Karabey Aksakallı, İşıl
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Aksakalli, Isil Karabey
Karabey, Isil
Aksakalli, Isil K.
Aksalli, Isil Karabey
Karabey, Isil
Aksakalli, Isil K.
Aksalli, Isil Karabey
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Dr. Öğr. Üyesi
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isil.karabey@erzurum.edu.tr
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4.4. Bilgisayar Mühendisliği Bölümü
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Competency Cloud
23 results
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Now showing 1 - 10 of 23
Conference Object Autoencoder-Based Anomaly Detection and Analysis in Log Data Generated in Cloud Systems Using Natural Language Processing(Institute of Electrical and Electronics Engineers Inc., 2025) Ayvaz, I.; Karabey Aksakalli, I.K.; Baygin, M.In this study, an Autoencoder-based model was developed to detect anomalies in log data obtained from cloud systems. The dataset used consists of log records from the Blue Gene/L (BGL) supercomputer. In the preprocessing phase, log messages were vectorized using the TF-IDF method, and structural features such as content length, word count, and the presence of component/type information were extracted to create an enriched feature matrix. The model attempted to reconstruct each log entry and calculated the reconstruction errors. Records were then classified as normal or anomalous based on a threshold corresponding to the 95th percentile of these errors. The developed model achieved a high performance with an accuracy rate of 99.61%, as well as strong results in precision, recall, and F1-score metrics. Additional evaluations using ROCAUC and Precision-Recall curves further confirmed the model's robustness. The results demonstrate that the Autoencoder architecture can effectively detect anomalies in large and complex log datasets. Within the scope of the study, the proposed model was also evaluated comparatively against recent approaches such as DeepLog, LogRobust, MLP, and LogEvent2Vec. The proposed model outperformed all other methods across all performance metrics. These findings highlight the Autoencoder-based method as a strong alternative in terms of both computational efficiency and anomaly detection capability. © 2025 IEEE.Article Comprehensive Insights Into Artificial Intelligence for Dental Lesion Detection: A Systematic Review(MDPI, 2024) Demir, Kubra; Sokmen, Ozlem; Aksakalli, Isil Karabey; Torenek-Agirman, KubraBackground/Objectives: The growing demand for artificial intelligence (AI) in healthcare is driven by the need for more robust and automated diagnostic systems. These methods not only provide accurate diagnoses but also promise to enhance operational efficiency and optimize resource utilization in clinical workflows. In the field of dental lesion detection, the application of deep learning models to various imaging techniques has gained significant prominence. This study presents a comprehensive systematic review of the utilization of deep learning methods for detecting dental lesions across different imaging modalities, including panoramic imaging, periapical radiographs, and cone-beam computed tomography (CBCT). A systematic search was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a structured and transparent review process. Methods: This study addresses four key research questions related to the types of objects used for AI in dental images, state-of-the-art approaches for detecting lesions in dental images, data augmentation methods, and challenges and possible solutions to the existing AI-based dental lesion detection. Furthermore, this systematic review was performed on 29 primary studies identified from multiple electronic databases. This review focused on studies published between 2019 and 2024, sourced from IEEE, Web of Knowledge, Springer, ScienceDirect, PubMed, and Google Scholar. Results: We identified five types of lesions in dental images as periapical lesions, cyst lesions, jawbone lesions, dental caries, and apical lesions. Among the fourteen state-of-the-art deep learning approaches, the results demonstrate that deep learning models, such as U-Net, AlexNet, and You Only Look Once (YOLO) version 8 (YOLOv8) are commonly employed for dental lesion detection. These deep learning models have the potential to serve as integral components of decision-making processes by improving detection accuracy and supporting clinical workflows. Furthermore, we found that among twelve types of data augmentation techniques, flipping, rotation, and reflection methods played an important role in increasing the diversity of the datasets. We also identified six challenges for dental lesion detection, and the main issues were identified as data integration, poor data quality, limited model generalization, and overfitting. Proposed solutions against the aforementioned challenges include the integration of larger datasets, model optimization, and diversification of data sources. Conclusions: This study provides a comprehensive overview of current methodologies and potential advancements in dental lesion detection using deep learning. The findings indicate that possible solutions against the challenges of AI-based diagnostic methods in dental lesion detection need to be more generalizable regardless of image type, the number of data, and data quality.Conference Object Utilization of Room-To Transition Time in Wi-Fi Fingerprint-Based Indoor Localization(IEEE, 2015) Karabey, Isil; Bayindir, LeventIn indoor localization applications, many different methods have been proposed to increase positioning accuracy. Among these methods, fingerprint-based techniques are generally preferred because they use existing resources such as Wi-Fi, Bluetooth, FM signals, etc., and can be implemented on commonly used devices such as mobile phones. In this paper, we evaluate different Wi-Fi fingerprint-based methods on two datasets (with and without room-to-room transition features) created from the same environment, and we investigate the impact of room-to-room transition features on classification performance. To the best of our knowledge, transition time between rooms has not been used in past studies on fingerprint-based indoor localization. This information is of significant importance, due to the physical distance between rooms. Therefore, in this study source room and transition time to a target room have been included as features in addition to signal sources and signal strength values in the target room. From preliminary experimental results we observed that the transition time between rooms increases the performance of all tested positioning algorithms, with the Back-propagation classifier showing the best performance increase (13%).Article Systematic Approach for Generation of Feasible Deployment Alternatives for Microservices(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Aksakalli, Isil Karabey; Celik, Turgay; Can, Ahmet Burak; Tekinerdogan, BedirMicroservice architectures rely on the development of modular and independent software units, which typically address a single task and communicate with other microservices via well-defined interfaces. This has several benefits such as easier maintenance and update of services. However, deploying a microservice-based application is often more complicated than a monolithic application. While a monolithic application can be deployed one at a time on a group of similar servers behind a load balancer, a microservice-based application consists of different microservices and each microservice usually has more than one runtime instance that needs to be configured and deployed. For a small number of microservices and applications, the deployment could be done manually. However, a large number of microservices are frequently observed in practice. In such cases, the deployment becomes cumbersome and error-prone and does not scale with the increased number of services. To cope with this problem, we present a systematic approach and the corresponding tool support for enabling the deployment of microservices to resources that have limited capacity. Hereby, we model and define the design space given the deployment parameters and automatically derive the feasible deployment solution. The approach is validated using a taxi-hailing system case study inspired by Uber which has spread all over the world in recent years.Conference Object Detection and Correction of English Grammar Errors Using Natural Language Processing Methods(Institute of Electrical and Electronics Engineers Inc., 2025) Yazici, M.; Baygin, M.; Karabey Aksakalli, I.K.This study proposes a two-stage approach for the automatic detection and correction of grammatical errors in English. In the first stage, incorrect sentences were identified using the Random Forest classifier, which showed the highest success among various machine learning algorithms such as Random Forest, Decision Tree, Multi-Layer Perceptron, and Linear Support Vector Machines. In the second stage, grammatical correction was performed on only these sentences using the transformer-based BART model and T5 model. The dataset utilized is the Pranav GEC dataset published on Kaggle. After preprocessing, labeling, and balancing, sentence representations were generated using the TF-IDF vectorization technique. Experimental results indicate that the Random Forest model achieved a high F1 score in the classification task, while the BART model provided highly effective corrections, especially in terms of precision. The proposed method reduces computational cost while improving overall accuracy and provides more balanced results compared to traditional seq2seq-based approaches. This study offers a technically and pedagogically effective solution model for grammatical error correction systems and has practical potential for integration with language learning technologies. It also provides a foundation for future research in areas such as multilingual support, user feedback-based learning, and contextsensitive correction. © 2025 IEEE.Conference Object An Evaluation of Fingerprint-Based Indoor Localization Techniques(Institute of Electrical and Electronics Engineers Inc., 2015) Karabey Aksakalli, I.; Bayindir, L.Since GPS, as a commonly used positioning system in outdoor environments, cannot be used in indoor environments, localization methods suitable for indoor environments are still being investigated. The fingerprinting method stands out from other indoor localization methods because it can use existing signal sources and can be implemented by ubiquitous devices such as mobile phones. In this study, several classification algorithms used in the fingerprinting method are applied to two datasets obtained from two different environments (home and workplace). Among these classification algorithms, Random Forest achieved the best results with 87% and 74% accuracy rates for these datasets. These results are close to the results reported in previous studies, and the accuracy of the algorithms varies depending on the environment in which the dataset has been formed. © 2015 IEEE.Article Micro-Ide: A Tool Platform for Generating Efficient Deployment Alternatives Based on Microservices(Wiley, 2022) Aksakalli, Isil Karabey; Celik, Turgay; Can, Ahmet Burak; Tekinerdogan, BedirMicroservice architecture (MSA) is a paradigm to design and develop scalable distributed applications using loosely coupled, highly cohesive components that can be deployed independently. The applications that realize the MSA may contain thousands of services that together form the overall system. Microservices interact with each other by producing and consuming data. Deploying frequently communicating services to the same physical resource would reduce network utilization, which is vital for reducing costs and improving scalability. Since the physical resources have limited capacity, it is not always possible to deploy communicating services to the same resource. Therefore, automated efficient deployment alternatives need to be generated for MSA in the design phase. To address this problem, we proposed an algorithmic approach to generate efficient microservice deployment configurations to available cloud resources in our previous study. In this study, a tool (Micro-IDE) has been proposed to realize and evaluate this approach. The Micro-IDE tool has been validated using a case study inspired by the Spotify application.Article A Hybrid Patchnet-Attention Based Deep Learning Architecture for Multi-Type Fabric Defect Classification in Textile Manufacturing and Quality Control(Elsevier - Division Reed Elsevier India Pvt Ltd, 2025) Aksakalli, Isil Karabey; Demir, Kubra; Sokmen, OzlemAccurate and timely detection of defects that may occur on fabric surfaces is a critical requirement for ensuring sustainable production quality in the textile industry. Due to human resource, time, and cost limitations, there is a growing interest in advanced image processing and deep learning-based automatic defect detection systems to improve the accuracy and efficiency of quality control in fabric manufacturing processes. In this study, we propose a novel hybrid PatchNet-Attention architecture that integrates patch-based feature extraction with an attention mechanism to improve defect localization and recognition. To evaluate the generalizability of the proposed architecture, its performance was tested on three public datasets using different class structures. Specifically, four classification scenarios were conducted: (i) classification with baseline models, (ii) patch-based classification, (iii) classification with a Convolutional Block Attention Module (CBAM)-enhanced model, and (iv) the proposed hybrid PatchNet-Attention architecture. Initially, 15 pre-trained Convolutional Neural Network (CNN) architectures were evaluated using transfer learning on the ZD001 dataset. The best-performing models, ResNet101V2 and Xception, were then selected as the foundation for constructing the hybrid PatchNet-Attention model. The experimental results demonstrate that configurations incorporating the attention mechanism consistently achieve the highest performance across all evaluated datasets. Specifically, the hybrid PatchNet-Attention model attained superior outcomes on the ZD001 dataset, with an F1-score of 99.15% and a Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) of 99.5% in the three-class setting, and an F1-score of 97.28% with a ROC-AUC of 99.74% in the nine-subclass configuration. In the TILDA data set, the proposed model produced an F1 score of 87.74% and an ROC-AUC of 98.09%, while in the FDD data set it achieved an F1 score of 98.95% and a ROC-AUC of 99.50%. The source code of the proposed method can be accessed from the Data Availability section.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.Article Prediction of Spontaneous Distal Ureteral Stone Passage Using Artificial Intelligence(Springer, 2024) Aksakalli, Tugay; Aksakalli, Isil Karabey; Cinislioglu, Ahmet Emre; Utlu, Adem; Demirdogen, Saban Oguz; Celik, Feyzullah; Karabulut, IbrahimPurpose Identifying factors predicting the spontaneous passage of distal ureteral stones and evaluating the effectiveness of artificial intelligence in prediction. Materials and methods The files of patients presenting with distal ureteral stones were retrospectively evaluated. Those who experienced spontaneous passage were assigned to Group P, while those who did not were assigned to Group N. Demographic and clinical data of both groups were compared. Then, logistic regression analysis was performed to determine the factors predicting spontaneous stone passage. Based on these factors, a logistic regression model was prepared, and artificial intelligence algorithms trained on the dataset were compared with this model to evaluate the effectiveness of artificial intelligence in predicting spontaneous stone passage. Results When comparing stone characteristics and NCCT findings, it was found that the stone size was significantly smaller in Group P (4.9 +/- 1.7 mm vs. 6.8 +/- 1.4 mm), while the ureteral diameter was significantly higher in Group P (3.3 +/- 0.9 mm vs. 3.1 +/- 1.1 mm) (p < 0.05). Parameters such as stone HU, stone radiopacity, renal pelvis AP diameter, and perirenal stranding were similar between the groups. In multivariate analysis, stone size and alpha-blocker usage were significant factors in predicting spontaneous stone passage. The ROC analysis for the logistic regression model constructed from the significant variables revealed an area under the curve (AUC) of 0.835, with sensitivity of 80.1% and specificity of 68.4%. AI algorithms predicted the spontaneous stone passage up to 92% sensitivity and up to 86% specifity. Conclusions AI algorithms are high-powered alternatives that can be used in the prediction of spontaneous distal ureteral stone passage.
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