WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Analysis of Power-Law Fin-Type Problems Using Physics Informed Neural Networks(Sciendo, 2025-12-01) Gocer, M.; Coskun, S. B.; Atay, M. T.This study aims to model the temperature distribution in a single fin subjected to steady one-dimensional heat conduction with nonlinear thermal behavior. For the modeling and solution of the problem, the Physics-Informed Neural Networks (PINNs) architecture was used. The temperature-dependent heat conduction problem and the nonlinear boundary conditions of this problem were formulated with a differential equation. With the help of the PINN architecture, the loss function was minimized in order to reduce the difference between the true value and the predicted value. During this minimization process, the PINN architecture was forced to be consistent with the physical laws. The results obtained after training the PINN architecture exhibit successful performance in terms of accuracy and reliability when compared with the results in the literature. These findings highlight the potential of PINNs as a powerful alternative to conventional methods for solving complex nonlinear heat conduction problems.Editorial What Does the Water Inside the Brain Tell Us? Diffusion Tensor Imaging(Sciendo, 2018-10-01) Acer, Niyazi; Dundar, Mehmet Sait; Bastepe-Gray, SerapThe brain consist of about 75 percent water. Diffusion tensor imaging (DTI) is an advanced magnetic resonance (MR) technique imaging that has been developed for diagnostic and research in medicine. It can be use DTI tractography to better understand degenerating axons of white matter lesions in some neurological diseases such as MS, AD, trauma, cerebral ischemia, epilepsy, brain tumors and metabolic disorders.Article Citation - WoS: 22Citation - Scopus: 26Suppression of Inflammatory Cytokines Expression With Bitter Melon (Momordica Charantia) in TNBS-Instigated Ulcerative Colitis(Sciendo, 2020-09-01) Semiz, Asli; Acar, Ozden Ozgun; Cetin, Hulya; Semiz, Gurkan; Sen, AlaattinBackground and Objective: This study was aimed to elucidate the molecular mechanism of Momordica charantia (MCh), along with a standard drug prednisolone, in a rat model of colitis induced by trinitrobenzene sulfonic acid (TNBS). Methods: After the induction of the experimental colitis, the animals were treated with MCh (4 g/kg/day) for 14 consecutive days by intragastric gavage. The colonic tissue expression levels of C-C motif chemokine ligand 17 (CCL-17), interleukin (IL)-1 beta, IL-6, IL-23, interferon-gamma (IFN-gamma), nuclear factor kappa B (NFkB), and tumor necrosis factor-alpha (TNF-alpha), were determined at both mRNA and protein levels to estimate the effect of MCh. Besides, colonic specimens were analyzed histopathologically after staining with hematoxylin and eosin. Results: The body weights from TNBS-instigated colitis rats were found to be significantly lower than untreated animals. Also, the IFN-gamma, IL-1 beta, IL-6, Il-23, TNF-alpha, CCL-17, and NF-kB mRNA and protein levels were increased significantly from 1.86-4.91-fold and 1.46-5.50-fold, respectively, in the TNBS-instigated colitis group as compared to the control. Both the MCh and prednisolone treatment significantly reduced the bodyweight loss. It also restored the induced colonic tissue levels of IL-1 beta, IL-6, IFN-gamma, and TNF-alpha to normal levels seen in untreated animals. These results were also supported with the histochemical staining of the colonic tissues from both control and treated animals. Conclusion: The presented data strongly suggests that MCh has the anti-inflammatory effect that might be modulated through vitamin D metabolism. It is the right candidate for the treatment of UC as an alternative and complementary therapeutics.Article Citation - WoS: 1Motion Artifact Detection in Colonoscopy Images(Sciendo, 2018-07-01) Kacmaz, Rukiye Nur; Yilmaz, Bulent; Dundar, Mehmet Sait; Dogan, SerkanComputer-aided detection is an integral part of medical image evaluation process because examination of each image takes a long time and generally experts' do not have enough time for the elimination of images with motion artifact (blurred images). Computer-aided detection is required for both increasing accuracy rate and saving experts' time. Large intestine does not have straight structure thus camera of the colonoscopy should be moved continuously to examine inside of the large intestine and this movement causes motion artifact on colonoscopy images. In this study, images were selected from open-source colonoscopy videos and obtained at Kayseri Training and Research Hospital. Totally 100 images were analyzed half of which were clear. Firstly, a modified version of histogram equalization was applied in the pre-processing step to all images in our dataset, and then, used Laplacian, wavelet transform (WT), and discrete cosine transform-based (DCT) approaches to extract features for the discrimination of images with no artifact (clear) and images with motion artifact. The Laplacian-based feature extraction method was used for the first time in the literature on colonoscopy images. The comparison between Laplacian-based features and previously used methods such as WT and DCT has been performed. In the classification phase of our study, support vector machines (SVM), linear discriminant analysis (LDA), and k nearest neighbors (k-NN) were used as the classifiers. The results showed that Laplacian-based features were more successful in the detection of images with motion artifact when compared to popular methods used in the literature. As a result, a combination of features extracted using already existing approaches (WT and DCT) and the Laplacian-based methods reached 85% accuracy levels with SVM classification approach.Article Citation - WoS: 2Citation - Scopus: 2Comparison of Deep Learning and Conventional Machine Learning Methods for Classification of Colon Polyp Types(Sciendo, 2021-01-01) Dogan, Refika Sultan; Yilmaz, BulentDetermination of polyp types requires tissue biopsy during colonoscopy and then histopathological examination of the microscopic images which tremendously time-consuming and costly. The first aim of this study was to design a computer-aided diagnosis system to classify polyp types using colonoscopy images (optical biopsy) without the need for tissue biopsy. For this purpose, two different approaches were designed based on conventional machine learning (ML) and deep learning. Firstly, classification was performed using random forest approach by means of the features obtained from the histogram of gradients descriptor. Secondly, simple convolutional neural networks (CNN) based architecture was built to train with the colonoscopy images containing colon polyps. The performances of these approaches on two (adenoma & serrated vs. hyperplastic) or three (adenoma vs. hyperplastic vs. serrated) category classifications were investigated. Furthermore, the effect of imaging modality on the classification was also examined using white-light and narrow band imaging systems. The performance of these approaches was compared with the results obtained by 3 novice and 4 expert doctors. Two-category classification results showed that conventional ML approach achieved significantly better than the simple CNN based approach did in both narrow band and white-light imaging modalities. The accuracy reached almost 95% for white-light imaging. This performance surpassed the correct classification rate of all 7 doctors. Additionally, the second task (three-category) results indicated that the simple CNN architecture outperformed both conventional ML based approaches and the doctors. This study shows the feasibility of using conventional machine learning or deep learning based approaches in automatic classification of colon types on colonoscopy images.Article Citation - WoS: 3Citation - Scopus: 3Artificial Cells: A Potentially Groundbreaking Field of Research and Therapy(Sciendo, 2024-01-01) Dundar, Mehmet Sait; Yildirim, A. Baki; Yildirim, Duygu T.; Akalin, Hilal; Dundar, MunisArtificial cells are synthetic constructs that mimic the architecture and functions of biological cells. Artificial cells are designed to replicate the fundamental principles of biological systems while also have the ability to exhibit novel features and functionalities that have not been achieved before. Mainly, Artificial cells are made up of a basic structure like a cell membrane, nucleus, cytoplasm and cellular organelles. Nanotechnology has been used to make substances that possess accurate performance in these structures. There are many roles that artificial cells can play such as drug delivery, bio-sensors, medical applications and energy storage. An additional prominent facet of this technology is interaction with biological systems. The possibility of synthetic cells being compatible with living organisms opens up the potential for interfering with specific biological activities. This element is one of the key areas of research in medicine, aimed at developing novel therapies and comprehending life processes. Nevertheless, artificial cell technology is not exempt from ethical and safety concerns. The interplay between these structures and biological systems may give rise to questions regarding their controllability and safety. Hence, the pursuit of artificial cell research seeks to reconcile ethical and safety concerns with the potential advantages of this technology.Article Citation - WoS: 4Citation - Scopus: 4Examining Tongue Movement Intentions in EEG-Based BCI With Machine and Deep Learning: An Approach for Dysphagia Rehabilitation(Sciendo, 2024) Aslan, Sevgi Gokce; Yilmaz, BulentDysphagia, a common swallowing disorder particularly prevalent among older adults and often associated with neurological conditions, significantly affects individuals' quality of life by negatively impacting their eating habits, physical health, and social interactions. This study investigates the potential of brain-computer interface (BCI) technologies in dysphagia rehabilitation, focusing specifically on motor imagery paradigms based on EEG signals and integration with machine learning and deep learning methods for tongue movement. Traditional machine learning classifiers, such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, AdaBoost, Bagging, and Kernel were employed in discrimination of rest and imagination phases of EEG signals obtained from 30 healthy subjects. Scalogram images obtained using continuous wavelet transform of EEG signals corresponding to the rest and imagination phases of the experiment were used as the input images to the CNN architecture. As a result, KNN (79.4%) and SVM (63.4%) exhibited lower accuracy rates compared to ensemble methods like AdaBoost, Bagging, and Random Forest, all achieving high accuracy rates of 99.8%. These ensemble techniques proved to be highly effective in handling complex EEG datasets, particularly in distinguishing between rest and imagination phases. Furthermore, the deep learning approach, utilizing CNN and Continuous Wavelet Transform (CWT), achieved an accuracy of 83%, highlighting its potential in analyzing motor imagery data. Overall, this study demonstrates the promising role of BCI technologies and advanced machine learning techniques, especially ensemble and deep learning methods, in improving outcomes for dysphagia rehabilitation.