<?xml version="1.0" encoding="UTF-8"?><feed xmlns="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
<title>Bilgisayar Bilimleri Fakültesi/Faculty of Computer Sciences</title>
<link href="https://hdl.handle.net/20.500.12573/45" rel="alternate"/>
<subtitle/>
<id>https://hdl.handle.net/20.500.12573/45</id>
<updated>2026-05-08T11:45:23Z</updated>
<dc:date>2026-05-08T11:45:23Z</dc:date>
<entry>
<title>Positive solutions of multipoint φ-Laplacian BVPs with first-order derivative dependence</title>
<link href="https://hdl.handle.net/20.500.12573/2239" rel="alternate"/>
<author>
<name>Bachouche K.</name>
</author>
<author>
<name>Tair H.</name>
</author>
<author>
<name>DOĞAN, Abdülkadir</name>
</author>
<id>https://hdl.handle.net/20.500.12573/2239</id>
<updated>2024-07-03T11:50:11Z</updated>
<published>2023-01-01T00:00:00Z</published>
<summary type="text">Positive solutions of multipoint φ-Laplacian BVPs with first-order derivative dependence
Bachouche K.; Tair H.; DOĞAN, Abdülkadir
This paper concerns existence of positive solutions for a second-order boundary value problem of Sturm-Liouville type associated with a φ-Laplacian operator and posed on a bounded interval. Existence results are obtained by an adapted version of the Krasnosel'skii's fixed point theorem of cone expansion and compression. Some examples illustrate our results.
</summary>
<dc:date>2023-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Numerical solutions of the kawahara equation by the septic B-spline collocation method</title>
<link href="https://hdl.handle.net/20.500.12573/2214" rel="alternate"/>
<author>
<name>Karakoç, Battal Gazi</name>
</author>
<author>
<name>Zeybek, Halil</name>
</author>
<author>
<name>Ak, Turgut</name>
</author>
<id>https://hdl.handle.net/20.500.12573/2214</id>
<updated>2024-06-26T12:35:09Z</updated>
<published>2014-01-01T00:00:00Z</published>
<summary type="text">Numerical solutions of the kawahara equation by the septic B-spline collocation method
Karakoç, Battal Gazi; Zeybek, Halil; Ak, Turgut
In this article, a numerical solution of the Kawahara equation is presented by septic B-spline collocation method. Applying the Von-Neumann stability analysis, the present method is shown to be unconditionally stable. The accuracy of the proposed method is checked by two test problems. L2 and L∞ error norms and conserved quantities are given at selected times. The obtained results are found in good agreement with the some recent results.
</summary>
<dc:date>2014-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>A cubic B-spline Galerkin approach for the numerical simulation of the GEW equation</title>
<link href="https://hdl.handle.net/20.500.12573/2186" rel="alternate"/>
<author>
<name>Battal Gazi Karakoç S.</name>
</author>
<author>
<name>Zeybek, Halil</name>
</author>
<id>https://hdl.handle.net/20.500.12573/2186</id>
<updated>2024-06-06T10:52:11Z</updated>
<published>2016-01-01T00:00:00Z</published>
<summary type="text">A cubic B-spline Galerkin approach for the numerical simulation of the GEW equation
Battal Gazi Karakoç S.; Zeybek, Halil
The generalized equal width (GEW) wave equation is solved numerically by using lumped Galerkin approach with cubic B-spline functions. The proposed numerical scheme is tested by applying two test problems including single solitary wave and interaction of two solitary waves. In order to determine the performance of the algorithm, the error norms L2 and L∞ and the invariants I1, I2 and I3 are calculated. For the linear stability analysis of the numerical algorithm, von Neumann approach is used. As a result, the obtained findings show that the presented numerical scheme is preferable to some recent numerical methods.
</summary>
<dc:date>2016-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Normal Mixture Model-Based Clustering of Data Using Genetic Algorithm</title>
<link href="https://hdl.handle.net/20.500.12573/1572" rel="alternate"/>
<author>
<name>Gogebakan, Maruf</name>
</author>
<author>
<name>Erol, Hamza</name>
</author>
<id>https://hdl.handle.net/20.500.12573/1572</id>
<updated>2023-04-07T08:37:59Z</updated>
<published>2020-01-01T00:00:00Z</published>
<summary type="text">Normal Mixture Model-Based Clustering of Data Using Genetic Algorithm
Gogebakan, Maruf; Erol, Hamza
In this study, a new algorithm was developed for clustering multivariate big data. Normal mixture distributions are used to determine the partitions of variables. Normal mixture models obtained from the partitions of&#13;
variables are generated using Genetic Algorithms (GA). Each partition in the&#13;
variables corresponds to a clustering center in the normal mixture model. The&#13;
best model that fits the data structure from normal mixture models is obtained by&#13;
using the information criteria obtained from normal mixture distributions.
</summary>
<dc:date>2020-01-01T00:00:00Z</dc:date>
</entry>
</feed>
