Yazar "Cakmak, Muhammet" seçeneğine göre listele

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    AFCC-r: Adaptive Feedback Congestion Control Algorithm to Avoid Queue Overflow in LTE Networks
    (Springer, 2022) Cakmak, Muhammet; Albayrak, Zafer
    With the dramatic increase in the number of users and the widespread use of smartphones, most of the internet content today is provided by cellular connections. The purpose of many active queue management algorithms developed for the cellular Long-Term Evolution network is to prevent forced packet drops in the Evolved Node B (eNodeB) Radio Link Control buffer and to improve delay and end-to-end throughput values. Although the algorithms developed in the literature improve some of the end-to-end throughput, delay, and packet data fraction values during bottleneck and congestion, they cannot balance these values. The proposed virtual queue management algorithm recalculates the average queue value and the packet dropping probability according to different traffic loads to solve the queue delay and queue overflow problem providing a balance between throughput, delay, and packet data fraction. Simulation results illustrate that the proposed algorithm reduces the delay of the packets and increases fairness among users compared to the Drop-tail, Random Early Drop, Controlled Delay, Proportional Integral Controller Enhanced, and Packet Limited First In First Out Queue algorithms.
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    Performance Comparison of Queue Management Algorithms in LTE Networks using NS-3 Simulator
    (Univ Osijek, Tech Fac, 2021) Cakmak, Muhammet; Albayrak, Zafer; Torun, Cumhur
    One of the most important issues accepted by researchers in LTE cellular systems is to develop Queue Management Algorithms for RLC (Radio Link Control). The performance of queue-management algorithms depends on parameters such as latency, packet dropping, and bandwidth usage. Simulation software is used to evaluate the queue-management algorithms developed and to test their performance. In the literature, active queue management algorithms have been compared with wired and wireless networks. In contrast to prior works, in this study, we have analyzed active queue management algorithms using the LTE model in the NS-3 network simulator. When the data and the results obtained from the simulations have been evaluated, it is concluded that the RED algorithm using probabilistic methods and the threshold value is more successful than the other algorithms in LTE networks.
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    Performance of Ad-Hoc Networks Using Smart Technology Under DDoS Attacks
    (Springer International Publishing Ag, 2022) Said, Aden Ali; Cakmak, Muhammet; Albayrak, Zafer
    The networks used in many areas such as location-based services, robotics, smart building assessment, smart water management, smart mobile learning, medical image analysis and processing, wearable technologies have to deal with various security problems. Active queue management algorithms are used to manage network resources and solve problems in the network. DDoS attacks prevent the effective use of network resources and cause network services to be disrupted or dropout. In this study, we classify the performance of queue management algorithms such as RED, SRED, BLUE, SFB, REM and CoDel under DDoS attacks according to delay, throughput, jitter, fairness index values. As a result of the comparison, thanks to the flexible structure of the CoDel algorithm, it gives better results in terms of packet loss and fairness index value under DDoS attacks.
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    Security Classification of Smart Devices Connected to LTE Network
    (Springer International Publishing Ag, 2022) Ali, Samatar Mohamed; Cakmak, Muhammet; Albayrak, Zafer
    Today, cellular wireless communication has been widely used in many intelligent automation systems, embedded technologies, robotic smart building, climate monitoring, e-learning, decision support systems, wearable devices for e-health, image, video, and speech processing. The Long-Term Evolution (LTE) network, which is a cellular wireless network technology, is one of the most important parts of the spread of smart systems. Attacks on IP-based LTE networks cause all smart systems to be affected. Attacks and security issues on the LTE network cause the network to slow down or be completely disabled. It also prevents users from receiving the desired Quality of Service (QoS) service. Thus, it cannot serve all smart systems using the LTE network. In this study, problems such as DoS, DDoS, mobile botnet, signalling amplification attacks, network access issues, and IMS security Issues that can be encountered in the cellular LTE network are classified.
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    Sex Prediction of Hyoid Bone from Computed Tomography Images Using the DenseNet121 Deep Learning Model
    (Soc Chilena Anatomia, 2024) Bakici, Rukiye Sumeyye; Cakmak, Muhammet; Oner, Zulal; Oner, Serkan
    The study aims to demonstrate the success of deep learning methods in sex prediction using hyoid bone. The images of people aged 15-94 years who underwent neck Computed Tomography (CT) were retrospectively scanned in the study. The neck CT images of the individuals were cleaned using the RadiAnt DICOM Viewer (version 2023.1) program, leaving only the hyoid bone. A total of 7 images in the anterior, posterior, superior, inferior, right, left, and right-anterior-upward directions were obtained from a patient's cut hyoid bone image. 2170 images were obtained from 310 hyoid bones of males, and 1820 images from 260 hyoid bones of females. 3990 images were completed to 5000 images by data enrichment. The dataset was divided into 80 % for training, 10 % for testing, and another 10 % for validation. It was compared with deep learning models DenseNet121, ResNet152, and VGG19. An accuracy rate of 87 % was achieved in the ResNet152 model and 80.2 % in the VGG19 model. The highest rate among the classified models was 89 % in the DenseNet121 model. This model had a specificity of 0.87, a sensitivity of 0.90, an F1 score of 0.89 in women, a specificity of 0.90, a sensitivity of 0.87, and an F1 score of 0.88 in men. It was observed that sex could be predicted from the hyoid bone using deep learning methods DenseNet121, ResNet152, and VGG19. Thus, a method that had not been tried on this bone before was used. This study also brings us one step closer to strengthening and perfecting the use of technologies, which will reduce the subjectivity of the methods and support the expert in the decision-making process of sex prediction.