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    Collaboration Graph as a New Graph Definition Approach
    (Ieee, 2017) Ince, Kenan; Karci, Ali
    Modelling is a crucial step for analyzing the data. Graph is an important modelling technique for some areas especially if the data has some kind of relation between each other like complex networks. There are plenty of study in complex network area which uses graphs as a modelling tool. Collaboration networks are a kind of complex evolving networks. Also community detection and evaluation is an important topic in graph mining. Especially in recent years, the importance of social networks is increased and mining of these networks became more vital. However, there is no specific topic about collaboration graph which focus on how to evaluate the how strong a bond is and meaning of it. This study aims to propose a definition which named collaboration graph as a graph type for understanding structure of the network more clearly and less noisy.
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    Collaboration Network Analysis of Turkey in Regional Basis
    (Ieee, 2017) Ince, Kenan; Karci, Ali
    Modeling of data is an important step in process of interpreting the data and to understand the desired situation more clearly. The topic of social network structures is one of the highly studied subject and modeling is very important for social network mining. One of the modeling tools for such structures is Graphs. Graphs have been used for modeling and visualization tool of many structures such that computer networks, social networks, data structures and parallel programming topologies, etc. In this study, our data is scientific publications of Turkey and the goal is to find the cliques of academic collaboration network of Turkey at regional base via BK (Bron-Kerbosch) algorithm. By finding cliques, we aim to explore how relations are established between universities of Turkey and to reveal prominent university in each region.
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    Exploring the potential of deep learning and machine learning techniques for randomness analysis to enhance security on IoT
    (Springer, 2024) Ince, Kenan
    The Internet of Things (IoT) is an incredibly growing technology. However, due to hardware inadequacy, IoT security is not improving to the same extent. For this reason, lightweight encryption algorithms have begun to be developed. This paper presents a method for assessing the security of Pseudorandom Number Generator (PRNG) generated binary sequences in a reasonable time using a pre-trained deep learning (DL) model. Due to their long execution times, Randomness Test Standards (RTSs) that include statistical tests that examine whether the sequences generated by PRNGs contain any patterns that cause cryptographic vulnerabilities are not suitable for running on edge devices with low processing capacities such as the IoT. We argue that every random sequence, even generated by a PRNGs that are classified as cryptographically secure, utilized in cryptographic applications should be used after successful results obtained from RTSs in every time. Therefore, an alternative method based on machine learning has been proposed to overcome the processing time problem of these test suites. The most utilized RTSs are NIST 800-22 Rev.1a, GB/T 32915-2016 and AIS 20/31. The 800-22 Rev.1a, which NIST has designated as a standard, has been observed to be the most referenced test standard in the literature. With this implementation, we sought to show that 15 statistical tests of the NIST 800-22 rev.1a environment can be modeled using DL. The application findings indicate that this modeling can serve as an alternative to the existing test environments. The average accuracy recorded throughout 15 tests was 98.64 percent. As a result, the trained model can be implemented even in edge computing devices with limited capability including IoTs.
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    Modelling Academic Collaboration Network of Turkey as Graph and Dominating Set Analysis
    (Ieee, 2019) Kaygusuz, Muberra; Ince, Kenan
    Social network analysis; it is the work of deduction for a specific purpose from a interaction between different actors with dynamic and complex structure. The dominating set problem is to determine the dominant cluster with the minimum number of nodes in a network. In a social network, the criteria of centrality are important for the identification of the dominant node or nodes. Because the high centrality value of a node in the network increases the likelihood of being included in the dominant cluster. In this study, firstly, the data to be used in the analysis are modeled as graphs. By taking into consideration the positions of each node in the academic collaboration network, centrality values are calculated which provide information about the position of node in the network. The centrality criteria used in the study are degree centrality, closeness centrality, betweenness centrality and eigenvector centrality. The dominant nodes in the network were determined on the basis of universities by using the criteria of centrality.
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    Novel image pixel scrambling technique for efficient color image encryption in resource-constrained IoT devices
    (Springer, 2024) Ince, Cemile; Ince, Kenan; Hanbay, Davut
    In the digital age, where data is a valuable commodity, securing sensitive information has become a growing concern. Image encryption techniques play an essential role in protecting visual data from unauthorized access and ensuring privacy. However, with limited computing capacity in Internet of Things (IoT) devices, standard encryption algorithms are not feasible, rendering lightweight methods mandatory. This study proposes a novel Corner Traversal algorithm, an alternative to existing pixel scrambling techniques. The proposed algorithm demonstrably outperforms its counterparts in both higher confusion and lower time complexity, making it remarkably efficient. Integrated with chaos-based diffusion methods, this algorithm forms a comprehensive encryption scheme. The proposed lightweight image encryption scheme utilizing the Corner Traversal algorithm successfully passed rigorous statistical and differential security analysis. Compared to similar schemes, the proposed encryption scheme employing the Corner Traversal algorithm in the confusion phase distinguishes itself through exceptional NPCR (99.6093 for Lenna) and UACI (33.4648 for Lenna) values. Combined with other evaluation criteria, this method demonstrably meets the stringent security requirements of IoT systems.
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    Parallel Implementation of Genetic Algorithms on Multi-Core PCs
    (Turgut Ozal Univ, 2012) Ince, Kenan; Karci, Ali
    The goal of this study is reducing the time complexity of complex processes by using parallel programming on multi core PCs which is widely used in our daily computers. In this regard, same level as a result of the acceptance can be obtained in a shorter operating time by parallel implementation of an algorithm. Because of the increasing number of cores in processors with each passing day, we made sure that the application is scalable. This study show, if we implement the same problem parallel, the significant increase in performance occurs by increasing the number of cores. We implemented genetic algorithm to optimize Rastrigin function using Intel TBB. Our tests are made in two different computers. First computer contains the processors of Intel i3 M370@2.4 GHZ which has two core and each core has HT (hyper threading [1]) running on Microsoft Windows Seven operating system. Second computer contains the processors of Intel i5 520M@2.4 GHZ, which also has two cores and each core has HT as previous processor, running on Apple Mac OSX Lion operating system. In this study, not only the comparison of the serial and parallel versions of algorithm in the same platform is observed but also the differences between two different platforms are observed due to the operating time.