become diversified. In response, this paper demonstrates a system for performing automated testing with DDBSs, given that testing is significant in software verification and that accredited systems are more productive in business environments. The proposed system applies several tests to MongoDB-based NoSQL databases to evaluate their instantaneous conditions, such as average query response times and fragment utilizations, and, if necessary, suggest improvements for indexes and fragment deployment. Within this context, autogenerated data, replica, meta, system, fragment, and index tests are applied. Clearly, the system's most important feature is its fuzzy logic-enabled fragment reallocation module, which allows the creation and application of reallocation strategies that account for data changes in query frequency.Publication Metadata only Automatic energy expenditure measurement for health science(Elsevier Ireland Ltd, Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Ireland, 2018-04) Çatal, Çağatay; AKBULUT, AKHAN; 108363; 116056Background and objective: It is crucial to predict the human energy expenditure in any sports activity and health science application accurately to investigate the impact of the activity. However, measurement of the real energy expenditure is not a trivial task and involves complex steps. The objective of this work is to improve the performance of existing estimation models of energy expenditure by using machine learning algorithms and several data from different sensors and provide this estimation service in a cloud-based platform. Methods: In this study, we used input data such as breathe rate, and hearth rate from three sensors. Inputs are received from a web form and sent to the web service which applies a regression model on Azure cloud platform. During the experiments, we assessed several machine learning models based on regression methods. Results: Our experimental results showed that our novel model which applies Boosted Decision Tree Regression in conjunction with the median aggregation technique provides the best result among other five regression algorithms. Conclusions: This cloud-based energy expenditure system which uses a web service showed that cloud computing technology is a great opportunity to develop estimation systems and the new model which applies Boosted Decision Tree Regression with the median aggregation provides remarkable results. (C) 2018 Elsevier B.V. All rights reserved.Publication Embargo 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.Publication Open Access Blockchain-Based KYC Model for Credit Allocation in Banking(IEEE-Inst Electrical Electronics Engineers Inc., 2024) Karadağ, Bulut; Zaim, A. Halim; AKBULUT, AKHANThe implementation of the Know Your Customer (KYC) strategy by banks within the financial sector enhances the operational efficiency of such establishments. The data gathered from the client during the KYC procedure may be applied to deter possible fraudulent activities, money laundering, and other criminal undertakings. The majority of financial institutions implement their own KYC procedures. Furthermore, a centralized system permits collaboration and operation execution by multiple financial institutions. Aside from these two scenarios, KYC processes can also be executed via a blockchain-based system. The blockchain's decentralized network would be highly transparent, facilitating the validation and verification of customer data in real-time for all relevant stakeholders. In addition, the immutability and cryptography of the blockchain ensure that client information is secure and immutable, thereby eradicating the risk of data breaches. Blockchain-based KYC can further improve the client experience by eliminating the requirement for redundant paperwork and document submissions. After banks grant consumers loans, a blockchain-based KYC system is proposed in this study to collect limit, risk, and collateral information from them. The approach built upon Ethereum grants financial institutions the ability to read and write financial data on the blockchain network. This KYC method establishes a transparent, dynamic, and expeditious framework among financial institutions. In addition, solutions are discussed for the Sybil attack, one of the most severe problems in such networks.Publication Open Access Boosting the Visibility of Services in Microservice Architecture(Springer, 2023) TOKMAK, AHMET VEDAT; AKBULUT, AKHAN; Çatal, ÇağatayMonolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice's Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.Publication Embargo CAVE Sanal Gerçeklik Teknolojisinin Üniversite-Sanayi İşbirliği Açısından Değerlendirilmesi ve Örnek bir Durum Çalışması(2019-03) Çatal, Çağatay; AKBULUT, AKHAN; 108363; 116056CAVE (CAVE Automatic Virtual Environment) sanal gerçeklik teknoloji altyapısı, yurt dışındaki üniversiteler ve araştırma kurumları tarafından, son dönemde farklı fonlarla kurulmaya başlanmış ancak Türkiye’de henüz bir üniversitede, yüksek yatırım maliyeti nedeniyle bu tür bir altyapı kurulamamıştır. Bu çalışmada bu tür bir merkezin kurulması durumunda hem üniversitelerin hem Türkiye’deki sanayinin ayrı ayrı kazanımları değerlendirilerek oluşabilecek sinerji ortaya konulmuştur. Bu merkezin belirli zaman dilimlerinde diğer eğitim kurumlarının kullanımına açılması, eğitime farklı bir boyut getirerek, anlaşılması güç olan kavramların, görsel ve üç boyutlu sanal gerçeklik ortamında kolaylıkla anlaşılması sağlanabilecektir. Eğitime sunulacak katkılarının yanı sıra, endüstriyel boyutta henüz ürünlerin ilk prototipleri yapılmadan, üç boyutlu modellerinin CAVE ortamına taşınarak ergonomi ve kullanıcı deneyim testlerinin yapılması mümkün olacaktır. Bu yönüyle, altyapının üzerinden ilgili kurum ve kuruluşlara ulaşılması, yeni AR-GE projelerinin geliştirilmesi sağlanabilecektir. Çalışmada sunulan örnek durum senaryosunda 130 metrekarelik bir alanda kurulabilecek bir CAVE altyapısı tanıtılmış olup gerekecek materyaller ve ekipmanlar aktarılmış, bu teknolojinin ayrıntılı değerlendirilmesi farklı boyutlarıyla ortaya konulmuştur. Bu tür bir merkez kurmak isteyen üniversiteler ve şirketler için Mantıksal Çerçeve Matrisi sunulmuştur.Publication Metadata only 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; 176006In 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.Publication Metadata only Comparative Evaluation of Different Classification Techniques for Masquerade Attack Detection(Inderscience Enterprises Ltd., 2020) Elmasry, Wisam; AKBULUT, AKHAN; Zaim, Abdul HalimMasquerade 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.Publication Metadata only Computer aided autism threapy system design(IEEE, 345 E 47th St, New York, Ny 10017 USA, 2015) AKBULUT, AKHAN; 116056Autism spectrum disorder is a developmental disorder such as Asperger's or Rett Syndrome, which damages social interaction and contact with the environment of individuals that prevents brain development. In Turkey, 600,000 cases of autism spectrum disorder is known and one-third of this population is estimated as the children in the age range of 0-14. Specials therapies with private trainers are used for the treatment in specific institutions. Within the scope of this project computer-assisted therapies will be developed as a part of this special therapies. The proposed system offers various autism therapy trainings through the displays and patient interactions will be transferred via sensors of Kinect for Windows device to the computers. The system accelerates the stages of education of autistic children's and will support their education. The aim of the project is finish autistic children's education as fun as a playing game without tightening and help to accelerate their learning processes with revealing supplementary materials to family members and education foundations. With this software written for Kinect on Windows technology, a set consisting of a majority of autistic children's education will be created. This set can be easily provided and used by both schools teaching and both families. The previous studies are variety of games that may interest children's attention.Publication Metadata only Control in Networked Systems With Fuzzy Logic(Tubitak Scientific & Technical Research Council Turkey, Ataturk Bulvarı No 221, Kavaklıdere, Ankara, 00000, Turkey, 2013) Öztaş, Oğuzhan; AKBULUT, AKHAN; 116056; 105191Recently, the development of control systems based on network-based architecture is getting very high attention. Because of its fast data communication, network-based control is in high demand. However, there are some disadvantages, such as delays, data packet dropouts, and communication constraints. Network-based control systems are getting attention in the development of this architecture because of these disadvantages. Optimization of the system is supplied to improve the existing structure. The optimization is categorized into 2 main structures: software optimization and hardware optimization. The structure of the overall system is designed with these optimization strategies. A hierarchical and communicational structure has emerged as a result of constructing the overall structure. After the optimization and design processes, the emerged system is simulated to implement and test the structure. Sensors and actuators are implemented as programs in different computers. Controllers are set into different computers and an observer program is set into another computer to observe the overall system. The components of the system are set in a sequential structure and give the expected performance due to their environment.Publication Metadata only Cyberbullying Detection Through Deep Learning: A Case Study of Turkish Celebrities on Twitter(IOS Press, 2023) Karadağ, Bulut; AKBULUT, AKHAN; Zaim, Abdul HalimOne of the ways that celebs maintain their fame in the modern era is by posting updates and photos to social media platforms like Twitter, Instagram, and Facebook. Comments left on their posts, however, expose them to cyberbullying. Cyberbullying, as a form of electronic device-based harassment, negatively impacts the lives of individuals. Thirty famous people from the fields of acting, art, music, politics, sports, and writing were chosen for this research. These notable figures include the top five Twitter followers of Turkey in each demographic. Between December 2019 and December 2020, comment responses for each celebrity were collated. Using the Deep Learning model, we were able to detect abuse content with an accuracy of 89%. Additionally, the percentage of celebrities exposed to cyberbullying by group was presented.Publication Metadata only 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; 8693Phantom 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.Publication 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; 8693In 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.Publication Open Access Deep Learning-Based Defect Prediction for Mobile Applications(MPDI, 2022) JORAYEVA, MANZURA; AKBULUT, AKHAN; Çatal, Çağatay; Mishra, AlokSmartphones 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.Publication Open Access Design and Implementation of a Deep Learning-Empowered m-Health Application(Springer, 2023) AKBULUT, AKHAN; DESOUKI, SARA; ABDELKHALIQ, SARA; KHANTOMANI, LAYAL; Çatal, ÇağatayMany people are unaware of the severity of melanoma disease even though such a disease can be fatal if not treated early. This research aims to facilitate the diagnosis of melanoma disease in people using a mobile health application because some people do not prefer to visit a dermatologist due to several concerns such as feeling uncomfortable by exposing their bodies. As such, a skincare application was developed so that a user can easily analyze a mole at any part of the body and get the diagnosis results quickly. In the first phase, the corresponding image is extracted and sent to a web service. Later, the web service classifies using the pre-trained model built based on a deep learning algorithm. The final phase displays the confidence rates on the mobile application. The proposed model utilizes the Convolutional Neural Network and provides 84% accuracy and 72% precision. The results demonstrate that the proposed model and the corresponding mobile application provide remarkable results for addressing the specified health problem.Publication Open 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 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.Publication Metadata only Development of a software vulnerability prediction web service based on artificial neural networks(2017) Çatal, Çağatay; 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.Publication Metadata only Empirical study on multiclass classification-based network intrusion detection(WILEY, 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019-11) Elmasry, Wisam; Zaim, Abdul Halim; AKBULUT, AKHANEarly and effective network intrusion detection is deemed to be a critical basis for cybersecurity domain. In the past decade, although a significant amount of work has focused on network intrusion detection, it is still a challenge to establish an intrusion detection system with a high detection rate and a relatively low false alarm rate. In this paper, we have performed a comprehensive empirical study on network intrusion detection as a multiclass classification task, not just to detect a suspicious connection but also to assign the correct type as well. To surpass the previous studies, we have utilized four deep learning models, namely, deep neural networks, long short-term memory recurrent neural networks, gated recurrent unit recurrent neural networks, and deep belief networks. Our approach relies on the pretraining of the models by exploiting a particle swarm optimization-based algorithm for their hyperparameters selection. In order to investigate the performance differences, we also included two well-known shallow learning methods, namely, decision forest and decision jungle. Furthermore, we used in our experiments four datasets, which are dedicated to intrusion detection systems to explore various environments. These datasets are KDD CUP 99, NSL-KDD, CIDDS, and CICIDS2017. Moreover, 22 evaluation metrics are used to assess the model's performance in each of the datasets. Finally, intensive quantitative, Friedman test, and ranking methods analyses of our results are provided at the end of this paper. The results show a significant improvement in the detection of network attacks with our recommended approach.