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Conference Object Exercise Capacity and Activities of Daily Living in Ccpd Patients With Mild and Higher Symptom Scores(European Respiratory Soc Journals Ltd, 2024) Eyuboglu, Filiz; Saglam, Melda; Vardar-Yagli, Naciye; Calik-Kutukcu, Ebru; Coplu, Lutfi; Arikan, Hulya; Inal-Ince, Deniz[No Abstract Available]Conference Object Citation - Scopus: 4Quantum Machine Learning in Intrusion Detection Systems: a Systematic Mapping Study(Springer Science and Business Media Deutschland GmbH, 2024) Faker,O.; Cagiltay,N.E.The integration between quantum computing (QC) and machine learning algorithms (ML) aims to speed up computing processes and increase model accuracy rates, and this is what led developers and researchers to exploit this feature to study improving the performance of intrusion detection systems (IDSs). In this work, we present a systematic mapping review (SMR) of the most important works in the field of using quantum machine learning (QML) to increase the efficiency of anomaly detection techniques, which depend mainly on ML. After defining and applying the research methodology, the preliminary search results amounted to 240 studies from four databases. According to the exclusion and inclusion reports, we obtained 21 main studies. After reviewing and analyzing the results, the four research questions were answered. The review focused on the development of integration of intrusion detection systems with QML, the characteristics of such integration, increasing the efficiency of ML algorithms, and future research opportunities in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Conference Object Citation - Scopus: 1Extractive Text Summarization for Turkish: Implementation of Tf-Idf and Pagerank Algorithms(Springer Science and Business Media Deutschland GmbH, 2023) Akülker,E.; Turhan,Ç.Due to the massive amount of information available on the web, reaching the desired content has become more and more difficult. Automatic text summarization helps to solve the problem by minimizing the document size while keeping its core information. In this study, two extractive single document automatic text summarization systems for Turkish are presented which implement the statistical-based TF-IDF algorithm as well as the combination of TF-IDF with the graph-based PageRank algorithm. The study aims to reveal the usability and effectiveness of these algorithms for Turkish documents. Moreover, the results of the TF-IDF implementation and the hybrid approach are compared using the co-selection measures, precision, recall, and F-score. In the evaluation phase, the system-generated summaries are categorized and tested based on their word sizes and the predetermined thresholds and compared against the human-generated summaries. The results indicate that the hybrid system performs better than the TF-IDF system even in lower thresholds, and also both systems are inclined to improve average F-scores in higher threshold generated summarization. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Conference Object Driving Conditions Leading To Thermal Runaway in Li-Ion Battery EV's(IEEE, 2024) Ertan, H. Bulent; Azuaje-Berbeci, Bernardo J.The adoption of high-energy-density lithium-ion batteries (LIB) as the energy source in electric vehicles (EV) introduces significant safety concerns. Thermal runaway (TR), a self-accelerating rise in battery temperature resulting in catastrophic failure, is a significant safety concern. Cooling system failure within the EV's thermal management system is one of several factors that can trigger TR. Typically, TR is initiated by exceeding a critical temperature threshold under abusive conditions. Understanding the operating conditions that lead to the path of TR is essential for ensuring EV and occupant safety. Recently, a detailed electrochemical-thermal model that incorporates the chemical reactions within the battery until TR is introduced. This paper aims to illustrate how this model can be used to identify the conditions leading to TR under realistic EV driving scenarios. For this purpose, an Advisor/Matlab-based model of a hybrid EV is developed and verified by tests, is used to estimate the current required from the vehicle's battery pack at a given driving condition. This is followed by the prediction of battery thermal response using the mentioned finite-element-analysis-based battery model. Several scenarios are tested in this paper to determine whether TR occurs and to identify the factors contributing to TR. This study aids in comprehending the factors that contribute to TR and the development of preventative measures for battery management system design.Conference Object Threshold Structure-Preserving Signatures With Randomizable Key(Science and Technology Publications, Lda, 2025) Ağırtaş, A.R.; Çelik, E.; Kocaman, S.; Sulak, F.; Yayla, Oğuz; Çelik, Emircan; Ağırtas, Ahmet RamazanDigital signatures confirm message integrity and signer identity, but linking public keys to identities can cause privacy concerns in anonymized settings. Signatures with randomizable keys can break this link, preserving verifiability without revealing the signer. While effective for privacy, complex cryptographic systems need to be modular structured for efficient implementation. Threshold structure-preserving signatures enable modular, privacy-friendly protocols. This work combines randomizable keys with threshold structure-preserving signatures to create a valid, modular, and unlinkable foundation for privacy-preserving applications. © 2025 by Paper published under CC license (CC BY-NC-ND 4.0).Conference Object Fcnc Decays of Spin-1/2 Double Heavy Baryons To Spin-3/2 Single Heavy Baryons(E D P Sciences, 2024) Aliev, Tahmasib; Askan, Emre; Ozpineci, Altug; Sarac, YaseminIn recent years, there have been many discoveries in the spectroscopy of hadrons containing heavy quarks. Almost all baryons containing a single heavy quark are discovered in experiments. The quark model predicts the existence of many baryons with double heavy quarks. Among the possible double heavy baryons, only Xi(+)(cc) and Xi(++)(cc) have been experimentally observed in LHCb. Flavor-changing neutral current (FCNC) processes represent a promising platform for precise testing of SM and looking for new physics beyond the SM. In this study, the weak decays of spin-1/2 double heavy baryons to spin3/2 single heavy baryons induced by FCNC are studied within the light cone QCD sum rules method. First, the transition form factors of Xi(0,-)(bb) -> Xi(*0,(l) over bar l)(b), Xi(0,-)(bb) -> Sigma(*0,(l) over bar l)(b), Omega(-)(bb) -> Sigma(b)*(-(l) over barl), Omega(-)(bb) -> Sigma(*0,(l) over bar l)(b) decays are calculated. Then, the corresponding decay widths are estimated using the results for the form factors.Conference Object Evaluation of the Individualized Rehabilitation Program (happy Treatment Protocol) in Children With Nocturnal Enuresis(Springer London Ltd, 2023) Ozturk, A.; Sonmezer, E.; Dayanc, M.Conference Object Conference Object Conference Object Citation - WoS: 1Citation - Scopus: 1Deep Learning-Based Covid-19 Detection Using Lung Parenchyma Ct Scans(Springer international Publishing Ag, 2022) Kaya, Zeynep; Kurt, Zuhal; Koca, Nizameddin; Cicek, Sumeyye; Isik, SahinDuring the outbreak of the COVID-19 pandemic, it is important to improve early diagnosis using effective ways in order to lower the risks and further spread of the viruses as early as possible. This is also important when it comes to appropriate treatments and the reduction of mortality rates. In this respect, computer tomography (CT) scanning is a useful technique in detecting COVID-19. The present paper, as such, is an attempt to contribute to this process by generating an open-source, CT-based image dataset. This dataset contains the CT scans of lung parenchyma regions of 180 COVID-19 positives and 86 COVID-19 negative patients, all from Bursa Yuksek Ihtisas Training and Research Hospital. The experimental studies demonstrate that this dataset is effectively utilized deep learning-based models for diagnostic purposes. Firstly, a smart segmentation mechanism based on the k-means algorithm is applied to this dataset as a pre-processing stage. Then, the performance of the proposed method is evaluated using InceptionV3 and Xception convolutional neural networks, yielding a 96.20% and 96.55% accuracy rate and 95.00% and 95.50% F1-score, respectively. These state-of-the-art models are observed to detect COVID-19 cases faster and more accurately. In addition, the fine-tuning stage of the convolutional neural network (CNN) features sufficiently improves this accuracy rate. For these features, the support vector machine (SVM) classifier is used, resulting in remarkable 96.76% accuracy rate and 95.81% F1-score. The implications of the proposed method are immense both for present-day applications as well as future developments.
