Approach to the Search for Similar Software Projects Based on the UML Ontology

Guskov, Gleb; Namestnikov, A.M.; Yarushkina, Nadezhda

doi:10.1007/978-3-319-68324-9_1

Cited by 9 publications

(5 citation statements)

References 8 publications

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Section: Introductionmentioning

confidence: 99%

“…The disadvantage of neural networks is that the knowledge obtained by the neural network has distributed neurons that are not available to the user. But fuzzy logic is free from this drawback [4][5][6][7].A system created with fuzzy logic makes a decision based on the IF-THEN implication. At the same time, control rules are formulated and membership functions are built on the basis of the research results.…”

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confidence: 99%

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Building and training a fuzzy neural model of data mining tasks

Muhamediyeva

2022

J. Phys.: Conf. Ser.

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In this paper discussed the problem of constructing and training a fuzzy neural network based on fuzzy logical rules. Based on the constructed model, the developed algorithm, the objects classified with indistinct initial information. Under these conditions, traditional methods of mathematical statistics or simulation modeling do not allow building adequate models for solving data mining problems. We consider the solution of problems based on the use of intelligent modeling technologies using methods of neural networks and fuzzy logic in order to automate the construction of fuzzy rules based on methods and algorithms of machine learning.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Building and training a fuzzy neural model of data mining tasks

Muhamediyeva

2022

J. Phys.: Conf. Ser.

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“…At the moment, a lot of researchers use the ontological approach for the organization of the knowledge bases of expert and intelligent systems: M. Gao, C. Liu [ 11 ], D. Bianchini [ 12 ], N. Guarino [ 3 ], G. Guizzardi [ 13 ], R.A. Falbo [ 14 ], G. Stumme [ 15 ], N.G. Yarushkina [ 18 ], T.R. Gruber [ 16 ], A. Maedche [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Approach to the Search for Software Projects Similar in Structure and Semantics Based on the Knowledge Extracted from Existed Projects

Filippov

Guskov

Michailovich

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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This article presents a new effective model, algorithms, and methods for representing the subject area of an information system. The subject area is presented in the form of fragments of the knowledge base of the design support system. The knowledge base is formed in the process of analyzing class diagrams in UML notation and project source code. The proposed approaches can reduce the time of the design process and increase the quality of the obtained information system through the use of successful information system design solutions used in other projects. Search for successful design solutions is carried out using the developed metrics for determining the similarity of software systems projects. The metrics allow calculating the match of pattern in OWL ontology format with the source code of the project.

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“…Для учета лингвистической неопределенности и неполноты данных целесообразно использовать методы нечеткой логики и алгоритмы нечеткого логического вывода. Теория нечетких множеств [Zadeh, 1965], как одно из направлений искусственного интеллекта [Akhmetvaleev, Katasev, 2018;Ge et al, 2017;Guskov et al, 2018;Ismagilov et al, 2018], позволяет строить нечеткие модели объектов с использованием лингвистических переменных и механизмов логического вывода. При этом нечеткая модель представляет систему нечетко-продукционных правил и алгоритм вывода на правилах.…”

Section: Introductionunclassified

Neuro-fuzzy model of fuzzy rules formation for objects state evaluation in conditions of uncertainty

Katasev¹

2019

CRM

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This article solves the problem of constructing a neuro-fuzzy model of fuzzy rules formation and using them for objects state evaluation in conditions of uncertainty. Traditional mathematical statistics or simulation modeling methods do not allow building adequate models of objects in the specified conditions. Therefore, at present, the solution of many problems is based on the use of intelligent modeling technologies applying fuzzy logic methods. The traditional approach of fuzzy systems construction is associated with an expert attraction need to formulate fuzzy rules and specify the membership functions used in them. To eliminate this drawback, the automation of fuzzy rules formation, based on the machine learning methods and algorithms, is relevant. One of the approaches to solve this problem is to build a fuzzy neural network and train it on the data characterizing the object under study. This approach implementation required fuzzy rules type choice, taking into account the processed data specificity. In addition, it required logical inference algorithm development on the rules of the selected type. The algorithm steps determine the number and functionality of layers in the fuzzy neural network structure. The fuzzy neural network training algorithm developed. After network training the formation fuzzyproduction rules system is carried out. Based on developed mathematical tool, a software package has been implemented. On its basis, studies to assess the classifying ability of the fuzzy rules being formed have been conducted using the data analysis example from the UCI Machine Learning Repository. The research results showed that the formed fuzzy rules classifying ability is not inferior in accuracy to other classification methods. In addition, the logic inference algorithm on fuzzy rules allows successful classification in the absence of a part of the initial data. In order to test, to solve the problem of assessing oil industry water lines state fuzzy rules were generated. Based on the 303 water lines initial data, the base of 342 fuzzy rules was formed. Their practical approbation has shown high efficiency in solving the problem.

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Approach to the Search for Similar Software Projects Based on the UML Ontology

Cited by 9 publications

References 8 publications

Building and training a fuzzy neural model of data mining tasks

Building and training a fuzzy neural model of data mining tasks

Approach to the Search for Software Projects Similar in Structure and Semantics Based on the Knowledge Extracted from Existed Projects

Neuro-fuzzy model of fuzzy rules formation for objects state evaluation in conditions of uncertainty

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