WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article An Elementary Proof of Lucas's Theorem(Ramanujan Mathematical Society, 2025) Cinkir, ZubeyirLucas's Theorem is about finding the result of a binomial coefficient modulo a prime p efficiently. The result is expressed as a product of binomial coefficients involving the base p expansions of the parameters of the original binomial coefficient. We give an elementary proof of Lucas's Theorem by deriving an analogous Vander-monde identity modulo a prime number.Article Development of Resistant Starch Type-5 and Its Utilization in Cookie-Preparation(North University Center Baia Mare, 2025-11-30) Oskaybas-Emlek, Betul; Ozbey, Ayse; Kahraman, KevserThe objective of this study was the production of resistant starch type-5 (RS-5), its characterization, and utilization in cookie making. In first part of the study, the effects of starch-fatty acid complex formation (RS-5) between tapioca starch and lauric acid on the structure, digestibility, thermal and morphological properties of tapioca starch were investigated. X-ray diffraction revealed that the RS-5 had a V-type crystalline pattern. FT-IR analysis showed that a distinctive peak at 2846 cm-1 was only observed in RS-5. The resistant starch (RS) content of native starch increased from 22.76% to 28.02% with RS-5 formation. In the second part of the study, the RS-5 was added as a replacement for wheat flour with 10%, 20%, and 30% compared to control sample made with 100% wheat flour in cookie-making. The effects of RS-5 replacement of cookie samples on some physicochemical, estimated glycemic index (eGI) value, physical, and hardness properties were determined. Compared to control cookie, the cookie samples included RS-5 had lower hardness value, higher spread ratio. The eGI value of cookie samples was slightly decreased with the replacement with RS-5. The results demonstrated that the RS-5 has good potential for developing softer cookie with no adverse impact on eGI value.Article High-Accuracy Identification of Durian Leaf Diseases: A Convolutional Neural Network Approach Validated with K-Fold Cross-Validation and Bayesian Optimization(Springer, 2025-11-18) Soylemez, Ismet; Nalici, Mehmet Eren; Unlu, RamazanTo address the economic losses caused by plant diseases in durian farming, this study presents an optimized deep learning model that diagnoses diseases from leaf images with high accuracy. The model's performance is maximized through Bayesian optimization and hyperparameter tuning, while its reliability is maximized through layered five-fold cross-validation. Training the convolutional neural network model on 2595 leaf images displaying six different states (five diseased and one healthy) resulted in an average test accuracy of 91.98%. This high, consistent success rate demonstrates the model's generalizability to different datasets without overfitting. While the 'Healthy' and 'Algal' classes were successfully detected with high F1-scores, there are difficulties distinguishing between the 'Blight' and 'Colletotrichum' classes due to visual similarities. This study establishes a new reference point for durian disease classification and makes a significant contribution to the development of reliable artificial intelligence-based diagnostic tools for precision agriculture.Article Citation - WoS: 1Comprehensive Prediction of FBN1 Targeting Mirnas: A Systems Biology Approach for Marfan Syndrome(Galenos Publishing House, 2025-09-22) Orhan, M.E.; Demirci, Y.M.; Saçar Demirci, M.D.S.; Demirci, Muserref Duygu SacarObjective: Marfan syndrome (MFS) is a genetic connective tissue disorder primarily caused by mutations in the FBN1 gene. Emerging evidence highlights the regulatory role of microRNAs (miRNAs) in modulating gene expression in MFS, but a systematic investigation into miRNAs targeting FBN1 is lacking. This study aimed to comprehensively identify miRNAs interacting with the FBN1 transcript to reveal potential molecular regulators and therapeutic targets. Methods: Human miRNA sequences were retrieved from miRBase (Release 22.1), and the canonical FBN1 transcript (RefSeq: NM_000138.5) was used for target prediction. Computational interaction analysis was conducted using the psRNATarget server with stringent parameters to detect potential miRNA binding sites. Expression profiles and disease associations of the top candidate miRNAs were further investigated through database integration and literature review. Results: Out of 2656 human mature miRNAs analyzed, 251 were predicted to bind FBN1, with the hsa-miR-181 family exhibiting the highest number of predicted interactions. Evidence from the literature highlighted dysregulation of hsa-miR-181 expression in MFS patients, suggesting a functional role in disease pathophysiology. Conclusion: This study identifies key members of the hsa-miR-181 family as post-transcriptional regulators of FBN1, offering new insights into miRNA-driven mechanisms in MFS. These findings support the potential of RNA-based diagnostics and therapeutic strategies targeting miRNA-FBN1 interactions. ©Copyright 2025 The Author.Article Developing a Label Propagation Approach for Cancer Subtype Classification Problem(Tubitak Scientific & Technological Research Council Turkey, 2022-01-01) Guner, Pinar; Bakir-Gungor, Burcu; Coskun, MustafaCancer is a disease in which abnormal cells grow uncontrollably and invade other tissues. Several types of cancer have various subtypes with different clinical and biological implications. Based on these differences, treatment methods need to be customized. The identification of distinct cancer subtypes is an important problem in bioinformatics, since it can guide future precision medicine applications. In order to design targeted treatments, bioinformatics methods attempt to discover common molecular pathology of different cancer subtypes. Along this line, several computational methods have been proposed to discover cancer subtypes or to stratify cancer into informative subtypes. However, existing works do not consider the sparseness of data (genes having low degrees) and result in an ill-conditioned solution. To address this shortcoming, in this paper, we propose an alternative unsupervised method to stratify cancer patients into subtypes using applied numerical algebra techniques. More specifically, we applied a label propagation based approach to stratify somatic mutation profiles of colon, head and neck, uterine, bladder, and breast tumors. We evaluated the performance of our method by comparing it to the baseline methods. Extensive experiments demonstrate that our approach highly renders tumor classification tasks by largely outperforming the state-of-the-art unsupervised and supervised approaches.Conference Object Text Classification Experiments on Contextual Graphs Built by N-Gram Series(Springer International Publishing AG, 2025) Sen, Tarik Uveys; Yakit, Mehmet Can; Gumus, Mehmet Semih; Abar, Orhan; Bakal, GokhanTraditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. The main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. This research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.Conference Object TextNetTopics+: Enhancing Text Classification Through Classifier Diversity and Model Ensembling(Springer International Publishing AG, 2025) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikTextNetTopics is an innovative text classification framework that integrates topic modeling with feature selection to improve model accuracy and interpretability. Unlike traditional methods that rely on individual words, TextNetTopics selects cohesive topics extracted via Latent Dirichlet Allocation as features for document representation, effectively reducing dimensionality while preserving the semantic structure of the text. This study evaluates the performance of TextNetTopics utilizing multiple machine learning algorithms in the M (Modeling) component, including Random Forest, Support Vector Machine, Gradient Boosting, eXtreme Gradient Boosting, and Logistic Regression. To further enhance classification performance, we introduce TextNetTopics+, an ensemblebased extension that leverages both hard voting and soft voting mechanisms to combine the strengths of multiple classifiers. Comprehensive experiments on the LitCovid and WOS datasets demonstrate that ensemble learning in TextNetTopics + significantly outperforms individual classifiers in TextNetTopics, confirming its effectiveness in improving model robustness and generalization.Conference Object Temporal Logic-Based Intrusion Detection for Securing Connected Vehicles(Springer International Publishing AG, 2024) Bozdal, MehmetEnsuring the security and integrity of in-vehicle communication networks (IVCNs) is paramount. The increasing connectivity of vehicles exposes them to unprecedented security vulnerabilities, necessitating innovative methodologies to safeguard against cyberattacks and unauthorized access. This research presents a novel approach to enhance IVCN security through the deployment of a Signal Temporal Logic (STL)-based Intrusion Detection System (IDS). Considering the limited resources of Electronic Control Units (ECUs), this approach offers an adaptive and lightweight solution that addresses the unique challenges posed by the dynamic nature of vehicular networks. The proposed STL-based IDS effectively detects a broad spectrum of intrusions while maintaining acceptable overhead for resource-constrained ECUs, thanks to its distributed architecture. Comprehensive experimental evaluations demonstrate significant performance improvements in detecting Denial of Service (DoS) attacks, achieving the highest accuracy of 0.996 and recall of 1.000. The system also excels in detecting fuzzy attacks, with the highest accuracy of 0.996.Conference Object Simple, Sustainable Fabrication of Fully Solution-Processed, Transparent, Metal-Semiconductor Photodetectors Using a Surgical Blade as an Alternative to Conventional Tools(SPIE - The International Society for Optics and Photonics, 2022-05-24) Savas, Muzeyyen; Yazici, Ahmet Faruk; Arslan, Aysenur; Mutlugun, Evren; Erdem, Talha; Yazic, Ahmet Faruk; Erdem1, TalhaFabrication of optoelectronic devices relies on the expensive, energy-consuming conventional tools such as chemical vapor deposition, lithography, and metal evaporation. Furthermore, the films used in these devices are usually deposited at elevated temperatures and under vacuum that impose further restrictions to the device fabrication. Developing an alternative technology would contribute to the efforts on achieving a more sustainable optoelectronics technology. Keeping this focus in our focus, here we present a simple technique to fabricate visible photodetectors. These fully solution-processed and transparent metal-semiconductor-metal photodetectors employ silver nanowires (Ag NW) as the transparent electrodes replacing the indium-tin oxide (ITO) commonly used in optoelectronic devices. By repeatedly spin coating Ag NWs on a glass substrate followed by the coating of ZnO nanoparticles, we obtained a highly conductive transparent electrode reaching a sheet resistance of 95 Omega/square as measured by the four-probe method. Optical spectroscopy revealed that the transmittance of the Ag NW-ZnO films was 84% at 450 nm while transmittance of the ITO films was 90% at same wavelength. Following the formation of the conductive film, we scratched it using a heated surgical blade to open a gap. The scanning electron microscope images indicate that a gap of similar to 30 mm is opened forming an insulating line. As the active layer, we drop-casted red-emitting CdSe/ZnS core-shell quantum dots (QDs) on to this gap to form a metal-semiconductor-metal photodetector. These visible QD- based photodetectors exhibited responsivities and detectivities up to 8.5 mA/W and 0.95x10(9) Jones, respectively. These proof-of-concept photodetectors show that the environmentally friendly, low- cost, and energy-saving technique presented here can be an alternative to conventional, more expensive, and energy-hungry techniques while fabricating light-harvesting devices.Conference Object Citation - WoS: 8Citation - Scopus: 12SVM-RCE-R Optimization of Scoring Function for SVM-RCE(Springer International Publishing AG, 2021) Yousef, Malik; Jabeer, Amhar; Bakir-Gungor, BurcuGene expression data classification provides a challenge in classification due to it having high dimensionality and a relatively small sample size. Different feature selection approaches have been used to overcome this issue and SVM-RCE being one of the more successful approach. This study is a continuation of two previous research studies SVM-RCE and SVM-RCE-R. SVM-RCE-R suggests a new approach in the scoring function for the clusters, showing that for some different combination of weights the performance was improved. The aim of this study is to find the optimal weights for the scoring function suggested in the study of SVM-RCE-R using optimization approaches. We have discovered that finding the optimal weights for the scoring function would improve the performance of the SVM-RCE-in most cases. We have shown that in some cases the performance is increased dramatically by 10% in terms of accuracy and AUC. By increasing the performance of the algorithm, it is more likely that we can extract subset genes relating to the class association of a microarray sample.
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