Author classification using transfer learning and predicting stars in co-author networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Abbasi, Rashid | - |
dc.contributor.author | Kashif Bashir, Ali | - |
dc.contributor.author | Chen, Jianwen | - |
dc.contributor.author | Mateen, Abdul | - |
dc.contributor.author | Piran, Jalil | - |
dc.contributor.author | Amin, Farhan | - |
dc.contributor.author | Luo, Bin | - |
dc.date.available | 2021-04-16T02:40:44Z | - |
dc.date.created | 2021-04-16 | - |
dc.date.issued | 2021-03 | - |
dc.identifier.issn | 0038-0644 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80761 | - |
dc.description.abstract | The vast amount of data is key challenge to mine a new scholar that is plausible to be star in the upcoming period. The enormous amount of unstructured data raise every year is infeasible for traditional learning; consequently, we need a high quality of preprocessing technique to expand the performance of traditional learning. We have persuaded a novel approach, Authors classification algorithm using Transfer Learning (ACTL) to learn new task on target area to mine the external knowledge from the source domain. Comprehensive experimental outcomes on real-world networks showed that ACTL, Node-based Influence Predicting Stars, Corresponding Authors Mutual Influence based on Predicting Stars, and Specific Topic Domain-based Predicting Stars enhanced the node classification accuracy as well as predicting rising stars to compared with contemporary baseline methods. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.relation.isPartOf | SOFTWARE-PRACTICE & EXPERIENCE | - |
dc.title | Author classification using transfer learning and predicting stars in co-author networks | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000573330600001 | - |
dc.identifier.doi | 10.1002/spe.2884 | - |
dc.identifier.bibliographicCitation | SOFTWARE-PRACTICE & EXPERIENCE, v.51, no.3, pp.645 - 669 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85091604480 | - |
dc.citation.endPage | 669 | - |
dc.citation.startPage | 645 | - |
dc.citation.title | SOFTWARE-PRACTICE & EXPERIENCE | - |
dc.citation.volume | 51 | - |
dc.citation.number | 3 | - |
dc.contributor.affiliatedAuthor | Amin, Farhan | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | author classification | - |
dc.subject.keywordAuthor | semantic web | - |
dc.subject.keywordAuthor | social network | - |
dc.subject.keywordAuthor | transfer learning | - |
dc.subject.keywordPlus | EMOTION CLASSIFICATION | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | RANKING | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | TEXT | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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