http://belgelik.isikun.edu.tr/xmlui/handleiubelgelik/7343 JAEM 2026, Vol 16, No 8 koleksiyonunu içerir. 2026-08-04T03:30:44Z http://belgelik.isikun.edu.tr/xmlui/handleiubelgelik/7351 Regularized logistic regression with the Atan in high dimensional data Yousif, Ali Hameed; Kadhum Mezher, Zainab; Ali Dhumad, Najlaa Logistic regression models play an important role in analyzing binary classification problems in medical and biological data. One common method for estimating the parameters of a logistic regression model is the maximum likelihood method. However, this method does not perform well in high-dimensional settings or in the presence of multicollinearity. To overcome these problems, a penalty term is added to the objective function. In this paper, we propose a method for parameter estimation and variable selection in logistic regression models using an L0 -like arctangent (Atan) regularization approach. The Atan penalty, which is based on the arctangent function, enjoys oracle properties. The performance of the regularized logistic regression model with the Atan penalty is compared with that of the fused lasso and the SELO penalty. Monte Carlo simulation studies are conducted under different sample sizes and different standard deviation settings. In addition, a real data set is used to evaluate the performance of the proposed method. The results show that the proposed estimator outperforms the competing methods (fused lasso and SELO) in terms of both estimation accuracy and variable selection. 2026-08-01T00:00:00Z http://belgelik.isikun.edu.tr/xmlui/handleiubelgelik/7350 Numerical approximation of ABC-type fractional equations using a Vieta-Lucas polynomial Noman, Ghadah Sultan Esmail; Pawar, Dnyaneshwar Dadaji This paper presents a spectral collocation method based on shifted VietaLucas polynomials (SVLPs) for the numerical approximation of multi-term variable-order fractional differential equations (MT-VOFDEs) involving the Atangana-Baleanu-Caputo (ABC) fractional derivative in variable-order form. A novel operational matrix of the ABC derivative is derived in the SVLP framework, enabling an efficient transformation of the fractional system into an algebraic system. The stability and convergence of the method are theoretically analyzed. The proposed SVLP-based approach is then applied to several test problems, including systems of MT-VOFDEs with known exact polynomial solutions. Numerical results demonstrate exponential convergence, high accuracy, and reduced computational cost compared to other polynomial-based collocation methods. The orthogonality and recursive structure of the SVLPs contribute to computational efficiency and robustness. These findings highlight the effectiveness of the proposed method for solving complex fractional models governed by non-singular kernel operators. 2026-08-01T00:00:00Z http://belgelik.isikun.edu.tr/xmlui/handleiubelgelik/7349 An adjacent edge graceful labeling of pentagonal snake graph Nivetha, G. N.; Raj, T. Tharma; Gowri, A. Let G be a graph with p vertices and q edges. The graph G is said to be an adjacent edge graceful graph if there exists bijection mapping f : E(G) → {1, 2, 3, ..., q} such that the induced vertex mapping f*: V (G) → N, where N is a natural number by f*(r) = Σk f(ek) taken over all edges ek incident to adjacent vertices of r is an injection. In this article we will go to prove the Pentagonal snake, Alternative Pentagonal Snake, Double Pentagonal snake and Alternative Double Pentagonal Snake are Adjacent Edge Graceful graph. 2026-08-01T00:00:00Z http://belgelik.isikun.edu.tr/xmlui/handleiubelgelik/7348 A computational analysis of the face index of certain graph networks using multivariate regression Sharathkumar, Hunjanalu Thimmarayappa; Narasimha, Pooja; Swamy, Narahari Narasimha; Nagesh, Hadonahally Mudalagiraiah Researchers have recently introduced a topological descriptor, called the face index, which quantifies the number of faces in a molecular network/compound to enhance efficiency by reducing computational time while improving the accuracy of chemical property calculations for chemical structures. It is observed that the face index provides valuable insights into the structural variations of different materials, offering a more generalized perspective compared to traditional vertex degree-based topological descriptors. This makes it a valuable tool for advancing research in chemistry and material science. Motivated by this, in the present study, we compute the face index for the chemical graphs of three significant networks namely the pentahexoctite, tetracyanobenzene and pyracyclene networks. Additionally, we perform a covariance analysis of this index with various degree-based topological indices for these networks. To further assess its predictability, we employ multivariate regression analysis, examining the relationship between the face index and these indices, which reveals a strong correlation between them. 2026-08-01T00:00:00Z