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Enhanced Machine Learning Algorithms: Deep Learning, Reinforcement Learning, and Q-Learning

  • Park, Ji Su (Dept. of Computer Science and Engineering, Jeonju University) ;
  • Park, Jong Hyuk (Dept. of Computer Science and Engineering, Seoul National University of Science & Technology (SeoulTech))
  • Received : 2020.10.03
  • Accepted : 2020.10.22
  • Published : 2020.10.31

Abstract

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

Keywords

References

  1. S. Ma, W. Liu, C. You, S. Jia, and Y. Wu, "An improved defect detection algorithm of jean fabric based on optimized Gabor filter," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1008-1014, 2020. https://doi.org/10.3745/JIPS.02.0140
  2. C. Ren, D. K. Kim, and D. Jeong, "A survey of deep learning in agriculture: techniques and their applications," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1015-1033, 2020. https://doi.org/10.3745/JIPS.04.0187
  3. G. C. Yang, "Next-generation personal authentication scheme based on EEG signal and deep learning," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1034-1047, 2020. https://doi.org/10.3745/JIPS.03.0147
  4. S. Cho, "Rate adaptation with Q-learning in CSMA/CA wireless networks," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1048-1063, 2020. https://doi.org/10.3745/JIPS.03.0148
  5. L. Wang, C. Jin, and C. Xu, "An evaluative study of the operational safety of high-speed railway stations based on IEM-Fuzzy comprehensive assessment theory," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1064-1073, 2020. https://doi.org/10.3745/JIPS.04.0188
  6. D. Lee, "Comparison of reinforcement learning activation functions to improve the performance of the racing game learning agent," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1074-1082, 2020. https://doi.org/10.3745/JIPS.02.0141
  7. G. Xu and S. Zhang, "Fast leaf recognition and retrieval using multi-scale angular description method," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1083-1094, 2020. https://doi.org/10.3745/JIPS.02.0142
  8. X. Wang, F. Wang, Y. Song, G. Zhang, and S. Wang, "Design of intelligent management and service system for gas valve," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1095-1104, 2020. https://doi.org/10.3745/JIPS.04.0189
  9. J. Wen, "Gait recognition based on GF-CNN and metric learning," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1105-1112, 2020. https://doi.org/10.3745/JIPS.02.0143
  10. Y. Ye, X. Sun, M. Liu, J. Mi, T. Yan, and L. Ding, "Intelligent on-demand routing protocol for ad hoc network," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1113-1128, 2020. https://doi.org/10.3745/JIPS.03.0149
  11. F. Yao, "Thangka image inpainting algorithm based on wavelet transform and structural constraints," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1129-1144, 2020. https://doi.org/10.3745/JIPS.02.0144
  12. J. Zhu, F. Yu, G. Liu, M. Sun, D. Zhao, Q. Geng, and J. Su, "Classroom roll-call system based on ResNet networks," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1145-1157, 2020. https://doi.org/10.3745/JIPS.04.0190
  13. L. Zhao and K. Zhang, "Localization algorithm for wireless sensor networks based on modified distance estimation," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1158-1168, 2020. https://doi.org/10.3745/JIPS.03.0150
  14. P. Wang, J. Bai, and J. Meng, "A hybrid genetic ant colony optimization algorithm with an embedded cloud model for continuous optimization," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1169-1182, 2020. https://doi.org/10.3745/JIPS.01.0059
  15. M. T. N. Truong and S. Kim, "A study on visual saliency detection in infrared images using Boolean map approach," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1183-1195, 2020. https://doi.org/10.3745/JIPS.02.0145
  16. S. Malik, I. Ullah, D. Kim, and K. Lee, "Heuristic and statistical prediction algorithms survey for smart environments," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1196-1213, 2020. https://doi.org/10.3745/JIPS.04.0191
  17. S. B. Jang and Y. W. Ko, "An efficient object augmentation scheme for supporting pervasiveness in a mobile augmented reality," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1214-1222, 2020. https://doi.org/10.3745/JIPS.04.0192
  18. S. Woo and Y. Sung, "Dynamic action space handling method for reinforcement learning models," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1223-1230, 2020. https://doi.org/10.3745/JIPS.02.0146