Hybrid PSO-ANFIS for Speaker Recognition

DyHAP: Dynamic Hybrid ANFIS-PSO Approach for Predicting Mobile Malware

To deal with the large number of malicious mobile applications (e.g. mobile malware), a number of malware detection systems have been proposed in the literature. In this paper, we propose a hybrid method to find the optimum parameters that can be used to facilitate mobile malware identification. We also present a multi agent system architecture comprising three system agents (i.e. sniffer, extr...

Performance evaluation of gang saw using hybrid ANFIS-DE and hybrid ANFIS-PSO algorithms

One of the most significant and effective criteria in the process of cutting dimensional rocks using the gang saw is the maximum energy consumption rate of the machine, and its accurate prediction and estimation can help designers and owners of this industry to achieve an optimal and economic process. In the present research work, it is attempted to study and provide models for predicting the m...

EVELOPMENT OF ANFIS-PSO, SVR-PSO, AND ANN-PSO HYBRID INTELLIGENT MODELS FOR PREDICTING THE COMPRESSIVE STRENGTH OF CONCRETE

Concrete is the second most consumed material after water and the most widely used construction material in the world. The compressive strength of concrete is one of its most important mechanical properties, which highly depends on its mix design. The present study uses the intelligent methods with instance-based learning ability to predict the compressive strength of concrete. To achieve this ...

Hybrid Modelling in Speaker Recognition

Hybrid ANFIS-PSO approach for predicting optimum parameters of a protective spur dike

In this study a new approach was proposed to determine optimum parameters of a protective spur dike to mitigate scouring depth amount around existing main spur dikes. The studied parameters were angle of the protective spur dike relative to the flume wall, its length, and its distance from the main spur dikes, flow intensity, and the diameters of the sediment particles that were explored to fin...