Advances in Soft Computing models and its applications
Call for Papers
Deadline: 15 November 2019
International Journal of Computers and Applications
The International Journal of Computers and Applications (IJCA) is a unique platform for publishing novel ideas, research outcomes and fundamental advances in all aspects of Computer Science, Computer Engineering, and Computer Applications.
In the field of computer applications, soft computing is the use of inexact solutions to computationally hard tasks such as the solution of computationally complex problems, for which there is no known algorithm that can compute an exact solution if its inputs are represented by polynomial expressions. Soft computing differs from conventional computing in that, unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind.
The principal constituents of Soft Computing are Fuzzy Logic, Evolutionary Computation, Machine Learning and Probabilistic Reasoning, with the latter subsuming belief networks and parts of learning theory.
1. Fuzzy Logic: It is based on the observation that people make decisions based on imprecise and non-numerical information. These models have the capability of recognizing, representing, manipulating, interpreting, and utilizing data and information that are vague and lack certainty
2. Evolutionary Computation: It is a family of algorithms for global optimization inspired by biological evolution. Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings.
3. Machine Learning: Machine learning algorithms are used in a wide variety of applications, such as big data analysis, and image processing, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers.
4. Probabilistic Reasoning: The aim of a probabilistic logic is to combine the capacity of probability theory to handle uncertainty with the capacity of deductive logic to exploit the structure of formal argument. Probabilistic logics attempt to find a natural extension of traditional logic truth tables: the results they define are derived through probabilistic expressions instead.
Generally, soft computing techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems, such as sentential logic and predicate logic, or rely heavily on computer-aided numerical analysis. Soft computing techniques are intended to complement each other.
This special issue is aimed towards innovative research works in all the categories of soft computing, such as Fuzzy Logic, Evolutionary Computation, Machine Learning and Probabilistic Reasoning along with new strategies for information analysis and architectures:
Evolutionary Computation Systems:
1. Web Semantics using Evolutionary Computation systems
2. Visual and Audio using Soft Computing techniques
3. Feature reduction using Evolutionary Optimization systems
4. Fault tolerance/ Noise removal by Evolutionary Computation systems
5. Congestion Control and load balancing in cloud/network based on Evolutionary Computation systems
6. Network architecture and its routing strategy using Evolutionary Computation systems
7. Autonomic, High performance, utility - peer to peer architecture based on Evolutionary Computation systems
8. Energy and resource aware cloud/network based on Evolutionary Computation
Other Soft Computing Systems:
9. Bio medical Soft Computing systems
10. Decision Support systems based on soft computing
11. Artificial Intelligence / Machine learning based information systems
12. Similarity/ Clustering based soft computing techniques
13. Data/ Image/Multimedia retrieval using soft computing techniques.
14. Time-series analysis using Soft computing methods
15. Fault tolerance using soft computing models
16. System modelling and Identification using soft computing strategies
17. Pattern recognition or mining using soft computing techniques