This book introduces explainable neutrosophic collaborative intelligence.
Neutrosophic collaborative intelligence is an extension of fuzzy collaborative intelligence, in which a problem is analyzed from diverse perspectives to raise the chance that no relevant aspects of the problem will be ignored. In addition, as internet applications become widespread, dealing with disparate data sources is becoming more and more popular. Technical constraints, security issues, and privacy considerations often limit access to some sources. Furthermore, indeterminacy held by the participants leaves room for ensuring the applicability of the aggregation result.
To meet these challenges, concepts of neutrosophic computing intelligence and collaborative neutrosophic modeling have been proposed, and neutrosophic collaborative intelligence (NCI)-based systems are being established. So far, several studies have argued that for certain problems, a NCI approach is more flexible, precise, accurate, efficient, safe, and private than typical approaches.
This book systematically introduces the methodology, system architecture, and applications of NCI in engineering. Relevant references or real cases are also provided as supporting evidence.

Dr. Chen has published a few papers on neutrosophic or fuzzy collaborative intelligence and systems in prestigious journals such as cognitive computation, applied soft computing, International Journal of Advanced Manufacturing Technology, robotics and computer-integrated manufacturing, complex and intelligent systems, Journal of Intelligent Manufacturing, IEEE Transactions on Systems, Man, and Cybernetics, fuzzy sets and systems, and computers and industrial engineering. He has guest edited special issues on neutrosophic or fuzzy collaborative intelligence and systems in journals such as International Journal of Intelligent Systems and others. Dr. Chen has authored books on “Explainable Fuzzy Collaborative Intelligence: Methodology, System Architecture, and Applications in Engineering,” “Fuzzy Collaborative Forecasting and Clustering,” etc. Dr. Chen is an IET fellow.