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Article Citation - WoS: 1Citation - Scopus: 2Potential of Support-Vector Regression for Forecasting Stream Flow(Univ Osijek, Tech Fac, 2014) Radzi, Mohd Rashid Bin Mohd; Shamshirband, Shahaboddin; Aghabozorgi, Saeed; Misra, Sanjay; Akib, Shatirah; Kiah, Laiha Mat; Computer EngineeringStream flow is an important input for hydrology studies because it determines the water variability and magnitude of a river. Water resources engineering always deals with historical data and tries to estimate the forecasting records in order to give a better prediction for any water resources applications, such as designing the water potential of hydroelectric dams, estimating low flow, and maintaining the water supply. This paper presents three soft-computing approaches for dealing with these issues, i.e. artificial neural networks (ANNs), adaptive-neuro-fuzzy inference systems (ANFISs), and support vector machines (SVMs). Telom River, located in the Cameron Highlands district of Pahang, Malaysia, was used in making the estimation. The Telom River's daily mean discharge records, such as rainfall and river-level data, were used for the period of March 1984-January 2013 for training, testing, and validating the selected models. The SVM approach provided better results than ANFIS and ANNs in estimating the daily mean fluctuation of the stream's flow.Article Citation - WoS: 9Citation - Scopus: 22Anomaly Detection Using Fuzzy Q-Learning Algorithm(Budapest Tech, 2014) Shamshirband, Shahaboddin; Anuar, Nor Badrul; Kiah, Miss Laiha Mat; Misra, Sanjay; Computer EngineeringWireless networks are increasingly overwhelmed by Distributed Denial of Service (DDoS) attacks by generating flooding packets that exhaust critical computing and communication resources of a victim's mobile device within a very short period of time. This must be protected. Effective detection of DDoS attacks requires an adaptive learning classifier, with less computational complexity, and an accurate decision making to stunt such attacks. In this paper, we propose an intrusion detection system called Fuzzy Q-learning (FQL) algorithm to protect wireless nodes within the network and target nodes from DDoS attacks to identify the attack patterns and take appropriate countermeasures. The FQL algorithm was trained and tested to establish its performance by generating attacks from the NSL-KDD and CAIDA DDoS Attack datasets during the simulation experiments. Experimental results show that the proposed FQL IDS has higher accuracy of detection rate than Fuzzy Logic Controller and Q-learning algorithm alone.
