Title: Recent advances of statistics in computational intelligence (RASCI)
Abstract: Computational intelligence is usually considered to be the capability of a computer to learn a specific task from data or experimental observation.Computational intelligence plays a pivotal role in solving real-world complex problems.One of the main objectives of computational intelligence models is to discover the statistical disciplines hidden in the data sets.The most significant theoretical fundamental in developing different computational intelligence models is considered to be statistical analysis.There is a continuous scientific attempt to get better approaching into the cryptic information underneath the huge stack of statistical model and data that we come across to solve a specific problem.As a result, there has been a major change towards quantitative analysis of statistical methods as well as data through various computational approaches.Computational approaches that have been extremely popular and found significant application include Neural Network (NN) which provides with a representation framework for statistical construct, regression models that are based on pure statistical theory, Support Vector Machine (SVM) is based on framework of statistical learning, Bayesian method is also regarded as part of statistics, etc.On the whole, all the major tasks of computational intelligence can be represented as statistical methods and these methods can be explained from a statistical point of view.