Soft computing for intelligent data analysis (original) (raw)

A Review of Soft Computing Techniques and Applications

INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) ICRADL – 2021, 2021

Soft Computing can be defined as a science of thinking, reasoning that helps to deal with complex systems. Its main aim is to develop intelligent machines in order to solve realworld problems. It differs from the conventional hard computing as it can handle uncertainty, imprecision easily. It includes use of different techniques such as machine learning, artificial neural networks etc. that can be used together for solving complex problems that are difficult to tackle using conventional models of mathematics. These techniques play a vital role in identifying hidden patterns from the data and doing the classification for making intelligent decisions. This paper reviews some of the soft computing techniques and its applications.

SOFT COMPUTING AND ITS APPLICATIONS AS THE STATE-OF-THE-ART TECHNIQUE FOR CONTEMPORARY RESEARCH

IAEME PUBLICATION, 2020

Technological development in information system ensues because of hybrid intelligent systems in soft computing. Hybrid intelligent system is a kind of system which engages a blend of artificial intelligence subfield procedures and practices. Soft computing speaks about the confidence of computational techniques in various disciplines, which challenges in education, modeling, and investigating complex problems. High complexity soft computing applications have been brought as zero complexity due to the advancement of technological development in this era. This research article deals with the insight of soft computing branches, research applications and hybrid intelligent system that produces zero complexity which will create an inspiration to new researchers.

Soft-Computing Based Data Mining : A Review

2015

Soft computing is a consortium of methodologies that works synergistically and provides, in one form or another, flexible information processing capability for handling real-life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. The guiding principle is to devise methods of computation that lead to an acceptable solution at low cost by seeking for an approximate solution to an imprecisely/precisely formulated problem. Soft computing methodologies (involving fuzzy sets, neural networks, genetic algorithms, and rough sets) are most widely applied in the data mining step of the overall KDD process. Fuzzy sets provide a natural framework for the process in dealing with uncertainty. Neural networks and rough sets are widely used for classification and rule generation. Genetic algorithms (GAs) are involved in various optimization and search p...