Meta-Heuristic Feature Optimization for ontology-based data security in a campus workplace with robotic assistance.
BACKGROUND: For campus workplace secure text mining, robotic assistance with feature optimization is essential. The space model of the vector is usually used to represent texts. Besides, there are still two drawbacks to this basic approach: the curse and lack of semantic knowledge. OBJECTIVES: This...
| Publicado en: | Work Vol. 68; no. 3; pp. 913 - 923 |
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| Autores principales: | , , , , , |
| Formato: | algorithm computer program equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160235329&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160235329 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: 2021 vid: 68 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 160235329 148853043 160235329 160235329 10.3233/WOR-203425 160235329 ppf: 913 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Meta-Heuristic Feature Optimization for ontology-based data security in a campus workplace with robotic assistance. aug: au: Gong, Suning Dinesh Jackson Samuel, R. Pandian, Sanjeevi Kumar, Priyan Malarvizhi Pandey, Hari Mohan Srivastava, Gautam affil: School of Civil Engineering, Nantong Institute of Technology, Nantong, China sug: subj: Robotics Utilization Ontologies Data Security Methods Work Environment Evaluation Data Mining Methods Human Semantics Knowledge Algorithms Artificial Intelligence Information Retrieval Software Design Machine Learning Validity ab: BACKGROUND: For campus workplace secure text mining, robotic assistance with feature optimization is essential. The space model of the vector is usually used to represent texts. Besides, there are still two drawbacks to this basic approach: the curse and lack of semantic knowledge. OBJECTIVES: This paper proposes a new Meta-Heuristic Feature Optimization (MHFO) method for data security in the campus workplace with robotic assistance. Firstly, the terms of the space vector model have been mapped to the concepts of data protection ontology, which statistically calculate conceptual frequency weights by term various weights. Furthermore, according to the designs of data protection ontology, the weight of theoretical identification is allocated. The dimensionality of functional areas is reduced significantly by combining standard frequency weights and weights based on data protection ontology. In addition, semantic knowledge is integrated into this process. RESULTS: The results show that the development of the characteristics of this process significantly improves campus workplace secure text mining. CONCLUSION: The experimental results show that the development of the features of the concept hierarchy structure process significantly enhances data security of campus workplace text mining with robotic assistance. pubtype: Academic Journal doctype: algorithm computer program equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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