An adaptive neuro-fuzzy inference for blockchain-based smart job recommendation system. (5th May 2022)

• Record Type: Journal Article
• Authors: Ephzibah, E.P.; Sujatha, R.; Chatterjee, Jyotir Moy
• Is Part Of: International journal of information and decision sciences. Volume 14:Number 1(2022)
• Page Start: 1
• Page End: 14
• Publication Date: 2022-05-05
• Subjects: blockchain -- distributed database -- peer to peer network -- job recommendation system -- unsecured message transmission -- unauthenticated data -- time-consuming search -- incremental hashing -- classification model -- adaptive neuro-fuzzy inference system -- ANFIS; Decision making -- Periodicals; Decision support systems -- Periodicals; Management science -- Periodicals; 658.40305
• Languages: English
• ISSNs: 1756-7017

Abstract

Blockchain is a technology that supports secured transaction in a public distributed database. It maintains a peer-to-peer network where a transaction cannot be modified or tampered by unauthenticated users. It provides a safe message transfer from a sender to a receiver. Job recommendation is an online system that provides a mapping between the job seeker and the employer. This paper proposes a public blockchain of job recommendations based on incremental hashing. The examinations show that this blockchain job recommendation provides process integrity, traceability, security, high levels of transparency, drastic reduction in operational cost and high standard and systematic. The system has two stages. Firstly, using blockchain technology, the authenticated data is fetched. Secondly, a classification model using adaptive neuro-fuzzy inference system is built for mapping the job seeker to the recruiter. This approach proves to be authenticated as well as a smart job recommendation system.