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Article Citation - WoS: 16Citation - Scopus: 25An Intelligent Fuzzy Object-Oriented Database Framework for Video Database Applications(Elsevier, 2009) Ozgur, Nezihe Burcu; Koyuncu, Murat; Yazici, AdnanVideo database applications call for flexible and powerful modeling and querying facilities, which require an integration or interaction between database and knowledge-based technologies. It is also necessary for many real life video database applications to incorporate uncertainty, which naturally occurs due to the complex and subjective semantic content of video data. In this study, firstly, we introduce a fuzzy conceptual data model to represent the semantic content of video data. For that purpose, UML (unified modeling language) is utilized and extended to represent uncertain information along with video specific properties. Secondly, we present an intelligent fuzzy object-oriented database framework for video database applications. The introduced fuzzy conceptual model is used in this framework, which provides modeling of complex and rich semantic content and knowledge of video data including uncertainty. Moreover, it supports various flexible queries including (fuzzy) semantic, temporal and (fuzzy) spatial queries, based on the video data model. We think that the presented conceptual data model and the framework can be used for any video database application. (C) 2009 Elsevier B.V. All rights reserved.Article Sağlık Sektöründe Yapay Zeka Kullanımı(2024) Koyuncu, Murat; Göçer, SafiyeArticle Citation - WoS: 26Citation - Scopus: 35Security Awareness Level of Smartphone Users: an Exploratory Case Study(Hindawi Ltd, 2019) Koyuncu, Murat; Pusatli, TolgaAs smartphone technology becomes more and more mature, its usage extends beyond and covers also applications that require security. However, since smartphones can contain valuable information, they normally become the target of attackers. A physically lost or a hacked smartphone may cause catastrophic results for its owner. To prevent such undesired events, smartphone users should be aware of existing threats and countermeasures to be taken against them. Therefore, user awareness is a critical factor for smartphone security. This study investigates the awareness level of smartphone users for different security-related parameters and compares the awareness levels of different user groups categorized according to their demographic data. It is based on a survey study conducted on a population with a different range of age, education level, and IT security expertise. According to the obtained results, in general, the awareness level of participants is fairly low, which needs considerable improvement. In terms of age, the oldest group has the lowest level followed by the youngest group. Education level, in general, has a positive effect on the awareness level. Having knowledge about IT is another factor increasing the security awareness level of smartphone users.Article Citation - WoS: 5Citation - Scopus: 10A Deep Neural Network-Based Advisory Framework for Attainment of Sustainable Development Goals 1-6(Mdpi, 2020) Emmanuel, Okewu; Ananya, M.; Misra, Sanjay; Koyuncu, MuratResearch in sustainable development, program design and monitoring, and evaluation requires data analytics for the Sustainable Developments Goals (SDGs) not to suffer the same fate as the Millennium Development Goals (MDGs). The MDGs were poorly implemented, particularly in developing countries. In the SDGs dispensation, there is a huge amount of development-related data that needs to be harnessed using predictive analytics models such as deep neural networks for timely and unbiased information. The SDGs aim at improving the lives of citizens globally. However, the first six SDGs (SDGs 1-6) are more relevant to developing economies than developed economies. This is because low-resourced countries are still battling with extreme poverty and unacceptable levels of illiteracy occasioned by corruption and poor leadership. Inclusive innovation is a philosophy of SDGs as no one should be left behind in the global economy. The focus of this study is the implementation of SDGs 1-6 in less developed countries. Given their peculiar socio-economic challenges, we proposed a design for a low-budget deep neural network-based sustainable development goals 1-6 (DNNSDGs 1-6) system. The aim is to empower actors implementing SDGs in developing countries with data-based information for robust decision making.Article Citation - WoS: 24Citation - Scopus: 40An Inheritance Complexity Metric for Object-Oriented Code: a Cognitive Approach(Springer india, 2011) Misra, Sanjay; Akman, Ibrahim; Koyuncu, MuratSoftware metrics should be used in order to improve the productivity and quality of software, because they provide critical information about reliability and maintainability of the system. In this paper, we propose a cognitive complexity metric for evaluating design of object-oriented (OO) code. The proposed metric is based on an important feature of the OO systems: Inheritance. It calculates the complexity at method level considering internal structure of methods, and also considers inheritance to calculate the complexity of class hierarchies. The proposed metric is validated both theoretically and empirically. For theoretical validation, principles of measurement theory are applied since the measurement theory has been proposed and extensively used in the literature as a means to evaluate the software engineering metrics. We applied our metric on a real project for empirical validation and compared it with Chidamber and Kemerer (CK) metrics suite. The theoretical, practical and empirical validations and the comparative study prove the robustness of the measure.Article Fnbdt/scıp Protokolünün Yerel Alan Ağında Uygulanması ve Sınır Değerlerin Tespit Edilmesi(2010) Dilli, Orkun; Akçam, Nursel; Koyuncu, MuratTeknolojideki hızlı değişim her alanda olduğu gibi haberleşme sistemlerinde de yaşanmaktadır. Geliştirilen farklı sistemler veya cihazlar zaman kaybedilmeden kullanıma sunulmaktadır. Söz konusu değişim genelde olumlu olmakla birlikte bazen olumsuz sonuçlara da yol açabilmektedir. Bu olumsuz sonuçlardan bir tanesi, ISDN, PSTN ve IP tabanlı haberleşme cihazlarının birbirleriyle uçtan uca güvenli olarak haberleşme yapamamasıdır. Bu çalışmada, farklı haberleşme ağları üzerinde uçtan uca emniyetli haberleşmenin yapılması amacıyla geliştirilen FNBDT (Future Narrow Band Digital Terminal)/SCIP (Secure Communication Interoperability Protocol) protokolü, emulatörler vasıtasıyla IP ağ üzerinde değişik açılardan test edilmiştir. Bu tür çalışmaların yapılmasının FNBDT/SCIP protokolünün gelişimine katkıda bulunmak açısından büyük önem arz etmekte olduğu ve sunulan çalışmanın bu alanda gerçekleştirilmiş ilk çalışmalardan birisi olması nedeniyle de gelecekte konuyla ilgili yapılacak çalışmalara katkı sağlayacağı düşünülmektedir.Article Citation - WoS: 3Citation - Scopus: 4Intelligent Fuzzy Queries for Multimedia Databases(Wiley, 2011) Koyuncu, MuratMultimedia databases have emerged to cope up with the huge amount of multimedia data, which comes up as a result of technological advancement. However, more intelligent techniques are required to satisfy different query requirements of multimedia users. This study extends the query capability of a multimedia database through the integration of a fuzzy rule-based system. In addition to fuzzy semantic rules, which deduce new information from the data stored in the database, fuzzy spatial and temporal relations, which are inherent to multimedia applications, are defined in the rule-based system. Users can formulate fuzzy semantic, spatial, temporal, and spatiotemporal queries, resulting in the deduction of new information using the rules defined in the rule-based system. With some practical examples, the paper presents how a fuzzy rule-based system integrated to a fuzzy multimedia database improves the query capabilities of the database system intelligently. (C) 2011 Wiley Periodicals, Inc.Article Citation - WoS: 30Citation - Scopus: 48A Fusion-Based Framework for Wireless Multimedia Sensor Networks in Surveillance Applications(Ieee-inst Electrical Electronics Engineers inc, 2019) Yazici, Adnan; Koyuncu, Murat; Sert, Seyyit Alper; Yilmaz, TurgayMultimedia sensors enable monitoring applications to obtain more accurate and detailed information. However, the development of efficient and lightweight solutions for managing data traffic over wireless multimedia sensor networks (WMSNs) has become vital because of the excessive volume of data produced by multimedia sensors. As part of this motivation, this paper proposes a fusion-based WMSN framework that reduces the amount of data to be transmitted over the network by intra-node processing. This framework explores three main issues: 1) the design of a wireless multimedia sensor (WMS) node to detect objects using machine learning techniques; 2) a method for increasing the accuracy while reducing the amount of information transmitted by the WMS nodes to the base station, and; 3) a new cluster-based routing algorithm for the WMSNs that consumes less power than the currently used algorithms. In this context, a WMS node is designed and implemented using commercially available components. In order to reduce the amount of information to be transmitted to the base station and thereby extend the lifetime of a WMSN, a method for detecting and classifying objects on three different layers has been developed. A new energy-efficient cluster-based routing algorithm is developed to transfer the collected information/data to the sink. The proposed framework and the cluster-based routing algorithm are applied to our WMS nodes and tested experimentally. The results of the experiments clearly demonstrate the feasibility of the proposed WMSN architecture in the real-world surveillance applications.Review Citation - WoS: 7Citation - Scopus: 9A Survey of Covid-19 Diagnosis Using Routine Blood Tests With the Aid of Artificial Intelligence Techniques(Mdpi, 2023) Habashi, Soheila Abbasi; Koyuncu, Murat; Alizadehsani, RoohallahSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.Article Citation - Scopus: 1Optimizing the Stochastic Deployment of Small Base Stations in an Interleave Division Multiple Access-Based Heterogeneous Cellular Networks(Wiley, 2022) Noma-Osaghae, Etinosa; Misra, Sanjay; Koyuncu, MuratThe use of small base stations (SBSs) to improve the throughput of cellular networks gave rise to the advent of heterogeneous cellular networks (HCNs). Still, the interleave division multiple access (IDMA) performance in sleep mode active HCNs has not been studied in the existing literature. This research examines the 24-h throughput, spectral efficiency (SE), and energy efficiency (EE) of an IDMA-based HCN and compares the result with orthogonal frequency division multiple access (OFDMA). An energy-spectral-efficiency (ESE) model of a two-tier HCN was developed. A weighted sum modified particle swarm optimization (PSO) algorithm simultaneously maximized the SE and EE of the IDMA-based HCN. The result obtained showed that the IDMA performs at least 68% better than the OFDMA on the throughput metric. The result also showed that the particle swarm optimization algorithm produced the Pareto optimal front at moderate traffic levels for all varied network parameters of SINR threshold, SBS density, and sleep mode technique. The IDMA-based HCN can improve the throughput, SE, and EE via sleep mode techniques. Still, the combination of network parameters that simultaneously maximize the SE and EE is interference limited. In sleep mode, the performance of the HCN is better if the SBSs can adapt to spatial and temporal variations in network traffic.
