Implementation of deep learning-based currency recognition systems on visual data with adam optimizer - Subowo, Edy (Jurnal Mantik, 2024)
With the rising demand for automated financial systems, a reliable currency identification system is crucial. The dataset comprises images of currency notes, preprocessed and augmented to improve model performance. A convolutional neural network (CNN) model, built with TensorFlow and Keras, captures spatial hierarchies in the images. The Adam optimizer is employed to improve the efficiency of the training process, offering adaptive learning rates that accelerate convergence. The data is split into training and validation sets to evaluate accuracy and loss metrics over multiple epochs. Data augmentation techniques, such as random flipping, rotation, and zooming, are applied to enhance model robustness against variations in image quality and orientation. The model achieves over 90% accuracy by the final epoch, optimized with Adam, and is saved in formats such as TensorFlow SavedModel, TensorFlow.js, and Lite for web and mobile deployment. For example, the model is saved with model.save('saved_model/my_model') and can be reloaded using tf.keras.models.load_model('saved_model/my_model') for further evaluation. This system demonstrates the potential of deep learning with Adam optimization in automating financial processes, reducing errors, and improving transaction efficiency. Future work may focus on further optimization and real-time inference