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Huang, C.-J., Wang, Y.-W., Huang, T.-H., Lin, C.-F., Li, C.-Y. and Chen, H.-M. (2011) Applications of Machine Learning Techniques to a Sensor-Network-Based Prosthesis Training System. Applied Soft Computing, 11, 3229-3237.
http://dx.doi.org/10.1016/j.asoc.2010.12.025

has been cited by the following article:

• TITLE: Intelligent Dynamic Aging Approaches in Web Proxy Cache Replacement

  AUTHORS: Waleed Ali, Siti Mariyam Shamsuddin

  KEYWORDS: Cache Replacement, Web Proxy Server, Dynamic-Aging Approaches, Machine Learning

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.7 No.4, November 13, 2015

  ABSTRACT: One of commonly used approach to enhance the Web performance is Web proxy caching technique. In Web proxy caching, Least-Frequently-Used-Dynamic-Aging (LFU-DA) is one of the common proxy cache replacement methods, which is widely used in Web proxy cache management. LFU-DA accomplishes a superior byte hit ratio compared to other Web proxy cache replacement algorithms. However, LFU-DA may suffer in hit ratio measure. Therefore, in this paper, LFU-DA is enhanced using popular supervised machine learning techniques such as a support vector machine (SVM), a naive Bayes classifier (NB) and a decision tree (C4.5). SVM, NB and C4.5 are trained from Web proxy logs files and then intelligently incorporated with LFU-DA to form Intelligent Dynamic- Aging (DA) approaches. The simulation results revealed that the proposed intelligent Dynamic- Aging approaches considerably improved the performances in terms of hit and byte hit ratio of the conventional LFU-DA on a range of real datasets.