Integration of Case-based and Rule-based Reasoning Through Fuzzy Inference in Decision Support Systems

Avdeenko, Tatiana; Makarova, Ekaterina

doi:10.1016/j.procs.2017.01.016

Cited by 20 publications

(6 citation statements)

References 8 publications

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“…The results of the conducted research on the test data were presented that testify to good prospects for using the genetic algorithm not only in the retrieving stage but also in the adaptation stage of the CBR-cycle. It seems promising to integrate the genetic algorithm with the generation of fuzzy rules [12] to implement the adaptation of retrieved cases to the problem being solved.…”

Section: Resultsmentioning

confidence: 99%

Investigation of the genetic algorithm possibilities for retrieving relevant cases from big data in the decision support systems

Serdyukov¹,

Avdeenko²

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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In present paper we consider the advantages and disadvantages of case-based reasoning (CBR) approach for knowledge representation of the application domain. One of the CBR shortcomings is the insufficient speed of real-time retrieval of cases, as well as the insufficient relevance of the retrieved cases to the current situation. To solve these problems, we offer the use of genetic algorithm. We propose formal statement of the genetic algorithm to the CBR retrieving stage. The results of the investigation are presented. The major advantage of the genetic algorithm is that it gives a more compact set of retrieved cases extracted which possesses, however, characteristic features of the current situation. This property can be very useful when extracting such cases from big data In conclusion, the perspectives of applying the method for adaptation of cases have been given.

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Section: Resultsmentioning

confidence: 99%

Investigation of the genetic algorithm possibilities for retrieving relevant cases from big data in the decision support systems

Serdyukov¹,

Avdeenko²

2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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“…Public safety and security research aims to solve one of the most requisite and complex societal problems, as we call it. Therefore, to conclude upon the diverse range of involved disciplines and variables in this research, a transdisciplinary research approach and methodological consideration have been adopted (Costanza, 1991;Horlick-Jones & Sime, 2004;Max-Neef, 2005;Pohl, 2008;Popa, Guillermin, & Dedeurwaerdere, 2015;Polk, 2015;del Cerro Santamaría, 2015;Guimarães, Pohl, Bina, & Varanda, 2019) as cited in (Varga and Baracskai, 2021) p. 41) (Table 4) Using a comprehensive literature review, we could reach the impact assessment through multiple aspects related to urban safety and security management projects discussed in literature while narrowing down upon the subject cases ( (Althoff, 2012), (Avdeenko and Makarova, 2017), (Baracskai et al, 2017)). It is worth mentioning here in order to support the approach and methodology of this article.…”

Section: Methodsmentioning

confidence: 99%

Social Impact Assessment in Urban Security Management Projects: A Case Study from Pakistan

Ghani,

Toth,

David

et al. 2024

Acad. J. Interdiscip. Stud.

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Public safety and security management projects are devised to reduce crime, fear and calamities by prevailing law and order to reduce the harm in society. In a certain context, social impact assessment is a novel way to reveal the extent of effectiveness for these projects. This case study presents an Expert System based methodology regarding social impact assessment for two urban security management projects in Pakistan. An Expert System, DoctuS is employed as a tool to build a rule-based model tool using social impact attributes (as variables) from literature and expert knowledge (Author’s own insight as being involved in projects). Case-based reasoning (CBR) method is employed for the measurement of the impact of urban security management KPIs (Key Performance Indicators) in the two case projects. The empirical findings in this case study approaches the impact assessment by quantitative analysis of crime rates in JMP statistical package and Qualitative Expert knowledge-based model in DoctuS. Thereby, the study evaluates the pre and post ante project situations in order to assess the project’s effectiveness and impact on improved urban safety and security. In the first step, the projects are briefly described, followed by the crime rate analysis under the project jurisdictions and rules between attributes are defined. The second step defines the qualitative rule-based model along with the two cases described, and validated in DoctuS tool. Some attributes and rules have changed, and a new concise model is presented. The Expert System compares the project cases and evaluates the social impact assessment based on the defined KPIs. The Expert system validation presents a novel model for social impact assessment of subject urban security management projects. We propose this methodology and model as useful in investigating social impact assessment of projects specifically dealing with urban safety and security management. However, the generalizability of the findings of this case-study based articles need more sophisticated tests as recommendation for the extension to this study. Received: 31 May 2023 / Accepted: 19 November 2023 / Published: 5 January 2024

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“…On the other hand, if IVF program patients wait for the reply for too long, it might increase their anxiety level [1,6] and adversely affect the IVF program they are participating in [1,4,5,[15][16][17]. To handle this, the CBR model was modi ed by combining CBR model and RBR model [43][44][45][46], following the logical owchart in Fig. 4.…”

Section: Ccbr Similarity Formulamentioning

confidence: 99%

“…The result of rst-stage research proves that applying the CCBR similarity formula in CBR model can actually improve the accuracy and precision scores of the smart system's recommendations (in a measurable percentage). The results of rst-stage research are published in Healthcare Informatics Research journal and will be the basis to conduct the second stage research, which also uses the previous research that nd that the combination of CBR and Rule-Based Reasoning (RBR) model can also improve the accuracy score of a smart system's recommendations [43][44][45][46]…”

Section: Introductionmentioning

confidence: 99%

Modified Case-Based Reasoning Model Helps Fertility Subspecialist Doctor Handle the Revision Stage and Answer Accurately In-Vitro Fertilization Program Patient’s Questions

Sembiring

Christianto

Sediyono

2022

Preprint

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Eighty two percent of in-vitro fertilization (IVF) program patients immediately ask fertility subspecialist doctors via SMS/Whatsapp messenger app when they feel different symptoms than what they usually feel. However, the high workload has been fertility subspecialist doctors’ obstacle to immediately reply these IVF program patients’ anxious questions. On the other hand, the long time it takes to receive a reply increases IVF program patients’ anxiety and, in turn, the high anxiety affects the success rate of IVF program, making it all the more important to help fertility subspecialist doctors deal with their obstacles. The weakness of Case-Based Reasoning (CBR) model is that the recommendation accuracy score is lower than that of modified CBR model and it makes the workload of fertility subspecialist doctors greater who have been busy dealing with the revision stage. To overcome this weakness, the Chris Case-Based Reasoning (CCBR) similarity formula is applied to CBR model and it combines with the Rule-Based Reasoning (RBR) model. The measurement of Confusion Matrix in the performance of CBR model combination which uses the CCBR similarity formula with RBR model suggests that the accuracy score increases to 47% and the precision score remains 100%, hence this model is declared capable of helping fertility subspecialist doctors handle the revision stage and accurately answer IVF program patients’ anxious questions.The limited scope of research makes it impossible for this research finding to be used as a standard model just yet for it needs to be tested for a wider scope.

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Integration of Case-based and Rule-based Reasoning Through Fuzzy Inference in Decision Support Systems

Cited by 20 publications

References 8 publications

Investigation of the genetic algorithm possibilities for retrieving relevant cases from big data in the decision support systems

Investigation of the genetic algorithm possibilities for retrieving relevant cases from big data in the decision support systems

Social Impact Assessment in Urban Security Management Projects: A Case Study from Pakistan

Modified Case-Based Reasoning Model Helps Fertility Subspecialist Doctor Handle the Revision Stage and Answer Accurately In-Vitro Fertilization Program Patient’s Questions

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