Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 2Comparative Performance Analysis of Ethereum and Optimism Smart Contracts in Health Insurance(Institute of Electrical and Electronics Engineers Inc., 2024-10-26) Dedeturk, Beyhan Adanur; Dedeturk, Bilge KaganOne type of insurance that people purchase to cover unexpected medical expenses is health insurance. In exchange for premium payments, the insurance company may cover a portion of the insured person's medical expenses, such as prescription medications, hospital stays, and doctor visits. This method makes access to healthcare easier and less expensive. Health insurance systems do, however, have a number of issues, including fair insurance premium calculation, automation, data verification, privacy and security, and cost effectiveness. These issues are starting to be addressed by blockchain technology, particularly with the help of smart contracts. Using a comparison analysis between Ethereum and Optimism smart contracts, this paper demonstrates the performance of health insurance. Simulation of these BC technologies was carried out both on the Sepolia testnet and using Alchemy. Tools and metrics provided to monitor the performance of Alchemy applications, detect errors, and analyze user interactions were used in the measurements. While Ethereum's well-established ecosystem offers robust support for smart contracts, Optimism distinguishes itself as a scalable substitute that delivers quicker transaction speeds and more affordable options. According to the analysis results, the advantages and disadvantages of Ethereum and Optimism are highlighted when it comes to health insurance. © 2025 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 7A Comparative Analysis on Medical Article Classification Using Text Mining & Machine Learning Algorithms(Institute of Electrical and Electronics Engineers Inc., 2021-09-15) Kolukisa, Burak; Dedeturk, Bilge Kagan; Dedeturk, Beyhan Adanur; Gulsen, Abdulkadir; Bakal, Gokhan; Guisen, AbdulkadirThe 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.