Open Access
E3S Web Conf.
Volume 426, 2023
The 5th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2023)
Article Number 01041
Number of page(s) 8
Section Integrated Sustainable Science and Technology Innovation
Published online 15 September 2023
  1. S. H. Nam, Y. H. Kim, N. Q. Truong, J. Choi, K. R. Park, “Age Estimation by Super-Resolution Reconstruction Based on Adversarial Networks”, in IEEE Access, vol. 8, pp. 17103-17120, 2020, doi: 10.1109/ACCESS.2020.2967800 (2020) [CrossRef] [Google Scholar]
  2. K. R. Hassan, I. H. Ali, “Age and Gender Classification using Multiple Convolutional Neural Network”, 2020 IOP Conf. Ser.: Mater. Sci. Eng. 928 032039 (2020) [CrossRef] [Google Scholar]
  3. K. E. L. Karazle, V. Raman, P. Then, “Facial Age Estimation Using Machine Learning Techniques: An Overview”, Big Data and Cognitive Computing, vol. 6, no. 4, p. 128, Oct. 2022, doi: 10.3390/bdcc6040128 (2022) [CrossRef] [Google Scholar]
  4. Roushdy, Mohamed. “Hyper-Parameter Optimization of Convolutional Neural Network Based on Particle Swarm Optimization Algorithm.” Bulletin of Electrical Engineering and Informatics (2021). [Google Scholar]
  5. S. M. S. Uddin, M. S. Morshed, M. I. Prottoy, A. B. M. A. Rahman, “Age Estimation from Facial Images using Transfer Learning and K-fold Cross- Validation”, In PRIS ‘21: Proceedings of the 2021 International Conference on Pattern Recognition and Intelligent Systems, July, 33-36. (2021) [Google Scholar]
  6. M. Y. Shakor, N. M. S. Surameery, “CNN-Based Transfer Learning for 3D Knuckle Recognition”, Advances in Multimedia, vol. 2023, Article ID 6147422, 12 pages, 2023. (2023) [Google Scholar]
  7. S. Y. Prasetyo, G. Z. Nabiilah, Z. N. Izdihar, Nurhasanah, “Age Estimation from Face Image using Discrete Cosine Transform Feature and Artificial Neural Network”, 2022 5th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia, 2022, pp. 660-665, doi: 10.1109/ISRITI56927.2022.10052812 (2022) [Google Scholar]
  8. B. Navaneeth, M. Suchetha, “PSO optimized 1-D CNN-SVM architecture for real-time detection and classification applications”, Computers in Biology and Medicine. doi:10.1016/j.compbiomed.2019.03.017 (2019) [Google Scholar]
  9. A. B. P. Utama, A. P. Wibawa, Muladi, A. Nafalski, “PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis. Jurnal Online Informatika”, 7(2), 193-202. doi: 10.15575/join.v7i2.858 (2022) [CrossRef] [Google Scholar]
  10. C. J. Lin, C. H. Lin, C. C. Sun, S. H. Wang, “Evolutionary-Fuzzy-Integral-Based Convolutional Neural Networks for Facial Image Classification”, Electronics, vol. 8, no. 9, p. 997, Sep. 2019, doi: 10.3390/electronics8090997 (2019) [CrossRef] [Google Scholar]
  11. Poojary, R., & Pai, A. Comparative Study of Model Optimization Techniques in Fine-Tuned CNN Models. 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA). doi:10.1109/icecta48151.2019.8959681 (2019) [Google Scholar]
  12. Y. Khourdifil, M. Bahaj, “Heart Disease Prediction and Classification Using Machine Learning Algorithms Optimized by Particle Swarm Optimization and Ant Colony Optimization”, International Journal of Intelligent Engineering and Systems 12.1 (2019): 242-252 (2019) [CrossRef] [Google Scholar]
  13. T. O. Hodson, “Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not”, Geoscientific Model Development, vol. 15, no. 14, pp. 5481-5487, doi: 10.5194/gmd-15-5481-2022. (2022) [CrossRef] [Google Scholar]
  14. Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Huang, S., Brooks, M., ... & Asadi, H. Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods. American Journal of Roentgenology, 212(1), 38-43. (2019) [CrossRef] [PubMed] [Google Scholar]
  15. D. R. Kendari, T. R. Gadekallu, “Federated Learning Approach for Early Detection of Chest Lesion Caused by COVID-19 Infection Using Particle Swarm Optimization”, Electronics 12, no. 3: 710. (2023) [CrossRef] [Google Scholar]

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