Special Issue on Bridging Metaheuristic and Soft Computing

ficient of Determination and Nash-Sutcliffo Coefficient were used for comparing the prediction ability of the developed models. Four input parameters were Body & texture, aroma & flavour, moisture, and free fatty acid. Neurons in two hidden layers varied from 1 to 20. One output parameter was sensory score. Experiments show that Elman model with combination of 4-17-17-1 performed significantly well for predicting the shelf life of processed cheese stored at 7-8oC. In the third paper Herawan enhanced his previous work to present a relation between rough set and soft set. The idea of rough setbased topological space is proposed to show that a rough set can be considered as a soft set. There are two main steps presented. Firstly, a construction of a quasi-discrete topology using indiscernibility (equivalence) relation in rough set theory is described. Secondly, the paper describes that a general topology is a special case of soft set. It is shown that standard rough set is properly included in soft set. This confirms that soft set theory provides wider study both theoretically and in applications. The contribution of the fourth paper consists in a SPM (Self-learning Pattern Matching) i