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A Novel Intelligence-based e-Procurement System to offer Maximum Fairness Index in Ongoing Auction Process

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 8 No. 6 (2018)

DOI
10.11591/ijece.v8i6.pp4438-4447
ISSN
2088-8708, 2088-8708
Published
2018-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

A perfect auction policy is one of the most strategic elements that contribute to success factor for any e-Procurement system. An auction policy can be only term as an effective if it really offer win-win situation to both the bidder as well as to the merchant. After reviewing existing studies on e-Procurement system, it is found that there isno effective research work focusing on this point and maximum research contribution has limited its scope to certain application or case studis. Hence, the proposed system introduces a novel e-Procurement system which is equipped by an itelligence-building process for performing predictive analysis of ongoing auction process. A mathematical modelling is implemented where all teh variables have been formed using practical implementation of auction system and followed by optimization process using regression-based approach. The study outcome shows that proposed system offers better response time and higher predictive accuracy in contrast to existing approaches.

Collaboration network of the authors

5 authors · 4 co-authorship links

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  • Bharathiar University
  • Bangalore University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Sangeetha GovindaAffiliation unknown1
Salini SureshBharathiar University1
G. SangeethaBharathiar University1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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