scholarly journals An Approach to Building Decision Support Systems Based on an Ontology Service

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2946
Author(s):  
Anton Romanov ◽  
Julia Stroeva ◽  
Aleksey Filippov ◽  
Nadezhda Yarushkina
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Programming Languages ◽  
Virtual Machines ◽  
Fuzzy Inference ◽  
Support Systems ◽  
Complex Structures ◽  
Java Virtual Machines ◽  
Production Capacities ◽  
Base Formation

Modern decision support systems (DSSs) need components for storing knowledge. Moreover, DSSs must support fuzzy inference to work with uncertainty. Ontologies are designed to represent knowledge of complex structures and to perform inference tasks. Developers must use the OWLAPI and SWRL API libraries to use ontology features. They are impossible to use in DSSs written in programming languages not for Java Virtual Machines. The FuzzyOWL library and the FuzzyDL inference engine are required to work with fuzzy ontologies. The FuzzyOWL library is currently unmaintained and does not have a public Git repository. Thus, it is necessary to develop the ontology service. The ontology service must allow working with ontologies and making fuzzy inferences. The article presents ontology models for decision support, fuzzy inference, and the fuzzy inference algorithm. The article considers examples of DSSs for balancing production capacities and image analysis. The article also describes the architecture of the ontology service. The proposed novel ontology models for decision support make it possible to reduce the time of a knowledge base formation. The ontology service can integrate with external systems with HTTP protocol.

OBJECTIFICATION OF FUZZY INFERENCE IN DECISION-MAKING PROBLEMS

2021 ◽  
Author(s):  
L. P. Vershinina ◽  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Statistical Models ◽  
Fuzzy Inference ◽  
Support Systems ◽  
Practical Application ◽  
Inference Process ◽  
Quality Of Information

The basis of modern decision support systems is not so much analytical and statistical models as the practical application of specialists ‘ knowledge. Such systems are based on fuzzy technologies. The quality of decisions made depends on how accurately the quality of information is reflected in the fuzzy inference process. Ways to improve the objectivity of fuzzy inference at the stages of fuzzification, aggregation, activation, and accumulation are proposed.

scholarly journals Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

Diagnostics ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 52 ◽  
Author(s):  
Yamid Fabián Hernández-Julio ◽  
Martha Janeth Prieto-Guevara ◽  
Wilson Nieto-Bernal ◽  
Inés Meriño-Fuentes ◽  
Alexander Guerrero-Avendaño
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Fuzzy Inference ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Data Driven ◽  
Kappa Statistics ◽  
Levels Of Abstraction

Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

Deploying Decision Support Systems Using Semantic Web Technologies

2012 ◽  
pp. 51-61
Author(s):  
Lars Ludwig ◽  
David O’Sullivan
Keyword(s):  
Decision Support ◽  
Semantic Web ◽  
Decision Support Systems ◽  
Programming Languages ◽  
Support Systems ◽  
Web Technology ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Semantic Web Technology ◽  
Technology Industry

Decision support systems are deployed in a wide variety of business applications using a variety of core technologies and programming languages. One of the more promising technologies to evolve in recent year has been the semantic web. The semantic web aims to create more intelligent and machine readable web pages and online applications. The technologies, programming languages and methods of the semantic web are now maturing and standards have emerged that allow semantic web technology to be deployed broadly across information technology industry and the programming community in particular. This paper outlines a set of requirements for programmers considering the development of decision support systems using semantic web technology. Current strategies across the research community are surveyed that deploy semantic web applications. From the discussion of these strategies, ten basic requirements are derived. These requirements combine technological, psychological and philosophical research ideas. By crossing traditional research boundaries, a broad perspective on deploying decision support systems that utilize semantic web technologies is created.

Possibilities and Problems in Applying Decision Support Systems for Special Challenges in the Design of Building Constructions

2014 ◽  
Vol 899 ◽  
pp. 583-588
Author(s):  
Geza Kapovits ◽  
Gergely Dobszay
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Energy Consumption ◽  
Decision Support Systems ◽  
Engineering Design ◽  
Support Systems ◽  
Complex Structures ◽  
Special Group ◽  
Technical Performance ◽  
Materials Used

The objective of this study is to examine the possibility of using DSS in the engineering design of a special group of building constructions: 'cladded roofs'. These are roofs whose cladding is the same as the façade’s, giving buildings a homogeneous appearance. For architectural impression the roof is made of unusual materials and complex structures thus cannot be designed with traditional guidelines. The goal of using DSS is to facilitate and simplify the range of possible materials and structures, to assist and expedite finding the optimal version and to reduce the likelihood of design flaws. In addition to eco-efficiency in production, transportation, and energy consumption of buildings, we hold it to be important to reduce the amount of materials used to a necessary minimum. [1] To achieve this all materials should be used at places where their specific characteristics prevail most effectively. The criteria for applying DSS for ‘cladded roofs’ is the successful structuring of the necessary constructional data and variations. We propose a method to generate numerous proposed solutions and rank them based on their technical performance and value. The result is a kind of 'guided decision-making' which may be an important aid in the design of nonstandard structures.

Deploying Decision Support Systems Using Semantic Web Technologies

2010 ◽  
Vol 2 (1) ◽  
pp. 49-59
Author(s):  
Lars Ludwig ◽  
David O'Sullivan
Keyword(s):  
Decision Support ◽  
Semantic Web ◽  
Decision Support Systems ◽  
Programming Languages ◽  
Support Systems ◽  
Web Technology ◽  
Semantic Web Technologies ◽  
Broad Perspective ◽  
Web Technologies ◽  
Semantic Web Technology

Decision support systems are deployed in a wide variety of business applications using a variety of core technologies and programming languages. One of the more promising technologies to evolve in recent year has been the semantic web. The semantic web aims to create more intelligent and machine readable web pages and online applications. The technologies, programming languages and methods of the semantic web are now maturing and standards have emerged that allow semantic web technology to be deployed broadly across information technology industry and the programming community in particular. This paper outlines a set of requirements for programmers considering the development of decision support systems using semantic web technology. Current strategies across the research community are surveyed that deploy semantic web applications. From the discussion of these strategies, ten basic requirements are derived. These requirements combine technological, psychological and philosophical research ideas. By crossing traditional research boundaries, a broad perspective on deploying decision support systems that utilize semantic web technologies is created.

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