Implementation of deep learning-based currency recognition systems on visual data with adam optimizer
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Abstract
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
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Subowo, E. (2024) “Implementation of deep learning-based currency recognition systems on visual data with adam optimizer”, Jurnal Mantik, 8(3), pp. 1464-1473. doi: 10.35335/mantik.v8i3.5723.
References
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Wang, B., Xu, C., Wang, S., Gan, Z., Cheng, Y., Gao, J., Awadallah, A. H., & Li, B. (2021). Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models. NeurIPS, 1–24. http://arxiv.org/abs/2111.02840
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Yan, R., Ren, F., Wang, Z., Wang, L., Zhang, T., Liu, Y., Rao, X., Zheng, C., & Zhang, F. (2020). Breast cancer histopathological image classification using a hybrid deep neural network. Methods, 173(2019), 52–60. https://doi.org/10.1016/j.ymeth.2019.06.014
Adu, K., Mensah, P. K., Ayidzoe, M. A., Appiah, O., Quayson, E., Ninfaakang, C. B., & Opoku, M. (2022). GC3558: An open-source annotated dataset of Ghana currency images for classification modeling. Data in Brief, 45, 108616. https://doi.org/10.1016/j.dib.2022.108616
Aprillia, D., Rohana, T., Al Mudzakir, T., & Wahiddin, D. (2024). KLIK: Kajian Ilmiah Informatika dan Komputer Deteksi Nominal Mata Uang Rupiah Menggunakan Metode Convolutional Neural Network dan Feedforward Neural Network. Media Online, 4(4), 2068–2077. https://doi.org/10.30865/klik.v4i4.1711
Bae, K., Ryu, H., & Shin, H. (2019). Does Adam optimizer keep close to the optimal point? NeurIPS. http://arxiv.org/abs/1911.00289
Bidari, V. (2022). RBC Classification using Deep Learning. 4(7), 250–255. https://doi.org/10.1109/ocit53463.2021.00058
Bock, S., & Weis, M. (2019). A Proof of Local Convergence for the Adam Optimizer. Proceedings of the International Joint Conference on Neural Networks, 2019-July. https://doi.org/10.1109/IJCNN.2019.8852239
Chitra Desai. (2020). Comparative Analysis of Optimizers in Deep Neural Networks. International Journal of Innovative Science and Research Technology , 5(10), 959–962.
Hasan, N., & Bao, Y. (2021). Understanding current states of machine learning approaches in medical informatics: a systematic literature review. Health and Technology, 11(3), 471–482. https://doi.org/10.1007/s12553-021-00538-6
Huang, S., & Trad, D. (2023). Convolutional Neural-Network-Based Reverse-Time Migration with Multiple Reflections. Sensors, 23(8). https://doi.org/10.3390/s23084012
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53(8), 5455–5516. https://doi.org/10.1007/s10462-020-09825-6
Liu, Y., Zhou, Y., Wen, S., & Tang, C. (2014). A Strategy on Selecting Performance Metrics for Classifier Evaluation. International Journal of Mobile Computing and Multimedia Communications, 6(4), 20–35. https://doi.org/10.4018/IJMCMC.2014100102
Maharana, K., Mondal, S., & Nemade, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), 91–99. https://doi.org/10.1016/j.gltp.2022.04.020
Mahesh Bahrani, S. (2020). Deep Learning Approach for Indian Currency Classification. International Journal of Engineering Applied Sciences and Technology, 5(6), 335–340. https://doi.org/10.33564/ijeast.2020.v05i06.049
Menghani, G. (2023). Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better. ACM Computing Surveys, 55(12). https://doi.org/10.1145/3578938
Mohammed, A., & Kora, R. (2023). A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University - Computer and Information Sciences, 35(2), 757–774. https://doi.org/10.1016/j.jksuci.2023.01.014
Nanni, L., Maguolo, G., & Lumini, A. (2021). Exploiting Adam-like Optimization Algorithms to Improve the Performance of Convolutional Neural Networks. ArXiv, 1–5.
Nawrocka, A., Nawrocki, M., & Kot, A. (2023). Research study of image classification algorithms based on Convolutional Neural Networks. Proceedings of the 2023 24th International Carpathian Control Conference, ICCC 2023, 299–302. https://doi.org/10.1109/ICCC57093.2023.10178933
Raiaan, M. A. K., Sakib, S., Fahad, N. M., Mamun, A. Al, Rahman, M. A., Shatabda, S., & Mukta, M. S. H. (2024). A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks. Decision Analytics Journal, 11(April), 100470. https://doi.org/10.1016/j.dajour.2024.100470
Reyad, M., Sarhan, A. M., & Arafa, M. (2023). A modified Adam algorithm for deep neural network optimization. Neural Computing and Applications, 35(23), 17095–17112. https://doi.org/10.1007/s00521-023-08568-z
Sarvamangala, D. R., & Kulkarni, R. V. (2022). Convolutional neural networks in medical image understanding: a survey. Evolutionary Intelligence, 15(1), 1–22. https://doi.org/10.1007/s12065-020-00540-3
Shah, A. A., Malik, H. A. M., Muhammad, A. H., Alourani, A., & Butt, Z. A. (2023). Deep learning ensemble 2D CNN approach towards the detection of lung cancer. Scientific Reports, 13(1), 1–15. https://doi.org/10.1038/s41598-023-29656-z
Smilkov, D., Thorat, N., Assogba, Y., Yuan, A., Kreeger, N., Yu, P., Zhang, K., Cai, S., Nielsen, E., Soergel, D., Bileschi, S., Terry, M., Nicholson, C., Gupta, S. N., Sirajuddin, S., Rajat, D. S., Greg, M., Fernanda, C., & Wattenberg, M. (n.d.). T f .?: m l w b.
Subowo, E., Adi Artanto, F., Putri, I., & Umaedi, W. (2022). BLTSM untuk analisis sentimen berbasis aspek pada aplikasi belanja online dengan cicilan. Jurnal Fasilkom, 12(2), 132–140.
Subowo, E., Rosyadi, I., & Kusumawardhani, H. H. (2020). Twitter Data as Decision Tree Parameter for Analysis of Tourism Potential Policies. 436, 474–478. https://doi.org/10.2991/assehr.k.200529.099
Subowo, E., Soeprobowati, T. R., & Widodo, A. P. (2021). Prediction Meaning of Words with Word2Vec on Whatsapp Data for Disaster Topic. E3S Web of Conferences, 317, 05020. https://doi.org/10.1051/e3sconf/202131705020
Sultana, J., Ahmed, B. M., Masud, M. M., Huq, A. K. O., Ali, M. E., & Naznin, M. (2023). A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques. IEEE Access, 11(April), 45910–45935. https://doi.org/10.1109/ACCESS.2023.3274475
Vydiswaran, V. G. V., Zhang, Y., Wang, Y., & Xu, H. (2019). Special issue of BMC medical informatics and decision making on health natural language processing. BMC Medical Informatics and Decision Making, 19(Suppl 3), 6–8. https://doi.org/10.1186/s12911-019-0777-0
Wang, B., Xu, C., Wang, S., Gan, Z., Cheng, Y., Gao, J., Awadallah, A. H., & Li, B. (2021). Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models. NeurIPS, 1–24. http://arxiv.org/abs/2111.02840
Watson, E., Viana, T., Zhang, S., Sturgeon, B., & Petersson, L. (2024). Towards an End-to-End Personal Fine-Tuning Framework for AI Value Alignment. Electronics (Switzerland), 13(20), 0–1. https://doi.org/10.3390/electronics13204044
Yan, R., Ren, F., Wang, Z., Wang, L., Zhang, T., Liu, Y., Rao, X., Zheng, C., & Zhang, F. (2020). Breast cancer histopathological image classification using a hybrid deep neural network. Methods, 173(2019), 52–60. https://doi.org/10.1016/j.ymeth.2019.06.014
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