https://hdl.handle.net/20.500.12573/45 Fri, 08 May 2026 08:24:46 GMT 2026-05-08T08:24:46Z https://hdl.handle.net/20.500.12573/2239 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. Sun, 01 Jan 2023 00:00:00 GMT https://hdl.handle.net/20.500.12573/2239 2023-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2214 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. Wed, 01 Jan 2014 00:00:00 GMT https://hdl.handle.net/20.500.12573/2214 2014-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/2186 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. Fri, 01 Jan 2016 00:00:00 GMT https://hdl.handle.net/20.500.12573/2186 2016-01-01T00:00:00Z https://hdl.handle.net/20.500.12573/1572 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 variables are generated using Genetic Algorithms (GA). Each partition in the variables corresponds to a clustering center in the normal mixture model. The best model that fits the data structure from normal mixture models is obtained by using the information criteria obtained from normal mixture distributions. Wed, 01 Jan 2020 00:00:00 GMT https://hdl.handle.net/20.500.12573/1572 2020-01-01T00:00:00Z