Dear Kavita Kumari ,

Soft computing techniques have been used for a long time to solve optimization problems. Various disciplines provide real-life problems which are difficult to solve, at least mathematically, in principle; therefore soft.

https://www.sciencedirect.com/topics/computer-science/soft-computing-technique

Regards,

Shafagat

When dealing with large volumes of uncertain data, several soft computing methods can be effective. Fuzzy Logic is well-suited for handling data with vagueness and imprecision, while Neural Networks, particularly Deep Learning, excel at processing complex and uncertain datasets. Bayesian Networks are valuable for modeling uncertainty through probability distributions, while Evolutionary Algorithms can optimize solutions in noisy and uncertain environments. Other methods, such as Rough Sets, Probabilistic Logic, Swarm Intelligence, Case-Based Reasoning, and Interval Computations, offer additional tools for addressing uncertainty. The choice of the most suitable method depends on the specific characteristics of the data and the problem at hand, often involving a combination of these techniques or hybrid approaches for optimal results in uncertain data analysis.