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Öğe Changing the Patient Flow Process for the Green Area of Emergency Department: A Discrete-Event Simulation Approach(Dilaver TENGİLİMOĞLU, 2024) Atalan, AbdulkadirAim: This study aimed to analyze patient waiting times and hospital stay times by developing a different approach to the current patient flow processes in the green area of a hospital emergency department. Methods: Numerical and statistical results were obtained using a discrete event simulation model for the current and new situation of patient flow processes. Results: The waiting time decreased from 12.91 minutes in the current simulation model to 12.81 minutes in the proposed simulation model. The length of stay time decreased from 54.72 minutes in the current simulation model to 53.90 minutes in the proposed simulation model. In this case, it is seen that the proposed model provides a reduction in the length of stay and patient waiting time. Conclusion: The result shows that the proposed model provides some improvement in both waiting and length of stay time. This study offers a different approach to patient flow, allowing emergency department personnel to use their resources effectively and minimizing the time spent on patients' treatment or waiting periods. Thus, this study concluded that the effective management of recommended patient flow processes increases the capacity of emergency departments to deal with emergencies and accelerates access to emergency medical services.Öğe Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms(Mdpi, 2024) Atalan, Abdulkadir; Dönmez, Cem ÇağrıHospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital. The research leverages three distinct machine learning (ML) algorithms, namely Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), to analyze the appointment status of 1073 patients across nine different departments in a hospital. A mathematical formula has been developed to apply the penalty fee to evaluate the reappointment situations of the same patients in the first 100 days and the gaps in the appointment system, considering the estimated patient appointment statuses. Average penalty cost rates were calculated based on the ML algorithms used to determine the penalty costs patients will face if they do not show up, such as 22.87% for RF, 19.47% for GB, and 14.28% for AB. As a result, this study provides essential criteria that can help hospital management better understand the potential financial impact of patients missing appointments and can be considered when choosing between these algorithms.Öğe Exploring ChatGPT's role in healthcare management: Opportunities, ethical considerations, and future directions(Routledge Journals, Taylor & Francis Ltd, 2024) Atalan, Abdulkadir; Keskin, Abdulkadir; Özer, SüleymanThe 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.Öğe Forecasting of the Dental Workforce with Machine Learning Models(Bandırma Onyedi Eylül Üniversitesi, 2024) Atalan, Abdulkadir; Şahin, HasanThe aim of this study is to determine the factors affecting the dental workforce in Turkey to estimate the dentists employed with machine learning models. The predicted results were obtained by applying machine learning methods; namely, generalized linear model (GLM), deep learning (DL), decision tree (DT), random forest (RF), gradient boosted trees (GBT), and support vector machine (SVM) were compared. The RF model, which has a high correlation value (R2=0.998) with the lowest error rate (RMSE=656.6, AE=393.1, RE=0.025, SE=496115.7), provided the best estimation result. The SVM model provided the worst estimate data based on the values of the performance measurement criteria. This study is the most comprehensive in terms of the dental workforce, which is among the healthcare resources. Finally, we present an example of future applications for machine learning models that will significantly impact dental healthcare management.Öğe Integration of the Machine Learning Algorithms and I-MR Statistical Process Control for Solar Energy(Mdpi, 2023) Atalan, Yasemin Ayaz; Atalan, AbdulkadirThe importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and I-MR statistical process control (SPC) charts. The estimation data for the amount of solar energy production were obtained by using random forest (RF), linear regression (LR), gradient boosting (GB), and adaptive boost or AdaBoost (AB) algorithms from ML models. Data belonging to eight independent variables consisting of environmental and geographical factors were used. This study consists of approximately two years of data on the amount of solar energy production for 636 days. The study consisted of three stages: First, descriptive statistics and analysis of variance tests of the dependent and independent variables were performed. In the second stage of the method, estimation data for the amount of solar energy production, representing the dependent variable, were obtained from AB, RF, GB, and LR algorithms and ML models. The AB algorithm performed best among the ML models, with the lowest RMSE, MSE, and MAE values and the highest R2 value for the forecast data. For the estimation phase of the AB algorithm, the RMSE, MSE, MAE, and R2 values were calculated as 0.328, 0.107, 0.134, and 0.909, respectively. The RF algorithm performed worst with performance scores for the prediction data. The RMSE, MSE, MAE, and R2 values of the RF algorithm were calculated as 0.685, 0.469, 0.503, and 0.623, respectively. In the last stage, the estimation data were tested with I-MR control charts, one of the statistical control tools. At the end of all phases, this study aimed to validate the results obtained by integrating the two techniques. Therefore, this study offers a critical perspective to demonstrate a two-way verification approach to whether a system's forecast data are under control for the future.Öğe Machine Learning-Based Wind Energy Forecasting Using Weather Parameters: The Example of Yalova(Abdulkadir KESKİN Abdulkadir KESKİN, 2025) Atalan, Abdulkadir; Gündoğdu, Lütfi Alper; Kahyalık, Harun; Ayaz Atalan, YaseminIn this study, various machine learning algorithms were evaluated for estimating wind energy production using hourly meteorological data of Yalova province in 2018. The input parameters were input parameters of weather parameters such as temperature, relative humidity, air pressure, wind direction, and wind speed. In the analysis performed on a total of 50530 data points, methods such as Gradient Boosting (GB), Random Forests (RF), k-nearest neighbor (kNN), and Stochastic gradient descent (GBD) were compared. Model performances were evaluated according to Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), MAPE, and R2 criteria. According to the results, the best-performing algorithm was RF with an MSE value of 0.039, RMSE value of 0.197, MAE value of 0.081, MAPE value of 0.377, and R² score of 0.961. On the other hand, the SGD model showed the lowest performance with an MSE value of 0.175, RMSE value of 0.418, MAE value of 0.303, MAPE value of 0.581, and R² score of 0.822. These findings show that machine learning models, supported by selecting the correct weather parameters, can provide high accuracy in estimating wind energy production and contribute to energy management policies in this direction. Bu çalışmada, 2018 yılına ait Yalova ilinin saatlik meteorolojik verileri kullanılarak rüzgar enerjisi üretiminin tahmin edilmesinde çeşitli makine öğrenmesi algoritmaları değerlendirilmiştir. Girdi parametreleri; sıcaklık, bağıl nem, hava basıncı, rüzgar yönü ve rüzgar hızı gibi hava durumu parametreleridir. Toplam 50.530 veri noktası üzerinde yapılan analizde, Gradient Boosting (GB), Random Forests (RF), en yakın komşu (kNN) ve stokastik gradyan inişi (SGD) gibi yöntemler karşılaştırılmıştır. Model performansları Ortalama Mutlak Hata (MAE), Ortalama Kare Hata (MSE), Kök Ortalama Kare Hata (RMSE), Ortalama Mutlak Yüzde Hata (MAPE) ve R² kriterlerine göre değerlendirilmiştir. Sonuçlara göre, en iyi performansı gösteren algoritma; 0,039 MSE değeri, 0,197 RMSE değeri, 0,081 MAE değeri, 0,377 MAPE değeri ve 0,961 R² skoru ile RF olmuştur. Öte yandan, en düşük performansı gösteren model ise; 0,175 MSE değeri, 0,418 RMSE değeri, 0,303 MAE değeri, 0,581 MAPE değeri ve 0,822 R² skoru ile SGD modeli olmuştur. Bu bulgular, doğru hava durumu parametrelerinin seçimiyle desteklenen makine öğrenmesi modellerinin rüzgar enerjisi üretiminin tahmininde yüksek doğruluk sağlayabileceğini ve bu doğrultuda enerji yönetim politikalarına katkı sunabileceğini göstermektedir.Öğe 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, AbdulkadirThis 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.Öğe Process Capability Analysis of Prediction Data of ML Algorithms(İrfan ERSİN, 2024) Altuntaş, Tuğçe; Atalan, AbdulkadirThis study integrates process capability analysis with Machine Learning (ML) methods to optimize business processes. ML, especially Random Forest (RF) and k-nearest neighbor (kNN) algorithms, has enabled the practical analysis of large data sets by using them together with process capability analysis. This integration enabled real-time monitoring and predictive analytics, enabling the proactive identification of process variations and the making of timely adjustments to maintain or increase process capability. Additionally, ML algorithms have helped optimize process parameters and identify critical factors affecting process performance, allowing for continuous improvement and achieving desired quality standards with greater efficiency. In conclusion, this study provides the basis for the synergy between process capability analysis and ML methods to enable businesses to achieve higher levels of quality control, productivity, and competitiveness in dynamic and complex production environmentsÖğe Statistical Optimization and Analysis of Factors Maximizing Milk Productivity(Mdpi, 2025) Kurtulus, Yuecel; Sahin, Hasan; Atalan, AbdulkadirThis study was conducted to determine the biological and environmental factors affecting milk yield and dry matter consumption and to analyze the effects of these factors on animal production. The study determined the variables affecting milk yield as input factors, such as lactation period, number of days of gestation, age, TMR dry matter ratio, and environmental factors. As a result of regression analyses, it was determined that each 1% increase in the TMR dry matter ratio decreased the milk yield by 0.9148 L, and each increase in the number of lactations increased the daily milk yield by 3.753 L. However, it was observed that the increase in the number of lactation days caused a decrease in milk production, and milk yield decreased as the gestation period extended. The most appropriate independent variable values were determined using statistical optimization analyses to maximize milk yield and optimize dry matter consumption. As a result of the analyses, the optimum value for the TMR dry matter ratio was calculated as 46.77%, 5 for lactation number, 6 for lactation day number, 230 days for gestation period, 55.8 months for cow age, and 20 degrees C for air temperature. The optimum values of the dependent variables were determined to be 61.145 L for daily milk yield and 19.033 units for dry matter consumption. The prediction intervals provided by the model served as reference points for future observations and showed that milk production was strongly affected by certain environmental and biological factors.Öğe 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, AbdulkadirProviding 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.Öğe Testing the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance(Mdpi, 2025) Atalan, Yasemin Ayaz; Atalan, AbdulkadirThis study proposes a two-stage methodology for predicting wind energy production using time, environmental, technical, and locational variables. In the first stage, machine learning algorithms, including random forest (RF), gradient boosting (GB), k-nearest neighbors (kNNs), linear regression (LR), and decision trees (Tree), were employed to estimate energy output. Among these, RF exhibited the best performance with the lowest error metrics (MSE: 0.003, RMSE: 0.053) and the highest R-2 value (0.988). In the second stage, analysis of variance (ANOVA) was conducted to evaluate the statistical relationships between independent variables and the predicted dependent variable, identifying wind speed (p < 0.001) and rotor speed (p < 0.001) as the most influential factors. Furthermore, RF and GB models produced predictions most closely aligned with actual data, achieving R-2 values of 88.83% and 89.30% in the ANOVA validation phase. Integrating RF and GB models with statistical validation highlighted the robustness of the methodology. These findings demonstrate the robustness of integrating machine learning models with statistical verification methods.Öğe The ChatGPT application on quality management: a comprehensive review(Routledge Journals, Taylor & Francis Ltd, 2025) Atalan, AbdulkadirArtificial intelligence (AI)-based ChatGPT application has begun to be discussed in many areas due to its rapid impact on human life. This study provides an overview of the applicability of ChatGPT for quality management and discusses the advantages, disadvantages, limitations, different perspectives, and future concerns of ChatGPT. The current review study reveals the contribution levels of ChatGPT to the effectiveness of decisions that need to be taken into account and implemented in quality management, which is a human-oriented approach. The top 9 (3 main and 6 sub-subjects) titles, abstracts, and keywords for each search term (a total of 166 publications) were analyzed independently to examine all aspects of the topic of this study. PubMed, Scopus, Web of Science (WoS), medRxiv, arXiv, and SSRN databases were used to select relevant articles. Studies emphasize that the ChatGPT application has inevitably become an indispensable tool to provide better service, increase efficiency, and reduce errors by supporting quality management processes. In addition, while analyzing a certain point with specific techniques in quality-related studies, the studies examined indicate that ChatGPT provides continuous improvement and accuracy in quality management because it is a constantly developed and updated model. However, users should note that ChatGPT has many limitations regarding quality management and the need for human expertise and control. As a result, the present review study will contribute to researchers by providing basic suggestions for future quality applications of the ChatGPT tool.
