@article {779,
	title = {Knowledge-Intensive Decision Support System for Manufacturing Equipment Maintenance},
	journal = {Journal of Digital Information Management},
	volume = {18},
	year = {2020},
	month = {06/2020},
	pages = {85-98},
	type = {Research},
	chapter = {85},
	abstract = {<p><span style="color: rgb(51, 51, 51); font-family: Arial, Helvetica, sans-serif;">To ensure continuous production in industrial plants, the high valued manufacturing eqipments should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and reparation must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and maintenance. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Manufacturing Equipment Maintenance in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of case based Reasoning and ontology, the Ki-DSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our approach and the benefit of the ontology support.</span></p>
},
	doi = {https://doi.org/10.6025/jdim/2020/18/3/85-98},
	url = {http://dline.info/fpaper/jdim/v18i3/jdimv18i3_1.pdf},
	author = {Djamila Bouhalouan and Bakhta Nachet and Abdelkader Adla}
}
@article {637,
	title = {Knowledge Management: Tool for Enhancing HRM Practices and Organizational Innovation},
	journal = {Progress in Machines and Systems},
	volume = {7},
	year = {2018},
	pages = {28-36},
	type = {Research},
	author = {Mueen Uddin}
}
@article {523,
	title = {Keyword extraction from Chinese text based on multidimensional weighted features},
	journal = {Journal of Digital Information Management},
	volume = {14},
	year = {2016},
	note = {Cited By :1Export Date: 22 February 2018},
	month = {2016},
	pages = {177 - 183},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982833307\&partnerID=40\&md5=21b1c4d3d08f73e83fe646df1ec10e91},
	author = {Jian, Y.}
}
@article {557,
	title = {Kernel fuzzy C-means clustering for word sense disambiguation in biomedical texts},
	journal = {Journal of Digital Information Management},
	volume = {13},
	year = {2015},
	note = {Cited By :5Export Date: 22 February 2018},
	month = {2015},
	pages = {411 - 420},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959203977\&partnerID=40\&md5=9bc27e007ec898b6bf5ebe7335ceb612},
	author = {Ren, K. and Ren, Y.F.}
}
@article {238,
	title = {Knowledge management of 1D SDS PAGE gel protein image information},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Export Date: 10 July 2014},
	month = {2010},
	pages = {223 - 232},
	abstract = {Computational management and analysis of proteomics data is providing important insights into the biological and clinical sciences. In this research study, a knowledge management strategy for 1D SDS PAGE gel protein image information has been developed and implemented in the New Vision to Protein intensity Analysis System (NeVPAS) application. This application focused on the analysis of skin mucus protein changes during a key developmental stage of Atlantic salmon (Salmo salar), the smoltification period. It includes a database to archive proteomics data. In this paper, knowledge management is expressed in terms of data acquisition, hierarchical organisation of data, knowledge retrieval by means of an information retrieval technique and knowledge presentation using text and graphics. This application of proteomics knowledge management in the marine field may also be extended to clinical analysis and environmental monitoring.},
	keywords = {1D SDS PAGE protein gel image, Atlantic salmon, Gel image analysis, Knowledge management},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960692176\&partnerID=40\&md5=be17770f0ee0ad9cba603f903cdd0aee},
	author = {Ramaswamy, G. and Wu, B. and Macevilly, U.}
}
@article {84,
	title = {Knowledge-based support for object-oriented software design and synthesis: A category theoretic approach},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2007},
	pages = {115 - 122},
	abstract = {To reuse previous knowledge of objectoriented design and adapt them to solve new problems, the collaboration relationships and the responsibility distribution among software objects need to be thoroughly understood and precisely formulated. The paper proposes a knowledge-based approach that employs category theoretic models to formalize and mechanize objectoriented software design and synthesis by focusing concern on reasoning about the interdependency relationships at different levels of abstraction and granularity. The major benefit of our approach is twofold: First, it provides an explicit semantics for formal object-oriented specifications, and therefore enables a high-level of reusability and dynamic adaptability. Second, it utilizes the ability of categorical computations to support automated software composition and refinement. A prototype tool that demonstrates the feasibility and effectiveness of our approach is also presented.},
	keywords = {Automated software, Object oriented design, Software metrics, Software quality},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350673897\&partnerID=40\&md5=de1990a30fe9efe685a0df6e39e37b82},
	author = {Zheng, Y. and Xue, J. and Hu, Q.}
}
