Fuzzy rule based intelligent system for user authentication based on user behaviour

Roy, Arpıta; Razia, Shaik; Parveen, Nikhat; Rao, A. Srinivasa; Nayak, Soumya Ranjan; Poonia, Ramesh Chandra

doi:10.1080/09720529.2020.1728894

Cited by 15 publications

(10 citation statements)

References 13 publications

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“…Fuzzy inference is actively used in various intelligent systems [12][13][14]. Conclusions about the state of some objects based on the analysis of its current state are formed with fuzzy inference.…”

Section: Fuzzy Inferencementioning

confidence: 99%

“…The central part of DSS is an inference system [9][10][11][12][13][14][15][16][17]. An inference system allows inferring results based on information about an analyzed object within a context.…”

Section: Introductionmentioning

confidence: 99%

“…A FIS can be implemented based on various methods: machine learning [14,16], fuzzy controller or simulator [9][10][11][12][13], knowledge base [6][7][8][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…A machine learning model is a black box, so it is difficult to explain the reason for its decisions. The results of fuzzy controllers or simulators are formed by inference and based on a set of fuzzy rules [9][10][11][12][13]. However, such rules are usually not contextualized, which can produce logical errors and inconsistencies.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Fuzzy Inferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…A FIS can be implemented based on various methods: machine learning [14,16], fuzzy controller or simulator [9][10][11][12][13], knowledge base [6][7][8][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…At present, there are 29,451 cases, among them, 7,137 recovered and 939 deaths are reported [11] with the rate of infection approximately 1.55, comparatively lower than other countries. Another set of literature that is coming in this subject is based on image processing and fuzzy ruled based approach can be deployed in order to achieve best result [12][13]. In this paper, we deal with Statistical analysis in terms of Gaussian model, ANOVA and T-Test Model along with the Probabilistic model of positivity rate for the constrained consequence, i.e., with lockdown, quarantine, self-isolation.…”

Section: Introductionmentioning

confidence: 99%

A statistical analysis of COVID-19 using Gaussian and probabilistic model

Nayak

Arora

Sinha

et al. 2020

Journal of Interdisciplinary Mathematics

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SARS Cov-2, COVID-19 (Coronavirus) emerged in Wuhan in early December 2019 and then spread exponentially across the globe. Although, a series of prevention strategies (lockdown, social-distancing) have been enforced to control this pandemic. In this study, we have made statistical analysis in terms of Gaussian modeling, ANOVA test and probabilistic model. After applying ANOVA we can conclude that the recovery rate for all the countries are significantly higher than the mortality rate except for the UK where the mortality rate is significantly higher than the recovery rate. Gaussian modeling applied here was able to predict the original peak values of confirmed cases of countries. Using the probabilistic model we were able to predict that there is around 5% probability that a person in India will be tested positive for COVID-19 on 100 tests.

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