CURONTO: An ontological model for curriculum representation.

Program curricula in higher education institutions are generally designed to satisfy a set of national and international standards within a discipline. These standards vary from an established set of educational objectives and course learning outcomes to appropriate coverage of the required knowledge areas of the discipline. To ensure that a program satisfies requirement standards, it is imperative that a continuous cycle of review and assessment of the program takes place periodically.

Speaker identification using MFCC-domain support vector machine

Speech recognition and speaker identification are important for authentication and verification in security purpose, but they are difficult to achieve. Speaker identification methods can be divided into text-independent and text-dependent. This paper presents a technique of text-dependent speaker identification using MFCC-domain support vector machine (SVM).

RGANN: An efficient algorithm to extract rules from ANNs

This paper describes an efficient rule generation algorithm, called rule generation from artificial neural networks (RGANN) to generate symbolic rules from ANNs. Classification rules are sought in many areas from automatic knowledge acquisition to data mining and ANN rule extraction. This is because classification rules possess some attractive features.

Extraction of symbolic rules from artificial neural networks

Although backpropagation ANNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained ANNs for the users to gain a better understanding of how the networks solve the problems.

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