Topical Collection on Fuzzy inference, neuro-fuzzy, and emerging fuzzy hybridization systems
Aims and Scope
Inference systems apply logical rules to the knowledge base to deduce new information. As a mode to conduct information that is not accurate, but rather uncertain, fuzzy sets provide conventional inference systems with a power to approximate human reasoning capabilities with the uncertainties associated with human cognitive processes. As a consequence, fuzzy inference systems have been successfully applied to a wide range of benchmark and real-world application problems.
The hybridization of fuzzy inference systems with related techniques, such as neural networks, granular computing and genetic algorithms can synergize the fuzzy reasoning process with the connectionist structure, information granules and evolving parameter learning. The joint effort made by different computational paradigms leads to enhanced performance that can target the biggest challenges in many fields, such as engineering, cyber-physical systems, and health care.
The aim of this special issue is to showcase state-of-the-art works in the field of fuzzy inference systems that are synergized with other computational intelligence techniques, such as neural networks, granular computing and genetic algorithms, in an effort to inspire the development of the next generation of fuzzy inference systems.
This collection will involve topic on but not limited to:
- Fuzzy/rough Logic
- Fuzzy Inference Systems
- Neuro-Fuzzy Systems
- Evolving Fuzzy Systems
- Information Granules
- Fuzzified Computational Intelligence Systems and their application in:
- Control Engineering
- Cyber-physical systems
- Diagnosis, prediction and other decision making
- Risk assessment
- Recommendation systems
- HealthcareComputer vision and graphics
- Expert systems
- Cyber security privacy and forensics
Longzhi Yang (Lead Guest Editor), Northumbria University, UK, Longzhi.email@example.com
Vijayakumar Varadarajan, The University of New South Wales, Sydney, Australia, firstname.lastname@example.org
Yanpeng Qu, Dalian Maritime University, China, email@example.com
Deadline for submissions: extended to 28th February 2021
Peer Review Process
All the papers will go through a double blind review process and will be reviewed by at least two reviewers. A thorough check will be done and the guest editors will check any significant similarity between the manuscript under consideration and any published paper or submitted manuscripts of which they are aware. In such case, the article will be directly rejected without proceeding further. Guest editors will make all reasonable effort to receive the reviewer’s comments and recommendation on time.
The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended. At least 30% of new content is expected.
Paper submissions for the special issue should follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines). Each manuscript should not exceed 16 pages in length (inclusive of figures and tables).
Authors should select ‘SI: Neuro, fuzzy and their Hybridization' during the submission step 'Additional Information'.