Yazar "Simsek, Mehmet" seçeneğine göre listele

Listeleniyor 1 - 6 / 6
  • Küçük Resim
    Öğe
    Combined centrality measures for an improved characterization of influence spread in social networks
    (Oxford Univ Press, 2020) Simsek, Mehmet; Meyerhenke, Henning
    Influence Maximization (IM) aims at finding the most influential users in a social network, that is, users who maximize the spread of an opinion within a certain propagation model. Previous work investigated the correlation between influence spread and nodal centrality measures to bypass more expensive IM simulations. The results were promising but incomplete, since these studies investigated the performance (i.e. the ability to identify influential users) of centrality measures only in restricted settings, for example, in undirected/unweighted networks and/or within a propagation model less common for IM. In this article, we first show that good results within the Susceptible-Infected-Removed propagation model for unweighted and undirected networks do not necessarily transfer to directed or weighted networks under the popular Independent Cascade (IC) propagation model. Then, we identify a set of centrality measures with good performance for weighted and directed networks within the IC model. Our main contribution is a new way to combine the centrality measures in a closed formula to yield even better results. Additionally, we also extend gravitational centrality (GC) with the proposed combined centrality measures. Our experiments on 50 real-world data sets show that our proposed centrality measures outperform well-known centrality measures and the state-of-the art GC measure significantly.
  • Küçük Resim Yok
    Öğe
    Improving Qos in mobile multimedia streaming with SCTP-PQ
    (Czech Technical Univ Prague, 2023) Huseynli, Alisettar; Simsek, Mehmet; Akcayol, M. Ali
    The Stream Control Transmission Protocol (SCTP) is often the preferred transport layer protocol in streaming applications. This protocol combines the best aspects of Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), but also offers additional features. SCTP supports multihoming and multi-streaming applications and has a congestion mechanism like TCP. Media streaming consists of different types of frames with different levels of importance. For example, I-frames carry more information than B-frames in Moving Picture Experts Group (MPEG). Usually, MPEG frames are processed using the First-In-First-Out (FIFO) algorithm. In this paper, a four-level priority queue integrated protocol named SCTP Priority Queue (SCTP-PQ) has been developed to reduce jitter and delay in real-time multimedia streaming for mobile devices. As part of the development, priority and retransmitted packets are determined on the sending side and these packets are processed faster by using the priority queue on the receiving side. In this way, the average queue delay of priority packets on the receiving side is reduced by 90 % and the throughput values are increased by an average of 10 times. The developed protocol has been extensively tested and compared with SCTP. The results show that the SCTP-PQ outperforms the standard SCTP in terms of jitter and delay.
  • Küçük Resim
    Öğe
    Nineteen-year retrospective evaluation of pemphigus in a single dermatology centre in Istanbul, Turkey
    (Termedia Publishing House Ltd, 2020) Kavala, Mukaddes; Zindanci, Ilkin; Turkoglu, Zafer; Kuru, Burce Can; Ozlu, Emin; Simsek, Mehmet
    Introduction: Pemphigus is an autoimmune intra-epidermal bullous disease of the skin and mucosae. Aim: To retrospectively evaluate the course, prognosis and clinical features of pemphigus. Material and methods: The files of 196 pemphigus patients admitted to our clinic between December 1995 and December 2014 were collected and analysed. Results: The male to female ratio among patients was 1 : 1.88. Pemphigus vulgaris (PV) was the most common clinical variant observed in 175 (89.3%) of the patients, followed by pemphigus foliaceus (PF) in 14 (7.1%) of the patients. The mean patient age at disease onset was 50 years. PV presented itself as skin lesions in 55 (31.4%) of the patients and as oral mucosa lesions in 120 (68.6%) of the patients. Complete remission and treatment withdrawal were obtained in 112 (57.1%) of the patients, for a mean period of 2.91 +/- 2.66 years (range: 4 months to 13 years). The mortality rate was 6%, and relapse occurred in 16 (14.3%) of the patients for a mean relapse period of 2.15 +/- 1.88 years (range: 6 months to 7 years). Mucocutaneous pemphigus (MCP) was the major clinical pattern observed in 96 (49%) of the patients. Conclusions: Within our study population, pemphigus predominately affected females, and the most common clinical variant was PV, a subtype that frequently occurs in middle-aged individuals. MCP was the most common clinical pattern. Although MCP and higher doses of corticosteroids were needed to control pemphigus, they did not seem to influence the prognosis.
  • Küçük Resim Yok
    Öğe
    Predicting athletic performance from physiological parameters using machine learning: Example of bocce ball
    (Ios Press, 2022) Simsek, Mehmet; Kesilmis, Inci
    Machine learning (ML) is an emerging topic in Sports Science. Some pioneering studies have applied machine learning to prevent injuries, to predict star players, and to analyze athletic performance. The limited number of studies in the literature focused on predicting athletic performance have adopted the cluster-then-predict classification approach. However, these studies have used the independent variable to represent athletic performance at both the clustering and classification stages. In this study we used only physiological parameters in the classification of bocce athletes. Their performance classes were predicted with high accuracy, thus contributing new findings to the literature. The support vector machines-radial basis function (SVM-RBF) kernel correctly predicted all athletes from the high-performance bocce player (HPBP) cluster and 75% of the athletes in the low-performance bocce player (LPBP) cluster. Using machine learning to predict athletic performance from balance data was found to be a time-saving approach for selecting high-potential bocce athletes.
  • Küçük Resim Yok
    Öğe
    Predicting athletic performance from physiological parameters using machine learning: Example of bocce ball
    (Ios Press, 2022) Simsek, Mehmet; Kesilmis, Inci
    Machine learning (ML) is an emerging topic in Sports Science. Some pioneering studies have applied machine learning to prevent injuries, to predict star players, and to analyze athletic performance. The limited number of studies in the literature focused on predicting athletic performance have adopted the cluster-then-predict classification approach. However, these studies have used the independent variable to represent athletic performance at both the clustering and classification stages. In this study we used only physiological parameters in the classification of bocce athletes. Their performance classes were predicted with high accuracy, thus contributing new findings to the literature. The support vector machines-radial basis function (SVM-RBF) kernel correctly predicted all athletes from the high-performance bocce player (HPBP) cluster and 75% of the athletes in the low-performance bocce player (LPBP) cluster. Using machine learning to predict athletic performance from balance data was found to be a time-saving approach for selecting high-potential bocce athletes.
  • Küçük Resim
    Öğe
    Semantic analysis on social networks: A survey
    (Wiley, 2020) Bayrakdar, Sumeyye; Yucedag, Ibrahim; Simsek, Mehmet; Dogru, Ibrahim Alper
    As social networks are getting more and more popular day by day, large numbers of users becoming constantly active social network users. In this way, there is a huge amount of data produced by users in social networks. While social networking sites and dynamic applications of these sites are actively used by people, social network analysis is also receiving an increasing interest. Moreover, semantic understanding of text, image, and video shared in a social network has been a significant topic in the network analysis research. To the best of the author's knowledge, there has not been any comprehensive survey of social networks, including semantic analysis. In this survey, we have reviewed over 200 contributions in the field, most of which appeared in recent years. This paper not only aims to provide a comprehensive survey of the research and application of social network analysis based on semantic analysis but also summarizes the state-of-the-art techniques for analyzing social media data. First of all, in this paper, social networks, basic concepts, and components related to social network analysis were examined. Second, semantic analysis methods for text, image, and video in social networks are explained, and various studies about these topics are examined in the literature. Then, the emerging approaches in social network analysis research, especially in semantic social network analysis, are discussed. Finally, the trending topics and applications for future directions of the research are emphasized; the information on what kind of studies may be realized in this area is given.