Rough Sets Applied to the RoughCast System for Steel Castings

Kluska-Nawarecka, S.; Wilk-Kołodziejczyk, D.; Regulski, K.; Dobrowolski, Grzegorz

doi:10.1007/978-3-642-20042-7_6

Cited by 19 publications

(19 citation statements)

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“…The experimental results confirming that the methods of reasoning used by humans can be represented in the LPR are presented in subsequent papers [4,5]. The objective set by the creators has caused that LPR is significantly different from other known knowledge representation methods mentioned in the Introduction (rule-based systems, classical logic, fuzzy logic, Demster-Shafer theory, Bayesian networks, rough sets).…”

Section: Methodsmentioning

confidence: 52%

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Supporting the Manufacturing Process of Metal Products with the Methods of Artificial Intelligence

Wilk-Kołodziejczyk

2016

Archives of Metallurgy and Materials

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The aim of this research programme was to develop a series of methods and solutions to support the decision-making process in foundry and materials engineering. The specific problems discussed included the selection of methods for data processing and knowledge representation formalisms, backed up by the creation of decision algorithms based on contemporary achievements of artificial intelligence, tailored to the needs of foundry industry and metallurgy. The manufacturing process of metal items is associated with many aspects, which affect the quality of end product. For process engineers responsible for the supervising and planning of production, an important feature is, among others, the diversified nature of numerous aspects of the knowledge acquisition and integration from distributed sources of information which, when made available in an appropriate manner, can support the improvement of manufacturing process.

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Section: Methodsmentioning

confidence: 52%

“…The subsequent articles [2,3,4] present a summary of various formalisms enabling the representation of knowledge incomplete and uncertain coming from distributed sources. Large fragments of these works are related to the presentation of domain knowledge using formalisms applied in the logic of plausible reasoning (LPR).…”

Section: Introductionmentioning

confidence: 99%

Supporting the Manufacturing Process of Metal Products with the Methods of Artificial Intelligence

Wilk-Kołodziejczyk

2016

Archives of Metallurgy and Materials

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“…The minimum subset of attributes is created in the goal of creating the least amount of decision rules [25].…”

Section: Rough Setsmentioning

confidence: 99%

Formalization of Technological Knowledge in the Field of Metallurgy using Document Classification Tools Supported with Semantic Techniques

Regulski

2017

Archives of Metallurgy and Materials

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The process of knowledge formalization is an essential part of decision support systems development. Creating a technological knowledge base in the field of metallurgy encountered problems in acquisition and codifying reusable computer artifacts based on text documents. The aim of the work was to adapt the algorithms for classification of documents and to develop a method of semantic integration of a created repository. Author used artificial intelligence tools: latent semantic indexing, rough sets, association rules learning and ontologies as a tool for integration. The developed methodology allowed for the creation of semantic knowledge base on the basis of documents in natural language in the field of metallurgy.

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“…Rough logic and rough sets theory are mainly devoted to analysis of incomplete, uncertain, and inconsistent data (Kluska-Nawarecka et al, 2011).…”

Section: Rough Setsmentioning

confidence: 99%

ReSySTER: A hybrid recommender system for Scrum team roles based on fuzzy and rough sets

Colomo‐Palacios¹,

González‐Carrasco²,

López-Cuadrado³

et al. 2012

International Journal of Applied Mathematics and Computer Science

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Agile development is a crucial issue within software engineering because one of the goals of any project leader is to increase the speed and flexibility in the development of new commercial products. In this sense, project managers must find the best resource configuration for each of the work packages necessary for the management of software development processes in order to keep the team motivated and committed to the project and to improve productivity and quality. This paper presents ReSySTER, a hybrid recommender system based on fuzzy logic, rough set theory and semantic technologies, aimed at helping project leaders to manage software development projects. The proposed system provides a powerful tool for project managers supporting the development process in Scrum environments and helping to form the most suitable team for different work packages. The system has been evaluated in a real scenario of development with the Scrum framework obtaining promising results.

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Rough Sets Applied to the RoughCast System for Steel Castings

Cited by 19 publications

References 1 publication

Supporting the Manufacturing Process of Metal Products with the Methods of Artificial Intelligence

Supporting the Manufacturing Process of Metal Products with the Methods of Artificial Intelligence

Formalization of Technological Knowledge in the Field of Metallurgy using Document Classification Tools Supported with Semantic Techniques

ReSySTER: A hybrid recommender system for Scrum team roles based on fuzzy and rough sets

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