The Principles of Designing an Expert System in Teaching Mathematics

Salekhova, Lailya; Nurgaliev, Albert; Zaripova, Rinata; Khakimullina, Nailya

doi:10.13189/ujer.2013.010202

Cited by 7 publications

(2 citation statements)

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“…An expert system (ES) was used in daily human life, such as health [8], [24], [25], emergencies [26], [27], education [28]- [30], military [31], [32], economics [33], business management [34], [35], engineering [36]- [38], aeronautics [39], agriculture [40]- [46], biological sciences [5], [15], [36], [47]- [49], forensics [50], [51], and veterinary [52]- [55]. As part of artificial intelligence (AI), the expert system (ES) was used knowledge-based to represent the human expert knowledge in decisionmaking [56], [57].…”

Section: Literature Reviewmentioning

confidence: 99%

Predict the thyroid abnormality particular disease likelihood of the symptoms’ certainty factor value and its confidence level: A regression model analysis

Al-Hakim,

Arief,

Pangestu

et al. 2023

SISTEMASI

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The traditional expert system (TES) in the medical field commonly uses a certainty factor (CF) rule-based algorithm that can be calculated several symptoms to determine the inference solutions. The main issue for this TES included a prediction for some particular disease likelihood in the cases of new patients. CF is calculated based on symptoms related to clinical signs in patients’ diagnoses. For some reason, this TES probably won’t predict uncertain things, such as particular disease likelihood of some diseases. So, supervised learning, such as linear regression, can solve this problem. We tried to analyse the existing TES for thyroid disorders due to modelling the regression equation to predict the thyroid abnormality particular disease likelihood, based on the symptoms’ CF value and its confidence level. We used multiple linear regression (MLR) and multiple polynomial regression (MPR) to analyse the best regression model to solve the problem. The results show that the MPR model indicates the best regression model for predicting particular disease likelihood of thyroid abnormality, supported by R-squared 94.7%, R-squared adjusted 94.4%, F-value 265.925, and p-value < 0.05, which are higher than MLR model. Our study proposed a foundation for expert system development by focusing more on machine learning expert system (MLES) analysis approaches than TES.

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Section: Literature Reviewmentioning

confidence: 99%

Predict the thyroid abnormality particular disease likelihood of the symptoms’ certainty factor value and its confidence level: A regression model analysis

Al-Hakim,

Arief,

Pangestu

et al. 2023

SISTEMASI

View full text Add to dashboard Cite

show abstract

“…The expert system was used to help some cases of human life [31], such as emergencies [32], [33], medical [21], [34], [35], engineering [36]- [38], education [39]- [41], business [42], [43], economics [44], military [45], [46], biological sciences [28], [36], [47]- [50], veterinary [51]- [54], agriculture [55]- [61], and forensics [62], [63]. As part of artificial intelligence, knowledge-based expert systems (ESs) are used to represent the knowledge of human experts in decision-making [64], [65].…”

Section: Literature Reviewmentioning

confidence: 99%

Using Regression Model Analysis for Forecasting the Likelihood of Particular Symptoms of COVID-19

Pangestu,

Sumirat,

Al-Hakim

et al. 2024

SISTEMASI

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A certainty factor (CF) rule-based technique is frequently used by traditional expert systems (TES) in the medical industry to compute several symptoms and identify the inference solutions. The primary concern for this TES was predicting the likelihood of a particular ailment in the circumstances of new patients. Based on symptoms connected to clinical indicators in patients' diagnosis, CF is estimated. This TES probably won't be able to forecast unknown things, like the possibility of a particular ailment. Therefore, supervised learning techniques like linear regression can address this issue. We attempted to analyze the current COVID-19 TES by modeling the regression equation to forecast the chance of a particular disease that is COVID-like based on the CF value and the confidence level of the symptoms. To examine the most effective regression model to address the issue, we employed multi-linear regression (MLR) and multi-polynomial regression (MPR). The findings demonstrate that the MLR and MPR models are the most accurate regression models for estimating the chance of a disease associated with COVID-like symptoms. Our work built a basis for the creation of expert systems by concentrating more on MLES (machine learning expert systems) analytical techniques than TES.

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Construction of Fuzzy-Classification Expert System in Cerebral Palsy for Learning Performance Facilitation

Ivanova

Ilieva

2020

Advances in Intelligent Systems and Computing

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The Principles of Designing an Expert System in Teaching Mathematics

Cited by 7 publications

References 0 publications

Predict the thyroid abnormality particular disease likelihood of the symptoms’ certainty factor value and its confidence level: A regression model analysis

Predict the thyroid abnormality particular disease likelihood of the symptoms’ certainty factor value and its confidence level: A regression model analysis

Using Regression Model Analysis for Forecasting the Likelihood of Particular Symptoms of COVID-19

Construction of Fuzzy-Classification Expert System in Cerebral Palsy for Learning Performance Facilitation

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