Paper Contents
Abstract
Soft computing is a synthesis of various computation methods. It is becoming more popular among various researchers and organisations as the demand for high-quality software and changing business rules grows. Soft computing techniques are used to generate results and analyses that measure capricious human mind phenomena such as partial truth, belief uncertainty, and approximation. The paper provides an overview of soft computing techniques such as Artificial Neural Networks (ANN), Fuzzy Logic Systems (FLS), Radial Basis Function (RBF), and others. With the help of the Neuphron open source framework, a soft computing framework has been proposed to predict software quality by calculating error in weights of different nodes of a Multi-layer perceptron (MLP) neural network. So even though soft computing is an evolving set of methodologies, this review not only reveals a promising direction for soft computing by incorporating deep learning, but also makes some suggestions for improving the performance of deep learning with soft computing techniques.
Copyright
Copyright © 2023 M. Renuka Devi, S. Anil Kumar, R.Abinaya. This is an open access article distributed under the Creative Commons Attribution License.
