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Citation - WoS: 33
Citation - Scopus: 41
Visual and Auditory Data Fusion for Energy-Efficient and Improved Object Recognition in Wireless Multimedia Sensor Networks
(Ieee-inst Electrical Electronics Engineers inc, 2019) Koyuncu, Murat; Yazici, Adnan; Civelek, Muhsin; Cosar, Ahmet; Sert, Mustafa
Automatic threat classification without human intervention is a popular research topic in wireless multimedia sensor networks (WMSNs) especially within the context of surveillance applications. This paper explores the effect of fusing audio-visual multimedia and scalar data collected by the sensor nodes in a WMSN for the purpose of energy-efficient and accurate object detection and classification. In order to do that, we implemented a wireless multimedia sensor node with video and audio capturing and processing capabilities in addition to traditional/ordinary scalar sensors. The multimedia sensors are kept in sleep mode in order to save energy until they are activated by the scalar sensors which are always active. The object recognition results obtained from video and audio applications are fused to increase the object recognition performance of the sensor node. Final results are forwarded to the sink in text format, and this greatly reduces the size of data transmitted in network. Performance test results of the implemented prototype system show that the fusing audio data with visual data improves automatic object recognition capability of a sensor node significantly. Since auditory data requires less processing power compared to visual data, the overhead of processing the auditory data is not high, and it helps to extend network lifetime of WMSNs.
A Model Proposal To Optimize Bandwidth Usage in Multi-Access Wireless Networks
(Univ Osijek, Tech Fac, 2012) Koyuncu, Murat; Gercek, Mehmet Kazim; Information Systems Engineering
Distribution of load among multiple access points, and therefore, optimizing the total throughput is one of the problems of wireless local area networks when there is more than one access point available in the network. It is not easy to balance the load when wireless hosts associate with one access point by using only the Received Signal Strength Indicator (RSSI). In this study, a new model which increases total throughput in a wireless local area network is proposed. The proposed model is mainly based on the prediction of the loads of all the available access points checking both their wireless and Ethernet interfaces and the association to the least loaded one. It is a host-based model and does not require any changes on the network infrastructure. The tests performed on the real wireless local area networks prove the applicability of the proposed model.
Citation - WoS: 7
Citation - Scopus: 9
A Survey of Covid-19 Diagnosis Using Routine Blood Tests With the Aid of Artificial Intelligence Techniques
(Mdpi, 2023) Habashi, Soheila Abbasi; Koyuncu, Murat; Alizadehsani, Roohallah
Severe 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.
Citation - Scopus: 1
Optimizing 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, Murat
The 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.
Citation - WoS: 12
Citation - Scopus: 12
A Polarity Calculation Approach for Lexicon-Based Turkish Sentiment Analysis
(Tubitak Scientific & Technological Research Council Turkey, 2019) Yurtalan, Gökhan; Koyuncu, Murat; Turhan, Çiğdem
Sentiment analysis attempts to resolve the senses or emotions that a writer or speaker intends to send across tothe people about an object or event. It generally uses natural language processing and/or artificial intelligence techniquesfor processing electronic documents and mining the opinion specified in the content. In recent years, researchers haveconducted many successful sentiment analysis studies for the English language which consider many words and wordgroups that set emotion polarities arising from the English grammar structure, and then use datasets to test theirperformance. However, there are only a limited number of studies for the Turkish language, and these studies have lowerperformance results compared to those studies for English. The reasons for this can be incorrect translation of datasetsfrom English into Turkish and ignoring the special grammar structures in the latter. In this study, special Turkish wordsand linguistic constructs which affect the polarity of a sentence are determined with the aid of a Turkish linguist, and anappropriate lexicon-based polarity determination and calculation approach is introduced for this language. The proposedmethodology is tested using different datasets collected from Twitter, and the test results show that the proposed systemachieves better accuracy than the previously developed lexical-based sentiment analysis systems for Turkish. The authorsconclude that especially analysis of word groups increases the overall performance of the system significantly.
Citation - WoS: 13
Citation - Scopus: 14
An Intelligent Multimedia Information System for Multimodal Content Extraction and Querying
(Springer, 2018) Yazici, Adnan; Koyuncu, Murat; Yilmaz, Turgay; Sattari, Saeid; Sert, Mustafa; Gulen, Elvan
This paper introduces an intelligent multimedia information system, which exploits machine learning and database technologies. The system extracts semantic contents of videos automatically by using the visual, auditory and textual modalities, then, stores the extracted contents in an appropriate format to retrieve them efficiently in subsequent requests for information. The semantic contents are extracted from these three modalities of data separately. Afterwards, the outputs from these modalities are fused to increase the accuracy of the object extraction process. The semantic contents that are extracted using the information fusion are stored in an intelligent and fuzzy object-oriented database system. In order to answer user queries efficiently, a multidimensional indexing mechanism that combines the extracted high-level semantic information with the low-level video features is developed. The proposed multimedia information system is implemented as a prototype and its performance is evaluated using news video datasets for answering content and concept-based queries considering all these modalities and their fused data. The performance results show that the developed multimedia information system is robust and scalable for large scale multimedia applications.
Citation - WoS: 3
Citation - Scopus: 5
Particle Swarm Optimization of the Spectral and Energy Efficiency of an Scma-Based Heterogeneous Cellular Network
(Wiley, 2022) Noma-Osaghae, Etinosa; Misra, Sanjay; Ahuja, Ravin; Koyuncu, Murat
Background The effect of stochastic small base station (SBS) deployment on the energy efficiency (EE) and spectral efficiency (SE) of sparse code multiple access (SCMA)-based heterogeneous cellular networks (HCNs) is still mostly unknown. Aim This research study seeks to provide insight into the interaction between SE and EE in SBS sleep-mode enabled SCMA-based HCNs. Methodology A model that characterizes the energy-spectral-efficiency (ESE) of a two-tier SBS sleep-mode enabled SCMA-based HCN was derived. A multiobjective optimization problem was formulated to maximize the SE and EE of the SCMA-based HCN simultaneously. The multiobjective optimization problem was solved using a proposed weighted sum modified particle swarm optimization algorithm (PSO). A comparison was made between the performance of the proposed weighted sum modified PSO algorithm and the genetic algorithm (GA) and the case where the SCMA-based HCN is unoptimized. Results The Pareto-optimal front generated showed a simultaneous maximization of the SE and EE of the SCMA-based HCN at high traffic levels and a convex front that allows network operators to select the SE-EE tradeoff at low traffic levels flexibly. The proposed PSO algorithm offers a higher SBS density, and a higher SBS transmit power at high traffic levels than at low traffic levels. The unoptimized SCMA-based HCN achieves an 80% lower SE and a 51% lower EE than the proposed PSO optimized SCMA-based HCN. The optimum SE and EE achieved by the SCMA-based HCN using the proposed PSO algorithm or the GA are comparable, but the proposed PSO uses a 51.85% lower SBS density and a 35.96% lower SBS transmit power to achieve the optimal SE and EE at moderate traffic levels. Conclusion In sleep-mode enabled SCMA-based HCNs, network engineers have to decide the balance of SBS density and SBS transmit power that helps achieve the desired SE and EE.
Citation - WoS: 9
Citation - Scopus: 11
Food Index: a Multidimensional Index Structure for Similarity-Based Fuzzy Object Oriented Database Models
(Ieee-inst Electrical Electronics Engineers inc, 2008) Yazici, Adnan; Ince, Cagri; Koyuncu, Murat
A fuzzy object-oriented data model is a fuzzy logic-based extension to an object-oriented database model that permits uncertain data to be explicitly represented. The fuzzy object-oriented database (FOOD) model is one of the proposed models in the literature to handle uncertainty in object-oriented databases. Several kinds of fuzziness are dealt with in the FOOD model, including fuzziness at attribute level and between object and class and between class and superclass relations. The traditional index structures do not allow efficient access to both crisp and fuzzy objects for fuzzy object-oriented databases since they are not efficient enough in processing both crisp and fuzzy queries. In this study, we propose a new index structure, namely a FOOD index (FI), to deal with different kinds of fuzziness in fuzzy object-oriented databases and to support multidimensional indexing. In this paper, we describe this proposed index structure and show how it supports various types of flexible queries, and evaluate its performance for exact, range, and fuzzy queries.
Citation - WoS: 30
Citation - Scopus: 48
A 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, Turgay
Multimedia 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.
Citation - WoS: 27
Citation - Scopus: 35
Security Awareness Level of Smartphone Users: an Exploratory Case Study
(Hindawi Ltd, 2019) Koyuncu, Murat; Pusatli, Tolga
As 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.

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