Yazar "Aktürk, Erman" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    Neural network based VO2max prediction models using maximal exercise and non-exercise data [Maksimal egzersiz ve egzersize dayali olmayan verileri kullanarak sinir agi tabanli VO2MAX tahmin modelleri]
    (2013) Aktarla ,Ece; Akay, Mehmet Fatih; Aktürk, Erman; Açikkar, Mustafa
    Artificial Neural Network (ANN) models based on maximal and non-exercise (N-Ex) variables are developed to predict maximal oxygen uptake (VO 2max) the input variables of the dataset are gender, age, body mass index (BMI), grade, selfreported rating of perceived exertion (RPE) from treadmill test, heart rate (HR), perceived functional ability (PFA) and physical activity rating (PA-R). The performance of the models is evaluated by calculating their standard error of estimate (SEE) and multiple correlation coefficient (R). The results suggest that the performance of VO2max prediction models based on maximal and standard N-Ex variables (i.e. gender, age, BMI etc) can be improved by including questionnaire variables (PFA and PA-R) in the models.
  • Küçük Resim
    Öğe
    Performance comparison of different regression methods for VO2max ESTIMATION [VO2max tahmini için farkli regresyon yöntemlerinin performans Karsilastirmas]
    (2013) Aktürk, Erman; Akay, Mehmet Fatih; Kilitçioğlu, Hasan
    The purpose of this paper is to develop maximal oxygen uptake (VO 2max) models by using different regression methods such as Multilayer Feed-Forward Artificial Neural Networks (MFANN's), Support Vector Regression (SVR), Generalized Regression Neural Networks (GRNN's) and Multiple Linear Regression (MLR). The dataset includes data of 439 subjects and the input variables of the dataset are gender, age, body mass index (BMI), percent body fat (BF), respiratory exchange ratio (RER) from treadmill test, self-reported rating of perceived exertion (RPE) from treadmill test, heart rate (HR) and time to exhaustion from treadmill test. The performance of the models is evaluated by calculating their standard error of estimates (SEE) and multiple correlation coefficients (R). The results suggest that MFANN-based VO2max prediction models perform better than other prediction models.