Person: AKBULUT, AKHAN
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AKBULUT
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AKHAN
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Publication Survey on Access Control Mechanisms in Cloud Computing(River Publishers, 2018-05-26) AKBULUT, AKHAN; BAYDOĞMUŞ, GÖZDE KARATAŞ; 110942; 116056The benefits that Internet-based applications and services have given to the end user with today’s cloud computing technology are very remarkable. The distributed services instantly scaled over the Internet provided by cloud computing can be achieved by using some mechanisms in the background. It is a critical task for end users to control access to resources because lack of control often leads to security risks. In addition, this may cause systems to fail. This paper describes seven different access control mechanisms used in cloud computing platforms for different purposes. Besides, the advantages and disadvantages of various models developed from previous service-based architectures and used for cloud computing are detailed and classified. During the assessments, NIST’s metrics were taken as a reference, and in the study, 109 articles from the past decade were examined. We also compared our research with the existing survey papers.Publication Special issue: soft computing in software engineering preface(Elsevier Science Bv, Po Box 211, 1000 AE Amsterdam, Netherlands, 2016-12) Çatal, Çağatay; Bayrak, Coşkun; Nassif, Ali Bou; Polat, Kemal; AKBULUT, AKHAN; 108363; 6194; 37249; 116056Publication 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, ÇağatayPhantom 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.Publication 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 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.Publication Techniques 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.Publication 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 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 Stacking-Based Ensemble Learning for Remaining Useful Life Estimation(Springer, 2023) Türe, Begüm Ay; AKBULUT, AKHAN; Zaim, Abdul Halim; Çatal, ÇağatayExcessive 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%.Publication Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review(MDPI, 2022) JORAYEVA, MANZURA; AKBULUT, AKHAN; Çatal, Çağatay; Mishra, AlokSoftware defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing. Therefore, we carried out a Systematic Literature Review (SLR) study to evaluate how machine learning has been applied to predict faults in mobile applications. This study defined nine research questions, and 47 relevant studies were selected from scientific databases to respond to these research questions. Results show that most studies focused on Android applications (i.e., 48%), supervised machine learning has been applied in most studies (i.e., 92%), and object-oriented metrics were mainly preferred. The top five most preferred machine learning algorithms are Naive Bayes, Support Vector Machines, Logistic Regression, Artificial Neural Networks, and Decision Trees. Researchers mostly preferred Object-Oriented metrics. Only a few studies applied deep learning algorithms including Long Short-Term Memory (LSTM), Deep Belief Networks (DBN), and Deep Neural Networks (DNN). This is the first study that systematically reviews software defect prediction research focused on mobile applications. It will pave the way for further research in mobile software fault prediction and help both researchers and practitioners in this field.Publication Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic(2020-02-26) Elmasry, W.; Zaim, A.H; AKBULUT, AKHANThe prevention of intrusion is deemed to be a cornerstone of network security. Although excessive work has been introduced on network intrusion detection in the last decade, finding an Intrusion Detection Systems (IDS) with potent intrusion detection mechanism is still highly desirable. One of the leading causes of the high number of false alarms and a low detection rate is the existence of redundant and irrelevant features of the datasets, which are used to train the IDSs. To cope with this problem, we proposed a double Particle Swarm Optimization (PSO)-based algorithm to select both feature subset and hyperparameters in one process. The aforementioned algorithm is exploited in the pre-training phase for selecting the optimized features and model's hyperparameters automatically. In order to investigate the performance differences, we utilized three deep learning models, namely, Deep Neural Networks (DNN), Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), and Deep Belief Networks (DBN). Furthermore, we used two common IDS datasets in our experiments to validate our approach and show the effectiveness of the developed models. Moreover, many evaluation metrics are used for both binary and multiclass classifications 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. Experimental results show a significant improvement in network intrusion detection when using our approach by increasing Detection Rate (DR) by 4% to 6% and reducing False Alarm Rate (FAR) by 1% to 5% from the corresponding values of same models without pre-training on the same dataset. © 2019