Applications of soft computing in the field of analytics are becoming increasingly important, e.g. for fully automated systems that help in accurate identification of abnormalities or support good management among systems.
During the previous decade, soft computing has emerged as a potential candidate for solving complex and intricate global optimisation problems which would otherwise be difficult to solve by traditional methods. Currently, image processing, signal processing, industrial optimisation, control system applications and power system application fields have challenging needs which are to be unraveled by researchers.
Some popular soft computing techniques for machine learning and global optimisation include artificial neural networks, fuzzy logic, genetic algorithms (GA), differential evolution (DE), ant colony optimisation (ACO), particle swarm optimisation (PSO), artificial bee colony (ABC), firefly algorithm (FFA) algorithm etc., these methods that have been successfully applied to a wide range of benchmark and real-world application problems.
This special issue is an ideal platform for researchers to come up with innovative ideas and approaches in the area of data mining using these applied soft computing strategies. This issue gains much importance since it directly impacts business analytics.
Suitable topics include, but are not limited to, the following:
- Fuzzy c-means algorithm
- Optimised problem definition
- Machine learning based system design
- Optimised modelling for analytic tools
- Intelligent and automation in soft computing for data mining
- Artificial neural network operation for non-linear data approximation
- Evolutionary approach
- Multivariate data manipulation
- Solving uncertainty nature
- Lagrangian differentiation
- Multi dimension data analytics
- Multi point business analytics for scattered data
- K- means analytics
- Big data analytics for multi dimension data, etc.
Submission of manuscripts: 30 May, 2017