22 sonuçlar
Arama Sonuçları
Listeleniyor 1 - 10 / 22
Yayın Sınıflandırma için diferansiyel mahremiyete dayalı öznitelik seçimi(Gazi Univ, Fac Engineering Architecture, 2018) Var, Esra; İnan, AliVeri madenciliği ve makine öğrenmesi çözümlerinin en önemli ön aşamalarından biri yapılacak analizde kullanılacak verinin özniteliklerinin uygun bir alt kümesini belirlemektir. Sınıflandırma yöntemleri için bu işlem, bir özniteliğin sınıf niteliği ile ne oranda ilişkili olduğuna bakılarak yapılır. Kişisel gizliliği koruyan pek çok sınıflandırma çözümü bulunmaktadır. Ancak bu yöntemler için öznitelik seçimi yapan çözümler geliştirilmemiştir. Bu çalışmada, istatistiksel veritabanı güvenliğinde bilinen en kapsamlı ve güvenli çözüm olan diferansiyel mahremiyete dayalı özgün öznitelik seçimi yöntemleri sunulmaktadır. Önerilen bu yöntemler, yaygın olarak kullanılan bir veri madenciliği kütüphanesi olan WEKA ile entegre edilmiş ve deney sonuçları ile önerilen çözümlerin sınıflandırma başarımına olumlu etkileri gösterilmiştir.Yayın Comment on "Modified Baptista type chaotic cryptosystem via matrix secret key" [Phys. Lett. A 372 (2008) 5427](Elsevier Science BV, 2009-09-07) Rhouma, Rhouma; Solak, Ercan; Arroyo, David; Li, Shujun; Alvarez, Gonzalo; Belghith, Safya MdimeghIn this comment, we analyze a recently proposed Baptista-like cryptosystem and show that it is not invertible. Others weaknesses are also reported. A modified version of this cryptosystem is proposed to show how to overcome the non-invertibility.Yayın Cryptanalysis of a multi-chaotic systems based image cryptosystem(Elsevier Science BV, 2010-01-15) Solak, Ercan; Rhouma, Rhouma; Belghith, Safya MdimeghThis paper is a cryptanalysis of a recently proposed multi-chaotic systems based image cryptosystem. The cryptosystem is composed of two shuffling stages parameterized by chaotically generated sequences. We propose and implement two different attacks which completely break this encryption scheme.Yayın k-Means clustering by using the calculated Z-scores from QEEG data of children with dyslexia(Taylor & Francis, 2023) Eroğlu, Günet; Arman, FehimLearning the subtype of dyslexia may help shorten the rehabilitation process and focus more on the relevant special education or diet for children with dyslexia. For this purpose, the resting-state eyes-open 2-min QEEG measurement data were collected from 112 children with dyslexia (84 male, 28 female) between 7 and 11 years old for 96 sessions per subject on average. The z-scores are calculated for each band power and each channel, and outliers are eliminated afterward. Using the k-Means clustering method, three different clusters are identified. Cluster 1 (19% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 2 (76% of the cases) has negative z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers in all channels. Cluster 3 (5% of the cases) has positive z-scores for theta, alpha, beta-1, beta-2, and gamma-band powers at AF3, F3, FC5, and T7 channels and mostly negative z-scores for other channels. In Cluster 3, there is temporal disruption which is a typical description of dyslexia. In Cluster 1, there is a general brain inflammation as both slow and fast waves are detected in the same channels. In Cluster 2, there is a brain maturation delay and a mild inflammation. After Auto Train Brain training, most of the cases resemble more of Cluster 2, which may mean that inflammation is reduced and brain maturation delay comes up to the surface which might be the result of inflammation. Moreover, Cluster 2 center values at the posterior parts of the brain shift toward the mean values at these channels after 60 sessions. It means, Auto Train Brain training improves the posterior parts of the brain for children with dyslexia, which were the most relevant regions to be strengthened for dyslexia.Yayın Cryptanalysis of a cryptosystem based on discretized two-dimensional chaotic maps(Elsevier Science BV, 2008-11-17) Solak, Ercan; Çokal, CahitRecently, an encryption algorithm based on two-dimensional discretized chaotic maps was proposed [Xiang et al., Phys. Lett. A 364 (2007) 252]. In this Letter, we analyze the security weaknesses of the proposal. Using the algebraic dependencies among system parameters. we show that its effective key space can be shrunk. We demonstrate a chosen-ciphertext attack that reveals a portion of the key.Yayın Design and analysis of classifier learning experiments in bioinformatics: survey and case studies(IEEE Computer Soc, 2012-12) İrsoy, Ozan; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim EthemIn many bioinformatics applications, it is important to assess and compare the performances of algorithms trained from data, to be able to draw conclusions unaffected by chance and are therefore significant. Both the design of such experiments and the analysis of the resulting data using statistical tests should be done carefully for the results to carry significance. In this paper, we first review the performance measures used in classification, the basics of experiment design and statistical tests. We then give the results of our survey over 1,500 papers published in the last two years in three bioinformatics journals (including this one). Although the basics of experiment design are well understood, such as resampling instead of using a single training set and the use of different performance metrics instead of error, only 21 percent of the papers use any statistical test for comparison. In the third part, we analyze four different scenarios which we encounter frequently in the bioinformatics literature, discussing the proper statistical methodology as well as showing an example case study for each. With the supplementary software, we hope that the guidelines we discuss will play an important role in future studies.Yayın Hybrid high dimensional model representation (HHDMR) on the partitioned data(Elsevier B.V., 2006-01-01) Tunga, Mehmet Alper; Demiralp, MetinA multivariate interpolation problem is generally constructed for appropriate determination of a multivariate function whose values are given at a finite number of nodes of a multivariate grid. One way to construct the solution of this problem is to partition the given multivariate data into low-variate data. High dimensional model representation (HDMR) and generalized high dimensional model representation (GHDMR) methods are used to make this partitioning. Using the components of the HDMR or the GHDMR expansions the multivariate data can be partitioned. When a cartesian product set in the space of the independent variables is given, the HDMR expansion is used. On the other band, if the nodes are the elements of a random discrete data the GHDMR expansion is used instead of HDMR. These two expansions work well for the multivariate data that have the additive nature. If the data have multiplicative nature then factorized high dimensional model representation (FHDMR) is used. But in most cases the nature of the given multivariate data and the sought multivariate function have neither additive nor multiplicative nature. They have a hybrid nature. So, a new method is developed to obtain better results and it is called hybrid high dimensional model representation (HHDMR). This new expansion includes both the HDMR (or GHDMR) and the FHDMR expansions through a hybridity parameter. In this work, the general structure of this hybrid expansion is given. It has tried to obtain the best value for the hybridity parameter. According to this value the analytical structure of the sought multivariate function can be determined via HHDMR.Yayın Constructing a WordNet for Turkish using manual and automatic annotation(Assoc Computing Machinery, 2018-05) Ehsani, Razieh; Solak, Ercan; Yıldız, Olcay TanerIn this article, we summarize the methodology and the results of our 2-year-long efforts to construct a comprehensive WordNet for Turkish. In our approach, we mine a dictionary for synonym candidate pairs and manually mark the senses in which the candidates are synonymous. We marked every pair twice by different human annotators. We derive the synsets by finding the connected components of the graph whose edges are synonym senses. We also mined Turkish Wikipedia for hypernym relations among the senses. We analyzed the resulting WordNet to highlight the difficulties brought about by the dictionary construction methods of lexicographers. After splitting the unusually large synsets, we used random walk-based clustering that resulted in a Zipfian distribution of synset sizes. We compared our results to BalkaNet and automatic thesaurus construction methods using variation of information metric. Our Turkish WordNet is available online.Yayın On the maximum cardinality cut problem in proper interval graphs and related graph classes(Elsevier B.V., 2022-01-04) Boyacı, Arman; Ekim, Tınaz; Shalom, MordechaiAlthough it has been claimed in two different papers that the maximum cardinality cut problem is polynomial-time solvable for proper interval graphs, both of them turned out to be erroneous. In this work we consider the parameterized complexity of this problem. We show that the maximum cardinality cut problem in proper/unit interval graphs is FPT when parameterized by the maximum number of non-empty bubbles in a column of its bubble model. We then generalize this result to a more general graph class by defining new parameters related to the well-known clique-width parameter. Specifically, we define an (?,?,?)-clique-width decomposition of a graph as a clique-width decomposition in which at each step the following invariant is preserved: after discarding at most ? labels, a) every label consists of at most ? sets of twin vertices, and b) all the labels together induce a graph with independence number at most ?. We show that for every two constants ?,?>0 the problem is FPT when parameterized by ? plus the smallest width of an (?,?,?)-clique-width decomposition.Yayın Fully decentralized and collaborative multilateration primitives for uniquely localizing WSNs(Springer International Publishing AG, 2010) Çakıroğlu, Olca Arda; Erten, CesimWe provide primitives for uniquely localizing WSN nodes. The goal is to maximize the number of uniquely localized nodes assuming a fully decentralized model of computation. Each node constructs a cluster of its own and applies unique localization primitives on it. These primitives are based on constructing a special order for multilaterating the nodes within the cluster. The proposed primitives are fully collaborative and thus the number of iterations required to compute the localization is fewer than that of the conventional iterative multilateration approaches. This further limits the messaging requirements. With relatively small clusters and iteration counts, we can localize almost all the uniquely localizable nodes.
- «
- 1 (current)
- 2
- 3
- »
