Browsing by Author "Bal C."

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    Seawater gel in allergic rhinitis: Entrapment effect and mucociliary clearance compared with saline
    (2010) Cingi C.; Halis Unlu H.; Songu M.; Yalcin S.; Topcu I.; Cakli H.; Bal C.
    Objective: We performed a prospective study to investigate the the efficacy of seawater gel in reducing symptoms in patients with mild allergic rhinitis. We also aimed to investigate the impact of nasal irrigation on mucociliary clearance with seawater gel compared with saline in this patient group. Methods: The study was performed in 100 consecutive adult individuals with a history of allergic rhinitis that was not controlled by anti-allergic drugs. Patients were assigned to receive seawater gel nasal spray for 10 days. The efficacy of treatment was assessed by means of total nasal symptom score and clinical findings. Results: A statistically significant difference was found between scores of ‘nasal discharge, nasal obstruction, sneezing, nasal itching—before and after treatment (p < 0.001). Clinical findings evaluation revealed a statistically significant decrease in lower turbinate colour rating and turbinate congestion at the end of treatment (p< 0.001). Saccharin transit time decreased from baseline in the seawater trials by 12% compared with a 4% decrease for saline. The difference between the percent changes was statistically significant (t = 2.177; p < 0.05). Conclusions: The present study provides evidence that a four times daily regimen of seawater gel can be an adjunctive therapy in the patient with allergic rhinitis. © 2010, SAGE Publications. All rights reserved.
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    On statistical summability (N, P) of sequences of fuzzy numbers
    (University of Nis, 2016) Talo Ö.; Bal C.
    In this paper we introduce the concept of statistical summability (N, 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, p). Furthermore, we prove a Korovkin-type approximation theorem for fuzzy positive linear operators by using the notion of statistical summability (N, p). © 2016, University of Nis. All rights reserved.
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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
    (Institute for Ionics, 2023) Taş 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 × 102, dense 105 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. © 2023, King Fahd University of Petroleum & Minerals.
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    Performance comparison of lithium polymer battery SOC estimation using GWO-BiLSTM and cutting-edge deep learning methods
    (Springer Science and Business Media Deutschland GmbH, 2023) Taş 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. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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    Bloomed or non-bloomed fruit tree classification with transfer learning
    (Taylor and Francis Ltd., 2023) Taş 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. © 2023 Informa UK Limited, trading as Taylor & Francis Group.