Thai digit handwriting image classification with convolutional neural networks

Abstract

This paper aims to determine the efficiency in classifying and recognizing Thai digit handwritten using convolutional neural networks (CNN). We created a new dataset called the Thai digit dataset. The performance test was divided into two parts: the first part determines the exact number of epochs, and the second part examines the occurrence of overfits in the model with Keras library's EarlyStoping() function, processed through Cloud Computing with Google Colaboratory, and used a Python programming language. The main parameters for the model were a dropout of 0.75, mini-batch size of 128, the learning rate of 0.0001, and using an Adam optimizer. This study found the model's predictive accuracy was 96.88 and the loss was 0.1075. The results showed that using CNN in image classification and recognition. It has a high level of prediction efficiency. However, the parameters in the model must be adjusted accordingly.

Neural network optimizer of proportional-integral-differential controller parameters

“…Currently, there are a large number of methods for determining the optimal tuning parameters of process control system regulators are proposed. These methods include the Ziegler-Nichols method [13], frequency method [14], and SIEMENS adaptive PID controller [15], based on identification approaches and methods of intelligent technologies [16], [17]. Application of these methods, for operative determination of regulators' tuning parameters at the change of operating modes of the control object (CO), causes some difficulties and faces certain difficulties in identification of the control object with inertial properties [18], [19].…”

Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation

“…Many recognition methods have been proposed. Some are used for Javanese script recognition [1]- [4], as well as non-Latin languages, such as Arabic [5]- [7], Tamil [8], Bangla or Bengali [9]- [11], Kannada [12], Gurmukhi [13], Tifinagh [14], and Thai [15]. Non-Latin character recognition is usually more difficult due to limited research and datasets and the relatively complex shapes of the character.…”