Browsing by Author "Keleştimur, Haluk"

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A New Method Based on Local Binary Gaussian Pattern for Classification of Rat Estrous Cycle Stages Using Smear Images
(Elsevier Sci Ltd, 2025) Serhatlioglu, Ihsan; Kilic, Irfan; Yaman, Orhan; Kacar, Emine; Oz, Zeynep Dila; Ozdede, Mehmet Ridvan; Kelestimur, Haluk
In this study, a unique dataset was created by classifying the images of vaginal smears taken from rats under a microscope for 4 different cycles. Classifying a new case image with the help of this dataset is a computer vision problem. In this study, to improve the weaknesses of the LBP algorithm, a new feature extraction method called Local Binary Gaussian Pattern (LBGP) is developed based on the Gaussian matrix, which helps to remove noise in images. Local Binary Gaussian Pattern proposes a Gaussian-like filter inspired by the Gaussian matrix. After converting the smearing image to the gray histogram, the image features obtained with the help of the Local Binary Pattern (LBP) and our proposed Local Binary Gaussian Pattern (LBGP) feature extractor are combined to obtain features that we call hybrid features. From these features, the ones above a certain threshold value are selected with the help of Neighborhood Component Analysis (NCA), and a Hybrid + Neighborhood Component Analysis (NCA) approach is presented. All hybrid features and hybrid features reduced by Neighborhood Component Analysis were trained with Support Vector Machine (SVM), Decision Trees (DT), Naive Bayes (NB), and k-nearest Neighbors (k-NN) classifiers. According to the classification results, it is seen that the Support Vector Machine (SVM) is effectively classified with the trained classifier. With the Support Vector Machine (SVM) classifier, a success rate of over 90 % (90.25 %) was achieved. Considering the difficulty of classifying smearing images, this result is promising for the future stages of this study.
A Suitable Way of Normalizing New Si To Make c and h Unities
(Turkic World Mathematical Soc, 2024) Yarman, Ozan; Yarman, Tolga
Yarman's Approach, which serves as the basis of YARK gravitation theory (as abbreviated from "Yarman-Arik-Kholmetskii"), together with its recently developed extension QTG (Quantal Theory of Gravity), motivated us to question the suitability of Natural Units commonly used in Quantum Field Theory (QFT) and other areas of physics. That and the consensus of the General Conference on Weights and Measures (CGPM) towards the establishment of "New SI" inspired us to explore an appropriate way of normalizing the metric system in order to make the utmost theoretical speed limit of light c and the Planck Constant h unities, as well as universal constants, respectively. Our metrological approach herein reveals that the correction factor k introduced to the retired definition of vacuum permeability mu(0) - as extracted from an indiscriminate Fine-Structure Constant alpha value - does not suffice to align the computed alpha with the latest experimental measurements of alpha. One may therefore require a rectified value for the elementary charge e along with the need to restore its uncertainty digits. All this is especially relevant within the context of the 20 May 2019 international decision to fix the Planck Constant to a definite value while letting the kilogram vary instead. One thus remarkably ends up with the necessity to either restore the uncertainty parts of the elementary charge in contrast to the SI redefinition, or to recalculate the correction factor k that latterly appears in vacuum permeability, or both. Another far-reaching option is the idea of restituting the uncertainties for the Planck Constant and/or lightspeed too when SI is normalized and then re-normalized without disturbing the meaningfulness of the related physical dimensions.