Using MCRDR based Agile approach for expert system development

Han, Soyeon Caren; Yoon, Hee-Geun; Kang, Byeong Ho; Park, Seong-Bae

doi:10.1007/s00607-013-0336-y

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…For example, RDR and MCRDR have been used across a diversity of research and application areas: telehealth (Han et al, 2013); legal text citation (Galgani, Compton, & Hoffmann, 2015), and flight control systems (Shirazi & Sammut, 2008). As for the speed of development and implementation, it has been shown that the MCRDR-backed knowledge base method is closely aligned with the agile software development approach (Han, Yoon, Kang, & Park, 2014).…”

Section: Introductionmentioning

confidence: 99%

Expert System Development for Course Enrollment Process Using Ripple Down Rules in a University in Surabaya

Nugroho

2019

ComTech

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The research aimed to develop expert system for helping lecturers to decide which courses to be taken by students in the next semester. The expert system contained specific pieces of knowledge to solve specific problems involved in the forms of system development and maintenance. This expert system was in the form of a website using the PHP programming language and MySQL database. The researcher used the Ripple Down Rules (RDR) method to identify the courses by putting forward the questions so that the system could decide which courses the students should take in next semester. The result shows that this web-based expert system can identify which courses that the students have to enroll after the students have answered questions generated by the system. The available data on the courses in the system adapt to the rules, so it is in line with the enrolled courses.

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Section: Introductionmentioning

confidence: 99%

Expert System Development for Course Enrollment Process Using Ripple Down Rules in a University in Surabaya

Nugroho

2019

ComTech

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“…Though these studies discuss the advantages and disadvantages of blended learning, we still have little knowledge about how learning activities are influenced by the social networks that are becoming more and more popular. Powered by Web 2.0 technologies, social networking is penetrating all sectors of society, including tertiary education, to transform the way that we connect and communicate, leading to unprecedented changes in the way social ties are formed and understood (Park et al 2014). A social learning network is defined as the online social network platform that supports university learning by connecting students and instructors beyond class-centric activities.…”

Section: Introductionmentioning

confidence: 99%

HCI in Business

2015

Lecture Notes in Computer Science

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Abstract. The Word Sense Disambiguation (WSD) problem has been considered as one of the most important challenging task in Natural Language Processing (NLP) research area. Even though, many of scientists applied the robust machine learning, statistical techniques, and structural pattern matching approach, the performance of WSD is still not able to bit human results due to the complexity of human language. In order to overcome this limitation, currently, the knowledge base such as WordNet has gained high popularity among researchers due to the fact that this knowledge base can extensively provide not only the definitions of nouns and verbs, but also the semantic networks between senses which were defined by linguists. However, knowledge bases are not fully dealing with entire words of human languages because maintaining and expanding the knowledge base is huge task which requires many efforts and time. Expanding knowledge base is not a big issue to concern however, a new approach is the major goal of this paper to solve WSD problem only based on limited knowledge resources. In this paper, we propose a method, named low ambiguity first (LAF) algorithm, which disambiguates a polysemous word with a low ambiguity degree first with given disambiguated words, based on the structural semantic interconnections (SSI) approach. The LAF algorithm is based on the two hypothesises that first, adjacent words are semantically relevant than other words far way. Second, word ambiguity can be measured by frequency differences between synsets of the given word in WordNet. We have proved these hypothesises in the experiment results, the LAF algorithm can improve the performance of traditional WSD results.

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“…In Ripple-down Rules (RDR), its unique knowledge acquisition process solves the problems lie on knowledge engineering process. RDR is built with rules of hierarchical exceptions [16]. It is a knowledge acquisition and representation technique that allows knowledge of a certain domain to be interpreted as rules.…”

Section: Human Knowledge Acquisitionmentioning

confidence: 99%

Combining RDR-Based Machine Learning Approach and Human Expert Knowledge for Phishing Prediction

Chung¹,

Chen²,

Han³

et al. 2016

PRICAI 2016: Trends in Artificial Intelligence

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Abstract. Detecting phishing websites has been noted as complex and dynamic problem area because of the subjective considerations and ambiguities of detection mechanism. We propose a novel approach that uses Ripple-down Rule (RDR) to acquire knowledge from human experts with the modified RDR model-generating algorithm (Induct RDR), which applies machine-learning approach. The modified algorithm considers two different data types (numeric and nominal) and also applies information theory from decision tree learning algorithms. Our experimental results showed the proposing approach can help to deduct the cost of solving over-generalization and over-fitting problems of machine learning approach. Three models were included in comparison: RDR with machine learning and human knowledge, RDR machine learning only and J48 machine learning only. The result shows the improvements in prediction accuracy of the knowledge acquired by machine learning.Keywords: phishing prediction, RDR; knowledge-based system; machine learning; decision tree IntroductionAn accelerative growth of Internet-based financing increases online fraudulent activity in which malicious people tries to reveal sensitive information of Internet users, also called as phishing. Phishing detection has received great attention but there has been limited research on a way of overall success due to the nature of problems. The problems of detecting phishing websites are very complex and hard to analyze as technical and social problems are joining each other [1]. Either machine learning technique and human expert system has been applied to acquire and maintain the knowledge for phishing website detection and prediction while the results do not show significance. A large number of knowledge-based systems are built for acquiring and maintaining the knowledge for detecting and predicting the phishing website. Phishing website detection knowledge was originally acquired from domain experts. However, acquiring knowledge from an expert in a slow pace cannot meet the demand of the expanding systems since a sophisticated expert system may require an extremely large number of rules. This leads to machine learning based approach as a solution to manage knowledge-based systems. Although machine learning technique can acquire knowledge from data without the help of a domain expert and an abundance of classifier models exist and decision tree based algorithms provide the best performance,

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Using MCRDR based Agile approach for expert system development

Cited by 10 publications

References 15 publications

Expert System Development for Course Enrollment Process Using Ripple Down Rules in a University in Surabaya

Expert System Development for Course Enrollment Process Using Ripple Down Rules in a University in Surabaya

HCI in Business

Combining RDR-Based Machine Learning Approach and Human Expert Knowledge for Phishing Prediction

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