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Article Citation - WoS: 5Citation - Scopus: 7China's Charm Defensive: Image Protection by Acquiring Mass Entertainment(Wiley, 2020) Yildirim, Nilgun Elikucuk; Aslan, MesutFocusing on discussion of China's soft power resources, this article argues that China performs two kinds of soft power strategies in developing and developed countries: offensive and defensive, respectively. While China's charm offensive aims to consolidate her comprehensive power through a development model, aid, investment, traditional culture, foreign policy, and international broadcasting in developing countries, the defensive aspect of China's soft power strategy aims to soften the rise of China with traditional culture by introducing appealing parts of Chinese culture through investments and international broadcasting in Western countries. China applies classical soft power tools in developing countries while she endeavors to protect her image in Western countries defensively. China's alternative defensive approach to soft power is mostly implemented through the acquisition of media outlets, and via the entertainment sector and gaming industry by Chinese-owned companies. However, even in the defensive and offensive bifurcation, if charm attacks result in failure, China could turn take a defensive stance in developing countries.Article Citation - WoS: 11Citation - Scopus: 20Reinforcement Learning Using Fully Connected, Attention, and Transformer Models in Knapsack Problem Solving(Wiley, 2022) Yildiz, Beytullah; Yıldız, Beytullah; Yıldız, BeytullahKnapsack is a combinatorial optimization problem that involves a variety of resource allocation challenges. It is defined as non-deterministic polynomial time (NP) hard and has a wide range of applications. Knapsack problem (KP) has been studied in applied mathematics and computer science for decades. Many algorithms that can be classified as exact or approximate solutions have been proposed. Under the category of exact solutions, algorithms such as branch-and-bound and dynamic programming and the approaches obtained by combining these algorithms can be classified. Due to the fact that exact solutions require a long processing time, many approximate methods have been introduced for knapsack solution. In this research, deep Q-learning using models containing fully connected layers, attention, and transformer as function estimators were used to provide the solution for KP. We observed that deep Q-networks, which continued their training by observing the reward signals provided by the knapsack environment we developed, optimized the total reward gained over time. The results showed that our approaches give near-optimum solutions and work about 40 times faster than an exact algorithm using dynamic programming.Article Citation - WoS: 1Citation - Scopus: 1Left Ventricular Geometry as a Predictor of Carotid Artery Stenosis Severity in Patients Undergoing Carotid Artery Stenting(Wiley, 2020) Karaduman, Bilge Duran; Ayhan, Huseyin; Keles, Telat; Bozkurt, EnginBackground and Aim Cerebrovascular diseases are the second most common cause of death worldwide. Moderate and severe carotid artery stenosis causes nearly 10% of all strokes. LV geometry is a familiar prognostic and diagnostic factor in several populations; yet, data on its role in carotid artery stenosis are unknown. In our study, we investigated the prognostic value of LV geometry in predicting carotid artery stenosis severity in patients undergoing carotid artery stenting. Methods Patients who underwent carotid artery stenting between January 2012 and January 2016 at our tertiary care center were evaluated retrospectively. Two hundred fifty-five patients who underwent carotid artery stenting were included in the study. Accessible echocardiographic documentation of ninety-eight patients was accessed and evaluated. Results LV normal geometry was detected in 37 (37.7%) of the 98 carotid artery stenting (CAS) patients, concentric hypertrophy in 13 (13.2%), eccentric hypertrophy in 9 (9.1%), and concentric remodeling in 39 (39.7%). By a majority, distal filter was used in normal geometry and eccentric hypertrophy groups (82.9% vs 100%, P: .017). Considering the relationship between carotid artery stenosis severity and LV geometry, we determined that the stenosis severity was statistically significantly higher in the concentric hypertrophy group (p:0.012). However, although no complications were detected in the concentric hypertrophy group, it did not reach statistical significance between the groups (P: .058). LVMi and as expected, Doppler velocity showed a significant correlation with stenosis severity (r = .23 vs .54; P: .021, <.001, respectively). Conclusion Echocardiographic evaluation of LV geometry provided prognostic information in the development of carotid artery stenosis. Abnormal LV geometry is an independent predictor in detecting the severity of carotid artery stenosis undergoing carotid artery stenting.Article Citation - WoS: 6Citation - Scopus: 4Is real per capita state personal income stationary? New nonlinear, asymmetric panel-data evidence(Wiley, 2020) Emirmahmutoglu, Furkan; Gupta, Rangan; Miller, Stephen M.; Omay, TolgaThis paper re-examines the stochastic properties of U.S. state real per capita personal income, using new panel unit-root procedures. The new developments incorporate non-linearity, asymmetry, and cross-sectional correlation within panel-data estimation. Including nonlinearity and asymmetry finds that 43 states exhibit stationary real per capita personal income whereas including only nonlinearity produces 42 states that exhibit stationarity. Stated differently, we find that two states exhibit nonstationary real per capita personal income when considering nonlinearity, asymmetry, and cross-sectional dependence.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.Article Citation - WoS: 8Citation - Scopus: 15Distributed denial-of-service attack mitigation in network functions virtualization-based 5G networks using management and orchestration(Wiley, 2021) Koksal, Sarp; Dalveren, Yaser; Maiga, Bamoye; Kara, AliThe fifth generation (5G) technology is expected to allow connectivity to billions of devices, known as Internet of Things (IoT). However, IoT devices will inevitably be the main target of various cyberattack types. The most common one is known as distributed denial-of-service (DDoS) attack. In order to mitigate such attacks, network functions virtualization (NFV) has a great potential to provide the benefit of elasticity and low-cost solutions for protecting 5G networks. In this context, this study proposes a new mechanism developed to mitigate DDoS attacks in 5G NFV networks. The proposed mechanism utilizes intrusion prevention system's (IPS) virtual machines (VMs) to intercept the queries. Based on the volume of DDoS traffic, IPS's VMs are dynamically deployed by means of management and orchestration (MANO) in order to balance the load. To evaluate the effectiveness of the mechanism, experiments are conducted in a real 5G NFV environment built by using 5G NFV environment tools. To our best knowledge, this is the first time that NFV-based mechanism is experimentally tested in a real 5G NFV environment for mitigating DDoS attacks in 5G networks. The experimental results verify that the proposed mechanism can mitigate DDoS attacks effectively.Article Validating the Turkish Adaptation of the Fear of Being Single Scale(Wiley, 2024) Kirimer-Aydinli, Fulya; Kucukkomurler, SanemPeople may experience anxiety regarding their future romantic relationship status. Fear of being single (FOBS) is a potential cause of this anxiety, characterized by distress about the idea of being single and assessed through the FOBS Scale. In the current study, the FOBS Scale was adapted into Turkish. The study included 349 individuals aged 28-55 years (M = 23.63 years, SD = 6.45 years). The reliability and validity of the measure and the associations with particular variables were investigated for the first time in the cultural context of Turkey. The confirmatory factor analysis revealed an acceptable model fit for the single-factor structure. Measurement invariance of the scale across relationship status was supported at configural and metric levels, but not at the scalar level. The convergent and divergent validity analyses indicated that FOBS is distinct from generalized anxiety, attachment anxiety, and the personality trait of neuroticism. FOBS was found to be related to the need to belong but not to the inclusion of close others into the self. It has been determined that FOBS is a distinctive phenomenon, and the Turkish version of the FOBS Scale is a valid and reliable tool for assessing FOBS in Turkey.Article Citation - WoS: 30Citation - Scopus: 44Text Classification Using Improved Bidirectional Transformer(Wiley, 2022) Tezgider, Murat; Yıldız, Beytullah; Yildiz, Beytullah; Aydin, Galip; Yıldız, BeytullahText data have an important place in our daily life. A huge amount of text data is generated everyday. As a result, automation becomes necessary to handle these large text data. Recently, we are witnessing important developments with the adaptation of new approaches in text processing. Attention mechanisms and transformers are emerging as methods with significant potential for text processing. In this study, we introduced a bidirectional transformer (BiTransformer) constructed using two transformer encoder blocks that utilize bidirectional position encoding to take into account the forward and backward position information of text data. We also created models to evaluate the contribution of attention mechanisms to the classification process. Four models, including long short term memory, attention, transformer, and BiTransformer, were used to conduct experiments on a large Turkish text dataset consisting of 30 categories. The effect of using pretrained embedding on models was also investigated. Experimental results show that the classification models using transformer and attention give promising results compared with classical deep learning models. We observed that the BiTransformer we proposed showed superior performance in text classification.Article Citation - WoS: 3Citation - Scopus: 5Particle 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, MuratBackground 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.Article Citation - WoS: 17Citation - Scopus: 26Toward Ontology-Based Risk Management Framework for Software Projects: an Empirical Study(Wiley, 2020) Abioye, Temitope Elizabeth; Arogundade, Oluwasefunmi Tale; Misra, Sanjay; Akinwale, Adio T.; Adeniran, Olusola JohnSoftware risk management is a proactive decision-making practice with processes, methods, and tools for managing risks in a software project. Many existing techniques for software project risk management are textual documentation with varying perspectives that are nonreusable and cannot be shared. In this paper, a life-cycle approach to ontology-based risk management framework for software projects is presented. A dataset from literature, domain experts, and practitioners is used. The identified risks are refined by 19 software experts; risks are conceptualized, modeled, and developed using Protege. The risks are qualitatively analyzed and prioritized, and aversion methods are provided. The framework is adopted in real-life software projects. Precision recall and F-measure metrics are used to validate the performance of the extraction tool while performance and perception evaluation are carried out using the performance appraisal form and technology acceptance model, respectively. Mean scores from performance and perception evaluation are compared with evaluation concept scale. Results showed that cost is reduced, high-quality projects are delivered on time, and software developers found this framework a potent tool needed for their day-to-day activities in software development.
