Browsing by Author "Bal, C"

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    On Statistical Summability ((N)over-bar, P) of Sequences of Fuzzy Numbers
    Talo, Ö; Bal, C
    In this paper we introduce the concept of statistical summability ((N) over bar, p) of sequences of fuzzy numbers. We also present Tauberian conditions under which statistical convergence of a sequence of fuzzy numbers follows from its statistical summability ((N) over bar, p). Furthermore, we prove a Korovkin- type approximation theorem for fuzzy positive linear operators by using the notion of statistical summability ((N) over bar, p).
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    Bloomed or non-bloomed fruit tree classification with transfer learning
    Tas, G; Bal, C
    Agriculture is one of the oldest and most important production sectors in the history of mankind. Agricultural producers suffer losses every year due to the difficulties arising from seasonal conditions. In this study, a deep learning network has been developed to detect bloomed/non-bloomed trees that will support the use of pesticides in order to protect farmers from the damage of frost. For this purpose, a new data set including bloomed/non-bloomed was created over two years. Bloomed or non-bloomed tree detection performances were evaluated using different convolutional neural network (CNN) models with transfer learning on this new data set. Accuracy comparisons are given by including deep learning structures as NASNetMobile, MobileNetV2, ResNet50V2, VGG-16, VGG-19, and InceptionV3, and CNN from Scratch in the evaluation. The results are presented by changing different epochs and optimizer hyperparameter values with different learning rates. In addition, the runtime and parameter values of the examined CNN models were also compared. For the CNN from Scratch model, six different models were obtained by changing the number of convolution blocks and epochs. It was observed that the ResNet50V2 model made the best prediction with 98.65% accuracy after RMSprop optimizer, 10-4 learning rate and 20 epochs of training.
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    A New Lithium Polymer Battery Dataset with Different Discharge Levels: SOC Estimation of Lithium Polymer Batteries with Different Convolutional Neural Network Models
    Tas, G; Uysal, A; Bal, C
    In this study, a new dataset was created for use to estimate the state of charge (SOC) of lithium polymer batteries. A new experimental system was created to obtain the dataset by measuring the current, voltage, and temperature parameters of lithium polymer batteries. A convolutional neural network (CNN)-based deep learning model was used as the SOC prediction method. The effect of both batch size and dense network hyperparameter value on total parameter and deep learning error metric values for CNN-based lithium polymer battery SOC estimation is discussed. The proposed method, unlike deep learning models that require a high processing load in electronic cards, has provided remarkable results by being determined according to four different dense networks and two different batch size values. The proposed model has been obtained by performing experiments on optimizer, learning rate, dense network, and batch size values while determining the appropriate parameters to make successful predictions. The success of the CNN models was compared by conducting deep learning training on a computer with an Nvidia Gtx 1060 graphics card running the Ubuntu operating system. Adadelta optimizer achieved R2 0.977262 prediction success with learning rate 10(-2), batch size 5 x 10(2), dense 10(5) hyperparameter values. According to the results of the experiment, it was concluded that in the CNN deep learning method, large dense layers and small batch size values created less error in SOC estimation.
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    Performance comparison of lithium polymer battery SOC estimation using GWO-BiLSTM and cutting-edge deep learning methods
    Tas, G; Bal, C; Uysal, A
    In this study, the GWO-BiLSTM method has been proposed by successfully estimating the SOC with the BiLSTM deep learning method using the hyper-parameter values determined by the GWO method of the lithium polymer battery. EV, HEV, and robots are used more healthily with successful, reliable, and fast SOC estimation, which has an important place in the Battery Management System. In studies using deep learning methods, it is important to solve the problems of underfitting, overfitting, and estimation error by determining the hyper-parameters appropriately. Thus, this study aims to solve an important problem by investigating the problem of determining the hyperparameter values for the deep learning method with metaheuristic optimization methods. This study was designed to compare the prediction success of the BiLSTM method trained with the optimal hyperparameter values obtained by the GWO method with cutting-edge deep learning methods trained with hyperparameter values obtained by trial and error. The success of the proposed method was verified by comparing the cutting-edge data-based deep learning methods and the BiLSTM method with the SOC estimation MAE, MSE, RMSE, and Runtime(s) metrics. According to the findings obtained during the hyperparameter determination studies, it takes longer time to determine the hyperparameters by trial and error than to determine the hyperparameters by metaheuristic optimization method when estimating lithium battery SOC with the deep learning method. Also, the GWO-BiLSTM method was the most successful method with an RMSE of 0.09244% and an R2 of 0.9987 values according to the average results of SOC estimation made with the lithium polymer battery data set, which was created by experiments performed at different discharge levels and is new in the literature.
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    The Score for Allergic Rhinitis study in Turkey, 2020
    Cingi, C; Muluk, NB; Susaman, N; Küçükcan, N; Kar, M; Altintas, M; Altin, F; Eroglu, S; Kef, K; Ipçi, K; Güven, SG; Dizdar, SK; Çayir, S; Salcan, I; Korkmaz, MÖ; Yilmaz, AS; Topuz, B; Basak, S; Ural, A; Çobanoglu, BY; Erkan, AN; Oghan, F; Eskiizmir, G; Çakir, BÖ; Coskun, BU; Kara, CO; Gültekin, E; Üçüncü, H; Selcuk, A; Altuntas, EE; Durmus, K; Özlügedik, S; Toros, SZ; Karamese, O; Bayindir, T; Baylan, MY; Iynen, I; Yilmaz, O; Yilmaz, N; Avci, D; Aysel, A; Bal, C; Baser, S; Bozkurt, Z; Çatli, T; Çetinkaya, EA; Öner, F; Coskun, ZÖ; Dizdar, D; Eksi, E; Gümüslü, BC; Kaplan, AK; Kinar, A; Parildar, H; Resuli, AS; Köroglu, E; Yazici, D; Kurt, Y; Dilber, M; Çukurova, I; Annesi-Maesano, I
    Objective: This study aimed to determine how prevalent allergic rhinitis (AR) is in Turkey and to compare the current prevalence with the figures obtained 10 years earlier. Methods: This study included 9,017 participants. The minimum number of participants required from each center was determined via a stratified sampling technique according to regional demographic characteristics as ascertained from the last census. For each region, both men and women were administered the score for allergic rhinitis (SFAR) questionnaire and a score for each participant was calculated based on the responses supplied. Results: A total of 9,017 individuals (55.3% men and 44.7% women) took part in this study. Of these, 94.4% were urban residents and 5.6% lived in a rural setting. Of the men, 38.5% self-reported as suffering from AR. The corresponding figure in women was 40.5%. The overall prevalence of AR, as deduced on the basis of the SFAR, was found to be 36.7%. Comparing the prevalence in different regions, we found that AR was the least prevalent in the Black Sea region with a frequency of 35.8%. The highest prevalence was in the Mediterranean region, where the prevalence was 37.7%. There was no statistical significance in the apparent differences in prevalence between different geographical regions. Despite this, however, there was a clear increase in the frequency of AR over the preceding decade. This increase was most pronounced in the South-Eastern Anatolian region, where the frequency rose from 21.0% to 36.9%. Conclusion: Our results indicate that there has been a marked increase in the prevalence of AR in every region in Turkey over the last 10 years. This could be related to living conditions in urban environments. Alterations in lifestyle, urban living, air pollution causing impairments in immune defense mechanisms, and other aspects of modern lifestyles may account for the increase in AR in Turkey.