Expert Systems in Academic Advising

Elsayed, Rania A.; Seddik, Shahd; Rizkallah, Lydia W.

doi:10.1007/978-3-030-89701-7_18

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Expert systems are computer programs that are designed to have similar problem-solving behavior to human experts. Expert systems have been applied successfully to solve many problems such as academic advising [3], COVID-19 diagnosis [4], diagnosing and treating anxiety [5], and many more.…”

Section: Introductionmentioning

confidence: 99%

A Rule Learning Approach for Building an Expert System to Detect Network Intrusions

Galal

Nasr

Rizkallah

2023

IJICIS

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Network intrusion detection is the problem of detecting suspicious requests through networks.In recent years, many researchers focus on addressing this problem in the context of machine learning. Although machine learning algorithms are powerful, most of them lack the power of interpretability. Expert systems, on the other hand, are knowledge-based systems designed to simulate the problemsolving behavior of human experts. Expert systems possess the advantage of interpretability through an explanation mechanism that justifies their line of reasoning, however, they need the availability of a domain expert. This paper proposes the use of rule learning approaches to gain the best of both fields, being interpretable as the expert system and learnable through collected datasets without the need for explicit expertise. A separate and conquer rule learning approach is proposed for network intrusion detection. Our results show that the separate and conquer approach achieves a 0.99 weighted average F1-score on the test set which makes it very comparative to both decision trees and classical machine learning approaches. We also show that rules produced using separate and conquer are much simpler than decision trees and more interpretable.

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Section: Introductionmentioning

confidence: 99%

A Rule Learning Approach for Building an Expert System to Detect Network Intrusions

Galal

Nasr

Rizkallah

2023

IJICIS

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Building an enhanced case-based reasoning and rule-based systems for medical diagnosis

Mustafa,

Saad,

Rizkallah

2023

J. Eng. Appl. Sci.

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Expert systems are computer programs that use knowledge and reasoning to solve problems typically solved by human experts. Expert systems have been used in medicine to diagnose diseases, recommend treatments, and plan surgeries. Interpretability of the results in medical applications is crucial since the decision that will be taken based on the system’s output has a direct effect on people’s health and lives which makes expert systems ideal choices when dealing with these applications in contrast to other machine learning approaches. An expert system has the ability to explain its own line of reasoning providing a robust way of diagnosis. This paper presents two types of expert systems for medical diagnosis. The first system is a case-based reasoning system using a database of previously diagnosed cases to diagnose a new case. The second system is a rule-based expert system that uses a set of if–then rules extracted from a decision tree classifier to make diagnoses. In this paper, machine learning-based similarity functions are proposed and compared with other traditional similarity functions. The results of this study suggest that expert systems can be a valuable tool for medical diagnosis. The two systems presented in this paper achieved competitive results, and they provide diagnoses similar to those made by human experts.

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Expert Systems in Academic Advising

Cited by 2 publications

References 6 publications

A Rule Learning Approach for Building an Expert System to Detect Network Intrusions

A Rule Learning Approach for Building an Expert System to Detect Network Intrusions

Building an enhanced case-based reasoning and rule-based systems for medical diagnosis

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