Bayğın, Nursena
Loading...
Profile URL
Name Variants
Baygin, N.
N. Baygin
Baygin N
Baygin N.
Nursena Baygin
Baygin Nursena
N. Baygin
Baygin N
Baygin N.
Nursena Baygin
Baygin Nursena
Job Title
Dr. Öğr. Üyesi
Email Address
nursena.baygin@erzurum.edu.tr
Main Affiliation
4.4. Bilgisayar Mühendisliği Bölümü
Status
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
SDG data could not be loaded because of an error. Please refresh the page or try again later.
Scopus data could not be loaded because of an error. Please refresh the page or try again later.
WoS data could not be loaded because of an error. Please refresh the page or try again later.
Bibliometrics data could not be loaded because of an error. Please refresh the page or try again later.
Scholarly output chart could not be loaded because of an error. Please refresh the page or try again later.
Journals could not be loaded because of an error. Please refresh the page or try again later.
Scopus Quartile Distribution
Quartile distribution chart could not be loaded because of an error. Please refresh the page or try again later.
Competency Cloud
7 results
Scholarly Output Search Results
Now showing 1 - 7 of 7
Article Automated Characterization and Detection of Fibromyalgia Using Slow Wave Sleep EEG Signals with Glucose Pattern and D'hondt Pooling Technique(Springer, 2024) Aksalli, Isil Karabey; Baygin, Nursena; Hagiwara, Yuki; Paul, Jose Kunnel; Iype, Thomas; Barua, Prabal Datta; Acharya, U. RajendraFibromyalgia is a soft tissue rheumatism with significant qualitative and quantitative impact on sleep macro and micro architecture. The primary objective of this study is to analyze and identify automatically healthy individuals and those with fibromyalgia using sleep electroencephalography (EEG) signals. The study focused on the automatic detection and interpretation of EEG signals obtained from fibromyalgia patients. In this work, the sleep EEG signals are divided into 15-s and a total of 5358 (3411 healthy control and 1947 fibromyalgia) EEG segments are obtained from 16 fibromyalgia and 16 normal subjects. Our developed model has advanced multilevel feature extraction architecture and hence, we used a new feature extractor called GluPat, inspired by the glucose chemical, with a new pooling approach inspired by the D'hondt selection system. Furthermore, our proposed method incorporated feature selection techniques using iterative neighborhood component analysis and iterative Chi2 methods. These selection mechanisms enabled the identification of discriminative features for accurate classification. In the classification phase, we employed a support vector machine and k-nearest neighbor algorithms to classify the EEG signals with leave-one-record-out (LORO) and tenfold cross-validation (CV) techniques. All results are calculated channel-wise and iterative majority voting is used to obtain generalized results. The best results were determined using the greedy algorithm. The developed model achieved a detection accuracy of 100% and 91.83% with a tenfold and LORO CV strategies, respectively using sleep stage (2 + 3) EEG signals. Our generated model is simple and has linear time complexity.Conference Object Deep Learning Based Lesion Detection on Dental Panoramic Radiographs(Institute of Electrical and Electronics Engineers Inc., 2023) Demir, K.; Karabey Aksakalli, I.; Baygin, N.; Sokmen, O.C.With the development of technology, one of the new breakthroughs in the health field has been dental lesion detection. Dental lesion occurs with tissue changes that occur in the parts of the teeth that are especially decayed or filled, close to the gingiva. In cases where this change is not visible and cannot be distinguished from healthy tissue, computer-aided artificial intelligence methods can be used to assist the dentist quickly and accurately. In this study, panoramic images among radiographic images were obtained and deep learning methods applied to these images were compared in terms of accuracy, sensitivity, precision, F1 score, frame per second and intersection of units metrics. The labels of 660 panoramic images were validated by a specialist dentist and the images were trained using YOLOv5, YOLOv8 and SAM models. The experimental results show that YOLOv8 method reached the highest performance value with 0.975 accuracy, 0.978 precision and 0.986 sensitivity rate in the generated test model. © 2023 IEEE.Book Part The Role of Blockchain and DLT in Industry 5.0(Emerald Group Publishing Ltd., 2025) Baygin, N.Purpose: In this chapter of this book, the role and contributions of blockchain in Industry 5.0 are examined. Especially, the advantages offered by blockchain in mass customization, hyper-personalization, human-robot collaboration and cognitive systems, which constitute the main theme of Industry 5.0, are mentioned. Need for the study: With developing technology, revolutions are taking place in the industry. While these revolutions are occurring, various technologies serve as enablers. In this chapter, one of these technologies, blockchain, is examined. Methodology: Industrial revolutions represent important technological developments for societies. In this chapter, Industry 5.0, one of these revolutions, is discussed. In the first section, the main themes of Industry 5.0 were examined. Afterward, the transition from Industry 4.0 to Industry 5.0 was analyzed. Then, the opportunities offered by Industry 5.0 were reviewed. Subsequently, the contributions of blockchain to Industry 5.0 were examined. Finally, the role of blockchain in Industry 5.0 is summarized. Findings: The main themes of Industry 5.0 enable customized processes and smart production approaches. Blockchain makes significant contributions to these processes with its security and traceability features. In addition, smart contracts can increase transparency, traceability and security among stakeholders in the production process with their distributed ledger structure and immutability features. In blockchain networks, each transaction is carried out and approved by consensus. This consensus, provided by smart contracts, also secures transactions by reducing administrative costs. With these contributions, blockchain meets the security and smart management requirements of Industry 5.0. © 2025 by Nursena Baygin.Article Melatonin Pattern: A New Method for Machine Learning-Based Classification of Sleep Deprivation(MDPI, 2025) Baygin, NursenaBackground: Pattern recognition and machine learning-based classification approaches are frequently used, especially in the health field. In this research, a new feature extraction model inspired by the melatonin hormone (sleep hormone) and named MelPat (melatonin pattern) has been developed. The developed model has been tested on an open access dataset. Materials and Methods: An open access sleep deprivation electroencephalography (EEG) dataset was tested to evaluate the MelPat method. There are two classes in the dataset. These are (a) sleep deprivation (SD) and (b) healthy control (HC) groups, respectively. In this study, EEG signals were divided into 15 s segments, thus obtaining 1377 SD and 1378 HC samples. In the next phase of the research, a new feature extraction model was proposed, and this model was named MelPat as it was inspired by the melatonin hormone. Additionally, the feature vector was expanded using the statistical moment approach. In the signal decomposition phase of the model, the Tunable Q-Wavelet Transform (TQWT) method was used. Thus, the signal was decomposed into sub-bands, and feature extraction was applied to each band. Neighborhood Component Analysis (NCA) and Chi2 methods were used together to reduce the dimension of the feature vector and select the most significant features. In this phase, the most significant features from both feature selection algorithms were combined, and the final feature vector was obtained. In the classification phase of the model, the Support Vector Machine (SVM) algorithm, which is a shallow classifier, was used. The dataset used in the research has 61 channels. Therefore, after obtaining channel-based results, the iterative majority voting (IMV) algorithm was applied to achieve higher classification performance and generalize the results, and the most accurate results were automatically selected. Results: With the proposed MelPat algorithm, a high classification success of 97.71% was achieved on the open access sleep deprivation dataset. Conclusions: The obtained results show that the MelPat-based new classification approach is highly effective on the dataset collected for SD detection. Moreover, the fact that the proposed method is inspired by the melatonin chemical, which is the sleep hormone, makes the method attractive and ironic.Article PatchResNet: Multiple Patch Division-Based Deep Feature Fusion Framework for Brain Tumor Classification Using MRI Images(Springer, 2023) Muezzinoglu, Taha; Baygin, Nursena; Tuncer, Ilknur; Barua, Prabal Datta; Baygin, Mehmet; Dogan, Sengul; Acharya, U. RajendraModern computer vision algorithms are based on convolutional neural networks (CNNs), and both end-to-end learning and transfer learning modes have been used with CNN for image classification. Thus, automated brain tumor classification models have been proposed by deploying CNNs to help medical professionals. Our primary objective is to increase the classification performance using CNN. Therefore, a patch-based deep feature engineering model has been proposed in this work. Nowadays, patch division techniques have been used to attain high classification performance, and variable-sized patches have achieved good results. In this work, we have used three types of patches of different sizes (32 x 32, 56 x 56, 112 x 112). Six feature vectors have been obtained using these patches and two layers of the pretrained ResNet50 (global average pooling and fully connected layers). In the feature selection phase, three selectors-neighborhood component analysis (NCA), Chi2, and ReliefF-have been used, and 18 final feature vectors have been obtained. By deploying k nearest neighbors (kNN), 18 results have been calculated. Iterative hard majority voting (IHMV) has been applied to compute the general classification accuracy of this framework. This model uses different patches, feature extractors (two layers of the ResNet50 have been utilized as feature extractors), and selectors, making this a framework that we have named PatchResNet. A public brain image dataset containing four classes (glioblastoma multiforme (GBM), meningioma, pituitary tumor, healthy) has been used to develop the proposed PatchResNet model. Our proposed PatchResNet attained 98.10% classification accuracy using the public brain tumor image dataset. The developed PatchResNet model obtained high classification accuracy and has the advantage of being a self-organized framework. Therefore, the proposed method can choose the best result validation prediction vectors and achieve high image classification performance.Article Automated Mental Arithmetic Performance Detection Using Quantum Pattern- and Triangle Pooling Techniques with EEG Signals(Pergamon-Elsevier Science Ltd, 2023) Baygin, Nursena; Aydemir, Emrah; Barua, Prabal D.; Baygin, Mehmet; Doganm, Sengul; Tuncer, Turker; Acharya, U. RajendraBackground: Electroencephalography (EEG) signals recorded during mental arithmetic tasks can be used to quantify mental performance. The classification of these input EEG signals can be automated using machine learning models. We aimed to develop an efficient handcrafted model that could accurately discriminate "bad counters" vs. "good counters" in mental arithmetic. Materials and method: We studied a public mental arithmetic task performance EEG dataset comprising 20-channel EEG signal segments recorded from 36 healthy right-handed subjects divided into two classes 10 "bad counters" and 26 "good counters". The original 60-second EEG samples are divided into 424 15-second segments (119 and 305 belonging to the "bad counters" and "good counters", respectively) to input into our model. Our model comprised a novel multilevel feature extraction method based on (1) four rhombuses lattice pattern, a new generation function for feature extraction that was inspired by the lattice structure in post-quantum cryptography; and (2) triangle pooling, a new distance-based pooling function for signal decomposition. These were combined with downstream feature selection using iterative neighborhood component analysis, channel-wise result classification using support vector machine with leave-one-subject-out (LOSO) and 10-fold) crossvalidations (CVs) to calculate prediction vectors, iterative majority voting to generate voted vectors, and greedy algorithm to obtain the best results. Results: The model attained 88.44% and 96.42% geometric means and accuracies of 93.40% and 97.88%, using LOSO and 10-fold CVs, respectively. Conclusions: Our model's >93% classification accuracies compared favorably against published literature. Importantly, the model has linear computational complexity, which enhances its ease of implementation.Article Most Complicated Lock Pattern-Based Seismological Signal Framework for Automated Earthquake Detection(Elsevier, 2023) Ozkaya, Suat Gokhan; Baygin, Nursena; Barua, Prabal D.; Singh, Arvind R.; Bajaj, Mohit; Baygin, Mehmet; Acharya, U. RajendraBackground: Seismic signals record earthquakes and also noise from different sources. The influence of noise makes it difficult to interpret seismograph signals correctly. This study aims to develop a computationally lightweight, accurate, and explainable machine learning model for the automated detection of seismogram signals that could serve as an effective warning system for earthquake prediction.Material and method: We developed a handcrafted model for earthquake detection using a balanced dataset of 5001 earthquakes and 5001 non-earthquake signal samples. The model included multilevel feature extraction, selector-based feature selection, classification, and post-processing. Input signals were decomposed using tunable Q wave transform and fed to a statistical and textural feature extractor based on the most complicated lock pattern (MCLP). Four feature selectors were used to choose the most valuable features for the support vector machine classifier. Additionally, voted vectors were generated using iterative hard majority voting. Finally, the best model was chosen using a greedy algorithm.Results: The presented self-organized MCLP-based feature engineering model yielded 96.82% classification ac-curacy with 10-fold cross-validation using the seismic signal dataset.Conclusions: Our model attained high seismological signal detection performance comparable with more computationally expensive deep learning models. Our handcrafted explainable feature engineering model is computationally less expensive and can be easily implemented. Furthermore, we have introduced a competitive feature engineering model to the deep learning models for the seismic signal classification model.
