Browse > Article
http://dx.doi.org/10.6109/jkiice.2021.25.2.165

Sensor Data Collection & Refining System for Machine Learning-Based Cloud  

Hwang, Chi-Gon (Dept. of Computer Engineering, IIT, Kwangwoon University)
Yoon, Chang-Pyo (Dept Of Computer & Mobile Convergence, GyeongGi University of Science and Technology)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.2, 2021 , pp. 165-170 More about this Journal
Abstract
Machine learning has recently been applied to research in most areas. This is because the results of machine learning are not determined, but the learning of input data creates the objective function, which enables the determination of new data. In addition, the increase in accumulated data affects the accuracy of machine learning results. The data collected here is an important factor in machine learning. The proposed system is a convergence system of cloud systems and local fog systems for service delivery. Thus, the cloud system provides machine learning and infrastructure for services, while the fog system is located in the middle of the cloud and the user to collect and refine data. The data for this application shall be based on the Sensitive data generated by smart devices. The machine learning technique applied to this system uses SVM algorithm for classification and RNN algorithm for status recognition.
Keywords
Cloud system; Fog system; Machine learning; Support vector marchine(SVM); Recurrent neural network(RNN);
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. D. Cai, P. W. Ricardo, C. H. Jen, and K. C. Chou, "Application of SVM to predict membrane protein types," Journal of theoretical biology, vol. 226, no. 4, pp. 373-376, 2004.   DOI
2 D. Zhang, "Support Vector Machine," in Fundamentals of Image Data Mining, Springer International Pub., ch. 8, pp. 179-205, 2019.
3 M. N. Murty and R. Raghava, "Kernel-based SVM," Support vector machines and perceptrons, Springer, pp. 57-67, 2016.
4 Understanding SVM[Internet]. Available: https://opencvpython-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_svm/py_svm_basics/py_svm_basics.html.
5 S. Li, W. Li, C. Cook, C. Zhu, and Y.Gao, "Independently recurrent neural network (indrnn): Building a longer and deeper rnn," in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5457-5466, 2018.
6 G. Y. Lim and Y. B. Cho, "Dynamic RNN-CNN malware classifier correspond with Random Dimension Input Data," Journal of the Korea Institute of Information and Communication Engineering, vol. 23, no. 5, pp. 533-539, May. 2019.   DOI
7 B. Yoshua, "Learning deep architectures for AI," Foundations and trends in Machine Learning, vol. 2, no. 1, pp. 1-127, 2009.   DOI
8 R. Sheikhpour, M. A. Sarram, S. Gharaghani, and M. A. Z. Chahooki, "A Survey on semi-supervised feature selection methods," Pattern Recognition, vol. 64, pp. 141-158, Apr. 2017.   DOI
9 A. M. Abd and S. M. Abd, "Modelling the strength of lightweight foamed concrete using support vector machine (SVM)," Case studies in construction materials, vol. 6, pp. 8-15, 2017.   DOI
10 A. Sherstinsky, "Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network," Physica D: Nonlinear Phenomena, vol. 404, 2020.
11 M. Aazam and E. N. Huh, "Fog Computing and Smart Gateway Based Communication for Cloud of Things," International Conference on Future Internet of Things and Cloud(FiCloud) 2014, International Conference on IEEE, pp. 464-470, Dec. 2014.
12 S. Singh and I. Chana, "A survey on resource scheduling in cloud computing: Issues and challenges," Journal of grid computing, vol. 14, no. 2, pp. 217-264, 2016.   DOI