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    Exploring ChatGPT's role in healthcare management: Opportunities, ethical considerations, and future directions
    (Routledge Journals, Taylor & Francis Ltd, 2024) Atalan, Abdulkadir; Keskin, Abdulkadir; Özer, Süleyman
    The ChatGPT initiative, an advanced AI application, has gained significant attention in diverse sectors, including business, technology, and research. Despite its novelty, it has been accepted across various domains, sparking discussions and shaping roadmaps. This article reviews different perspectives on ChatGPT, highlighting existing literature's prevailing lack of standardized evaluation methodologies. The study examines ChatGPT among natural language processing models, exploring its merits, drawbacks, limitations, and future expectations for its application in health quality and management. A meticulous analysis of 104 publications, covering titles, abstracts, and keywords, was independently conducted, addressing four main and seven sub-subjects. The review utilized PubMed, Scopus, Google Scholar, ScienceDirect, and preprint databases such as medRxiv, arXiv, and SSRN to select relevant articles. ChatGPT is expected to efficiently process and analyze extensive healthcare data, facilitating data-driven decision-making for healthcare organizations to enhance their services. However, it is crucial to acknowledge that ChatGPT models are incomplete substitutes for human healthcare professionals and have limitations in addressing complex medical issues. Consequently, this study underscores the imperative for full human supervision in medical services, emphasizing the essential role of humans in ensuring the quality and effective management of healthcare services.
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    PRICE ESTIMATION OF SELECTED GRAINS PRODUCTS BASED ON MACHINE LEARNING FOR AGRICULTURAL ECONOMIC DEVELOPMENT IN TÜRKİYE
    (Pakistan Agricultural Scientists Forum, 2024) Keskin, Abdulkadir; Ersin, İrfan; Atalan, Abdulkadir
    This study aims to estimate the price fluctuations of essential grain products, namely bread wheat (Triticum aestivum), durum wheat (Triticum durum), barley (Hordeum vulgare), and corn (Zea mays), in T & uuml;rkiye using machine learning (ML) algorithms. Using data from January 2, 2020, to January 10, 2023, the study employs algorithms such as random forest (RF), neural network (NN), support vector machine (SVM), and linear regression (LR). Independent variables include oil prices, currency exchange rates, and grain production volumes. The random forest (RF) algorithm provided the best results with the highest R2 values, while NN and LR showed relatively lower performance. The study highlights the significant impact of production and consumption volumes on grain prices and underscores the importance of ML algorithms in predicting these prices amidst changing conditions. Investments in agricultural technologies should be increased to improve data collection and analysis processes, as this is crucial for preventing price fluctuations in the agricultural sector.
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    Strategic forecasting of renewable energy production for sustainable electricity supply: A machine learning approach considering environmental, economic, and oil factors in Turkiye
    (Public Library Science, 2025) Atalan, Yasemin Ayaz; Sahin, Hasan; Keskin, Abdulkadir; Atalan, Abdulkadir
    Providing electricity needs from renewable energy sources is an important issue in the energy policies of countries. Especially changes in energy usage rates make it necessary to use renewable energy resources to be sustainable. The electricity usage rate must be estimated accurately to make reliable decisions in strategic planning and future investments in renewable energy. This study aims to accurately estimate the renewable energy production rate to meet T & uuml;rkiye's electricity needs from renewable energy sources. For this purpose, well-known Machine Learning (ML) algorithms such as Random Forest (RF), Adaptive Boosting (AB), and Gradient Boosting (GB) were utilized. In obtaining forecast data, 15 variables were considered under the oil resources, environmental parameters, and economic factors which are the main parameters affecting renewable energy usage rates. The RF algorithm performed best with the lowest mean absolute percentage error (MAPE, 0.084%), mean absolute error (MAE, 0.035), root mean square error (RMSE, 0.063), and mean squared error (MSE, 0.004) values in the test dataset. The R-2 value of this model is 0.996% and the MAPE value is calculated lower than 10%. The AB model, on the other hand, has the highest error values in the test data set, but still provides an acceptable prediction accuracy. The R-2 value was 0.792% and the MAPE value (0.371%) of this model was calculated to be in the range of 20% < MAPE <= 50%. This study, with its proposed forecasting models, makes significant contributions to energy policies to develop appropriate policies only for planning the amount of electricity usage needed in the future. In this context, this study emphasizes that renewable energy-based electricity generation transformation should be considered as an important strategic goal in terms of both environmental sustainability and energy security.