Adanur Dedetürk, Beyhan
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Adanur Dedeturk, Beyhan
Adanur, Beyhan
Beyhan Adanur Dedetürk
Dedeturk, Beyhan Adanur
Dedetürk, Beyhan Adanur
Adanur, Beyhan
Beyhan Adanur Dedetürk
Dedeturk, Beyhan Adanur
Dedetürk, Beyhan Adanur
Job Title
Arş. Gör.
Email Address
beyhan.adanur@agu.edu.tr
Main Affiliation
02. 04. Bilgisayar Mühendisliği
Status
Current Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Sustainable Development Goals
13
CLIMATE ACTION
0
Research Products
17
PARTNERSHIPS FOR THE GOALS
0
Research Products
8
DECENT WORK AND ECONOMIC GROWTH
0
Research Products
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
Research Products
12
RESPONSIBLE CONSUMPTION AND PRODUCTION
0
Research Products
16
PEACE, JUSTICE AND STRONG INSTITUTIONS
0
Research Products
11
SUSTAINABLE CITIES AND COMMUNITIES
1
Research Products
1
NO POVERTY
0
Research Products
6
CLEAN WATER AND SANITATION
0
Research Products
10
REDUCED INEQUALITIES
1
Research Products
14
LIFE BELOW WATER
2
Research Products
15
LIFE ON LAND
0
Research Products
5
GENDER EQUALITY
0
Research Products
4
QUALITY EDUCATION
0
Research Products
7
AFFORDABLE AND CLEAN ENERGY
0
Research Products
3
GOOD HEALTH AND WELL-BEING
6
Research Products
2
ZERO HUNGER
0
Research Products
Documents
9
Citations
83
h-index
5
This researcher does not have a WoS ID.
Scholarly Output
17
Articles
10
Views / Downloads
487/435
Supervised MSc Theses
1
Supervised PhD Theses
1
WoS Citation Count
46
Scopus Citation Count
83
WoS h-index
4
Scopus h-index
5
Patents
0
Projects
1
WoS Citations per Publication
2.71
Scopus Citations per Publication
4.88
Open Access Source
11
Supervised Theses
2
| Journal | Count |
|---|---|
| PeerJ Computer Science | 4 |
| 28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK | 1 |
| -- 3rd International Conference on Computer Science and Engineering, UBMK 2018 -- Sarajevo -- 143560 | 1 |
| -- 6th International Conference on Computer Science and Engineering, UBMK 2021 -- Ankara -- 176826 | 1 |
| -- 9th International Conference on Computer Science and Engineering, UBMK 2024 -- Antalya -- 204906 | 1 |
Current Page: 1 / 3
Scopus Quartile Distribution
Competency Cloud
17 results
Scholarly Output Search Results
Now showing 1 - 10 of 17
Article Privacy-Preserving Wireless Indoor Localization Systems(2023) Dedeturk, Beyhan Adanur; Kolukısa, Burak; Tonyali, SametRecently the number of buildings and interior spaces has increased, and many systems have been proposed to locate people or objects in these environments. At present, several technologies, such as GPS, Bluetooth, Wi-Fi, Ultrasound, and RFID, are used for positioning problems. Some of these technologies provide good results for positioning outdoors whereas some others are effective for indoor environments. While GPS is used for outdoor localization systems, Wi-Fi, Bluetooth, Ultra WideBand, and RFID are used for indoor localization systems (ILSs). Today, due to the proliferation and extensive usage of Wi-Fi access points, wireless-based technologies in indoor localization are preferred more than others. However, even though the abovementioned technologies make life easier for their users, ILSs can pose some privacy risks in case the confidentiality of the location data cannot be ensured. Such an incident is highly likely to result in the disclosure of users’ identities and behavior patterns. In this paper, we aim to investigate existing privacy-preserving wireless ILSs and discuss them.Article Citation - Scopus: 5CSA-DE-LR Enhancing Cardiovascular Disease Diagnosis With a Novel Hybrid Machine Learning Approach(PeerJ Inc., 2024) Dedeturk, Beyhan Adanur; Dedeturk, Bilge Kagan; Bakir-Güngör, BurcuCardiovascular diseases (CVD) are a leading cause of mortality globally, necessitating the development of efficient diagnostic tools. Machine learning (ML) and metaheuristic algorithms have become prevalent in addressing these challenges, providing promising solutions in medical diagnostics. However, traditional ML approaches often need to be improved in feature selection and optimization, leading to suboptimal performance in complex diagnostic tasks. To overcome these limitations, this study introduces a new hybrid method called CSA-DE-LR, which combines the clonal selection algorithm (CSA) and differential evolution (DE) with logistic regression. This integration is designed to optimize logistic regression weights efficiently for the accurate classification of CVD. The methodology employs three optimization strategies based on the F1 score, the Matthews correlation coefficient (MCC), and the mean absolute error (MAE). Extensive evaluations on benchmark datasets, namely Cleveland and Statlog, reveal that CSA-DELR outperforms state-of-the-art ML methods. In addition, generalization is evaluated using the Breast Cancer Wisconsin Original (WBCO) and Breast Cancer Wisconsin Diagnostic (WBCD) datasets. Significantly, the proposed model demonstrates superior efficacy compared to previous research studies in this domain. This study’s findings highlight the potential of hybrid machine learning approaches for improving diagnostic accuracy, offering a significant advancement in the fields of medical data analysis and CVD diagnosis. © 2024 Elsevier B.V., All rights reserved.Article Citation - WoS: 4CSA-DE-LR: Enhancing Cardiovascular Disease Diagnosis With a Novel Hybrid Machine Learning Approach(PeerJ Inc, 2024) Dedeturk, Beyhan Adanur; Dedeturk, Bilge Kagan; Bakir-Gungor, BurcuCardiovascular diseases (CVD) are a leading cause of mortality globally, necessitating the development of efficient diagnostic tools. Machine learning (ML) and metaheuristic algorithms have become prevalent in addressing these challenges, providing promising solutions in medical diagnostics. However, traditional ML approaches often need to be improved in feature selection and optimization, leading to suboptimal performance in complex diagnostic tasks. To overcome these limitations, this study introduces a new hybrid method called CSA-DE-LR, which combines the clonal selection algorithm (CSA) and differential evolution (DE) with logistic regression. This integration is designed to optimize logistic regression weights efficiently for the accurate classification of CVD. The methodology employs three optimization strategies based on the F1 score, the Matthews correlation coefficient (MCC), and the mean absolute error (MAE). Extensive evaluations on benchmark datasets, namely Cleveland and Statlog, reveal that CSA-DELR outperforms state-of-the-art ML methods. In addition, generalization is evaluated using the Breast Cancer Wisconsin Original (WBCO) and Breast Cancer Wisconsin Diagnostic (WBCD) datasets. Significantly, the proposed model demonstrates superior efficacy compared to previous research studies in this domain. This study's findings highlight the potential of hybrid machine learning approaches for improving diagnostic accuracy, offering a significant advancement in the fields of medical data analysis and CVD diagnosis.Article A Comparative Analysis of Passenger Flow Forecasting in Trams Using Machine Learning Algorithms(2024) Akbaş, Ayhan; Dedeturk, Beyhan Adanur; Dedeturk, Bilge KaganForecasting tram passenger flow is an important part of the intelligent transportation system since it helps with resource allocation, network design, and frequency setting. Due to varying destinations and departure times, it is difficult to notice large fluctuations, non-linearity, and periodicity of tram passenger flows. In this paper, the first-order difference technique is used to eliminate seasonal structure from the time series data and the performance of different machine learning algorithms on passenger flow forecasting in trams is evaluated. Furthermore, the impact of the Covid-19 pandemic on forecasting success is examined. For this purpose, the tram data of Kayseri Transportation Inc. for the years 2018-2021 are used. Different estimation models including Linear Regression, Support Vector Regression, Random Forest, Artificial Neural Network, Convolutional Neural Network, and LongTerm Short Memory are applied and the time series forecasting performances of the models are evaluated with MAPE and R2 metrics.Doctoral Thesis Merkezi Olmayan Elektronik Sağlık Kaydı Yönetim Sistemi ve Makine Öğrenmesi Yöntemleri ile Hastalık Tahmini(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2024) Dedetürk, Beyhan Adanur; Güngör, BurcuElectronic health records (EHRs) are vital to the advancement of healthcare and can help detect and prevent diseases early. However, EHR sharing faces challenges such as managing large data volumes, ensuring data privacy, security, and interoperability. This thesis aims to develop and analyze a blockchain-based EHR sharing system for disease prediction mechanism integration using SysML. The AguHyper platform, built by merging the InterPlanetary File System (IPFS) with Hyperledger Fabric, ensures the immutability of health records by storing hash values in the blockchain and encrypted records in IPFS. The system architecture and implementation configurations, including CouchDB and the Raft consensus mechanism, are thoroughly examined. The study also presents a novel hybrid approach called CSA-DE-LR, which integrates Differential Evolution (DE) and Clonal Selection Algorithm (CSA) with Logistic Regression (LR) to improve LR weights for precise categorization of cardiovascular diseases. The integration of the AguHyper with the CSA-DE-LR is explained in detail. At the end of our performance evaluations, we concluded that the AguHyper model has the potential to speed up the process of collecting and sharing data, and it offers an efficient platform for the participants.Conference Object Citation - Scopus: 7A Comparative Analysis on Medical Article Classification Using Text Mining & Machine Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2021) Kolukisa, Burak; Dedeturk, Bilge Kagan; Dedeturk, Beyhan Adanur; Gulsen, Abdulkadir; Bakal, GokhanThe document classification task is one of the widely studied research fields on multiple domains. The core motivation of the classification task is that the manual classification efforts are impractical due to the exponentially growing document volumes. Thus, we densely need to exploit automated computational approaches, such as machine learning models along with data & text mining techniques. In this study, we concentrated on the classification of medical articles specifically on common cancer types, due to the significance of the field and the decent number of available documents of interest. We deliberately targeted MEDLINE articles about common cancer types because most cancer types share a similar literature composition. Therefore, this situation makes the classification effort relatively more complicated. To this end, we built multiple machine learning models, including both traditional and deep learning architectures. We achieved the best performance (R¿82% F score) by the LSTM model. Overall, our results demonstrate a strong effect of exploiting both text mining and machine learning methods to distinguish medical articles on common cancer types. © 2022 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 10Sağlıkta Blokzincir Tabanlı Sistem Bilişimi Uygulamaları(Institute of Electrical and Electronics Engineers Inc., 2020) Dedeturk, Beyhan Adanur; Bakir-Güngör, Burcu; Soran, AhmetRecently, the use of blockchain technology in the field of healthcare has increased. Although blockchain technology brought several innovations to healthcare, still there are problems waiting to be resolved. In order to provide alternative solutions to these problems, the use of fog computing together with blockchain technology has been proposed. In this study, the applications of blockchain based fog computing technology in healthcare are investigated. The aim of this study is to provide the readers an idea about the interactive use of blockchain and fog computing in the field of healthcare. For this purpose, firstly, fog computing and blockchain technologies are introduced. Afterwards, the integration of these areas, the advantages and disadvantages of using these technologies in the field of healthcare is discussed and a new system architecture is proposed. © 2021 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 4Blockchain-Based Fog Computing Applications in Healthcare(IEEE, 2020) Adanur, Beyhan; Bakir-Gungor, Burcu; Soran, AhmetRecently, the use of blockchain technology in the field of healthcare has increased. Although blockchain technology brought several innovations to healthcare, still there are problems waiting to be resolved. In order to provide alternative solutions to these problems, the use of fog computing together with blockchain technology has been proposed. In this study, the applications of blockchain based fog computing technology in healthcare are investigated. The aim of this study is to provide the readers an idea about the interactive use of blockchain and fog computing in the field of healthcare. For this purpose, firstly, fog computing and blockchain technologies are introduced. Afterwards, the integration of these areas, the advantages and disadvantages of using these technologies in the field of healthcare is discussed and a new system architecture is proposed.Article Citation - WoS: 17Citation - Scopus: 28Blockchain for Genomics and Healthcare: A Literature Review, Current Status, Classification and Open Issues(PeerJ Inc, 2021) Dedeturk, Beyhan Adanur; Soran, Ahmet; Bakir-Gungor, BurcuThe tremendous boost in the next generation sequencing technologies and in the "omics"technologies resulted in the generation of hundreds of gigabytes of data per day. Nowadays, via integrating -omics data with other data types, such as imaging and electronic health record (EHR) data, panomics studies attempt to identify novel and potentially actionable biomarkers for personalized medicine applications. In this respect, for the accurate analysis of -omics data and EHR, there is a need to establish secure and robust pipelines that take the ethical aspects into consideration, regulate privacy and ownership issues, and data sharing. These days, blockchain technology has picked up significant attention in diverse fields, including genomics, since it offers a new solution for these problems from a different perspective. Blockchain is an immutable transaction ledger, which offers secure and distributed system without a central authority. Within the system, each transaction can be expressed with cryptographically signed blocks, and the verification of transactions is performed by the users of the network. In this review, firstly, we aim to highlight the challenges of EHR and genomic data sharing. Secondly, we attempt to answer "Why"or "Why not"the blockchain technology is suitable for genomics and healthcare applications in detail. Thirdly, we elucidate the general blockchain structure based on the Ethereum, which is a more suitable technology for the genomic data sharing platforms. Fourthly, we review current blockchain-based EHR and genomic data sharing platforms, evaluate the advantages and disadvantages of these applications, and classify these applications using different metrics. Finally, we conclude by discussing the open issues and introducing our suggestion on the topic. In summary, to facilitate the diagnosis, monitoring and therapy of diseases with the effective analysis of -omics data with other available data types, through this review, we put forward the possible implications of the blockchain technology to life sciences and healthcare.Article Aguhyper: A Hyperledger-Based Electronic Health Record Management Framework(PeerJ Inc, 2024) Dedeturk, Beyhan Adanur; Bakir-Gungor, BurcuThe increasing importance of healthcare records, particularly given the emergence of new diseases, emphasizes the need for secure electronic storage and dissemination. With these records dispersed across diverse healthcare entities, their physical maintenance proves to be excessively time-consuming. The prevalent management of electronic healthcare records (EHRs) presents inherent security vulnerabilities, including susceptibility to attacks and potential breaches orchestrated by malicious actors. To tackle these challenges, this article introduces AguHyper, a secure storage and sharing solution for EHRs built on a permissioned blockchain framework. AguHyper utilizes Hyperledger Fabric and the InterPlanetary Distributed File System (IPFS). Hyperledger Fabric establishes the blockchain network, while IPFS manages the off -chain storage of encrypted data, with hash values securely stored within the blockchain. Focusing on security, privacy, scalability, and data integrity, AguHyper ' s decentralized architecture eliminates single points of failure and ensures transparency for all network participants. The study develops a prototype to address gaps identi fi ed in prior research, providing insights into blockchain technology applications in healthcare. Detailed analyses of system architecture, AguHyper ' s implementation con fi gurations, and performance assessments with diverse datasets are provided. The experimental setup incorporates CouchDB and the Raft consensus mechanism, enabling a thorough comparison of system performance against existing studies in terms of throughput and latency. This contributes signi fi cantly to a comprehensive evaluation of the proposed solution and offers a unique perspective on existing literature in the fi eld.
