Most of the Soft Computing algorithms will learn from human knowledge and mimic human skills. We survey the principal constituents of soft computing techniques including Fuzzy Logic, Artificial Neural Networks, Support Vector Machines, Probabilistic Reasoning, Genetic Algorithms and Multi-Variate Adaptive Regressive Splines. Soft Computing techniques are being widely used by the IDS community d...

Medical image mining is one of the most rewarding and challenging field of application in data mining and knowledge discovery. Soft computing is a consortium of methodologies that provides flexible information processing capability. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and limited truth in order to achieve tractability, robustness, and low-cos...

This study investigates the prediction model of compressive strength of self–compacting concrete (SCC) by utilizing soft computing techniques. The techniques consist of adaptive neuro–based fuzzy inference system (ANFIS), artificial neural network (ANN) and the hybrid of particle swarm optimization with passive congregation (PSOPC) and ANFIS called PSOPC–ANFIS. Their perf...

Soft computing techniques are becoming popular in designing real world applications. Researchers are trying to integrate different soft computing paradigms such as fuzzy logic, artificial neural network, genetic algorithms, decision trees etc. to develop hybrid intelligent autonomous classification systems that provide more flexibility by exploiting tolerance and uncertainty of real life situat...