Browsing by Author "Saran, Nurdan Ayse"

Now showing 1 - 3 of 3

Citation - WoS: 3
Citation - Scopus: 7
A survey on server-based electronic identification and signature schemes to improve eIDAS: with a new proposal for Turkey
(Peerj inc, 2021) Erdogan, Ozgun; Saran, Nurdan Ayse; 20868
The design, development, and implementation of e-Government applications aim to improve the quality of daily life and facilitate the mobility of citizens by reducing the constraints imposed by existing borders. This study examines previous research in the literature on electronic identification (eID) credentials technologies and the projects carried out in Europe. This study focuses especially on server-based e-signing methods. In the light of these reviews, the applicability of a server-based mobile electronic signature model without disrupting local initiatives has been examined as a case study. As an exemplary case, Turkey's eID structure is examined from a technical and legal perspective. When creating the proposed server-based eID model, it was especially inspired by Austria's server-based approach in use. In this process, the suitability of the existing structure with the server-based e-signing method was examined. In addition, some suggestions were made to eliminate the problems that may prevent the use of the proposed server-based e-signing method. This study revealed that a server-based electronic signature approach would develop a more user-friendly and flexible solution in identity management. It was concluded that using a server-based signature approach would help achieve international standards for cross-border online identification methods.
Citation - WoS: 3
Citation - Scopus: 5
Distribution-preserving data augmentation
(Peerj inc, 2021) Saran, Murat; Saran, Nurdan Ayse; Saran, Murat; Nar, Fatih; Nar, Fatih; 20868; 17753; Bilgisayar Mühendisliği; Matematik
In the last decade, deep learning has been applied in a wide range of problems with tremendous success. This success mainly comes from large data availability, increased computational power, and theoretical improvements in the training phase. As the dataset grows, the real world is better represented, making it possible to develop a model that can generalize. However, creating a labeled dataset is expensive, time-consuming, and sometimes not likely in some domains if not challenging. Therefore, researchers proposed data augmentation methods to increase dataset size and variety by creating variations of the existing data. For image data, variations can be obtained by applying color or spatial transformations, only one or a combination. Such color transformations perform some linear or nonlinear operations in the entire image or in the patches to create variations of the original image. The current color-based augmentation methods are usually based on image processing methods that apply color transformations such as equalizing, solarizing, and posterizing. Nevertheless, these color-based data augmentation methods do not guarantee to create plausible variations of the image. This paper proposes a novel distribution-preserving data augmentation method that creates plausible image variations by shifting pixel colors to another point in the image color distribution. We achieved this by defining a regularized density decreasing direction to create paths from the original pixels' color to the distribution tails. The proposed method provides superior performance compared to existing data augmentation methods which is shown using a transfer learning scenario on the UC Merced Land-use, Intel Image Classification, and Oxford-IIIT Pet datasets for classification and segmentation tasks.
Citation - WoS: 0
Citation - Scopus: 0
Fast Binary Logistic Regression
(Peerj inc, 2025) Saran, Nurdan Ayse; Nar, Fatih
This study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus approximation, which enables reformulation of logistic regression parameter estimation into matrix-vector form. We also adopt the L-f-norm penalty, which allows using fractional norms, including the L-2-norm, L-1-norm, and L-0-norm, to regularize the model parameters. We put L-f-norm formulation in matrix-vector form, providing flexibility to include or exclude penalization of the intercept term when applying regularization. Furthermore, to address the common problem of collinear features, we apply singular value decomposition (SVD), resulting in a low-rank representation commonly used to reduce computational complexity while preserving essential features and mitigating noise. Moreover, our approach incorporates a randomized SVD alongside a newly developed SVD with row reduction (SVD-RR) method, which aims to manage datasets with many rows and features efficiently. This computational efficiency is crucial in developing a generalized model that requires repeated training over various parameters to balance bias and variance. We also demonstrate the effectiveness of our fast binary logistic regression (FBLR) method on various datasets from the OpenML repository in addition to synthetic datasets.