Browsing by Author "Bati, C.T."

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Determining the Best Model With Deep Neural Networks: Keras Application on Mushroom Data
(Centenary University, 2019) Ser, G.; Bati, C.T.
This study was conducted to reveal the best classifying model with deep neural networks. For this purpose, 20 different candidate models of optimization method (Sgd, Adagrad, Rmsprop, Adam and Nadam), activation function (Tanh and ReLU) and combinations of neurons were studied. By comparing the performance of these candidate models, the best model for classification was determined. The present results indicated that the performance of the models varied according to the parameters, the most successful model has 64 neurons in the hidden layer, the activation function was ReLU and the Rmsprop was used as the optimization method (92% accuracy). In addition, it was determined that the model with the lowest success rate was the model with 32 neurons, ReLU activation function and Sgd optimization method (70% accuracy). Also considering all results; Rmsprop, Adam and Nadam optimization methods were found to be more successful than the other two methods and ReLU activation function produced more successful results than Tanh. © 2019, Centenary University. All rights reserved.
Effects of Data Augmentation Methods on Yolo V5s: Application of Deep Learning With Pytorch for Individual Cattle Identification
(Centenary University, 2023) Bati, C.T.; Ser, G.
In this paper, we investigate the performance of the YOLO v5s (You Only Look Once) model for the identification of individual cattle in a cattle herd. The model is a popular method for real-time object detection, accuracy, and speed. However, since the videos obtained from the cattle herd consist of free space images, the number of frames in the data is unbalanced. This negatively affects the performance of the YOLOv5 model. First, we investigate the model performance on the unbalanced initial dataset obtained from raw images, then we stabilize the initial dataset using some data augmentation methods and obtain the model performance. Finally, we built the target detection model and achieved excellent model performance with an mAP (mean average precision) of 99.5% on the balanced dataset compared to the model on the unbalanced data (mAP of 95.8%). The experimental results show that YOLO v5s has a good potential for automatic cattle identification, but with the use of data augmentation methods, superior performance can be obtained from the model. © 2023, Centenary University. All rights reserved.