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Öğe Design of experiments optimization application in physics: a case study of the damped driven pendulum experiment(Yıldız Teknik Üniversitesi, 2021) Atalan, Abdulkadir; Şahin, HasanThe design of experiment (DoE) approach developed for experiments requiring cost and time is applied in many disciplines. Unfortunately, the insufficient use of the DoE technique in physics led to the emergence of this study. This study aims to demonstrate the applicability of the DoE technique in the field of physics with a case study. The most widely used full factorial experimental design was used for the damped driven pendulum case study. Length (m), dumpling (Newton), and mass (kg) as independent and energy (joule) as dependent variables were defined in this study to apply the DoE approach. As a result of the statistical analyses in DoE, optimization models were created, and optimum values were obtained for the case study. The experiment performed was proved to be statistically significant and valid by calculating the R-square as 0.97. The value of the objective function is calculated as 4.058 (joule). The optimum values for length, dumping, and mass was calculated as 2.719 m, 2.485(Newton), and 2.895 kg, respectively. In conclusion, this study will contribute to the literature to guide the researchers who spend a lot of time in experimental labs and have problems with experiment costs.Öğe Forecasting of the Dental Workforce with Machine Learning Models(2024) Atalan, Abdulkadir; Sahın, 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 Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources(Mdpi, 2022) Atalan, Abdulkadir; Sahin, Hasan; Atalan, Yasemin AyazA healthcare resource allocation generally plays a vital role in the number of patients treated (p(nt)) and the patient waiting time (w(t)) in healthcare institutions. This study aimed to estimate p(nt) and w(t) as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (delta(i)) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the delta(i) of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the delta(0.0), delta(0.1), delta(0.2), and delta(0.3), the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for p(nt); 0.9514, 0.9517, 0.9514, and 0.9514 for w(t), respectively in the training stage. The GB algorithm had the best performance value, except for the results of the delta(0.2) (AB had a better accuracy at 0.8709 based on the value of delta(0.2) for p(nt)) in the test stage. According to the AB algorithm based on the delta(0.0), delta(0.1), delta(0.2), and delta(0.3), the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for p(nt); 0.8820, 0.8821, 0.8819, and 0.8818 for w(t) in the training phase, respectively. All scenarios created by the delta(i) coefficient should be preferred for ED since the income provided by the p(nt) value to the hospital was more than the cost of healthcare resources. On the contrary, the w(t) estimation results of ML algorithms based on the delta(i) coefficient differed. Although w(t) values in all ML algorithms with delta(0.0) and delta(0.1) coefficients reduced the cost of the hospital, w(t) values based on delta(0.2) and delta(0.3) increased the cost of the hospital.Öğe 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.
