Test is one of the tools that are used to evaluate learner's achievement. Most of test scoring in e-learning systems are for true-false and fill in blank questions. Description questions are human efforts and time consuming. A courseware with its questions bank had been built based on ontology. Extracting the semantic keywords from the learner's answer would be used to score the answer. In this paper we introduce a method to score the learner's answer based on semantic keywords in the question's ontology. Position priority and frequency of occurrence the semantic keywords have been taken in our calculation. This scoring is used to evaluate the learner's performance to answer description questions.
This paper focuses on using the cooperative neuro-fuzzy system for the effective and customised selection of entities from large and heterogeneous resources by presenting a general architecture. An experiment is carried out with the fast-moving consumer goods to prove the utility of the architecture. It is observed that most consumers go for the frequent purchase of fast-moving consumer items. Further, various brands, costs, discounts, schemes, quantities, and reviews might make it challenging. Hence, such decisions need to be intelligent and practically feasible in terms of time and effort. The paper discusses neural networks to categorise the entities, type-1 & 2 fuzzy membership functions with rules, training sets, and graphical views of the fuzzy rules and the experiment details. Besides the generic approach and experiment, the paper also discusses the work done so far with their limitations and applications in other domains. At the end, the paper presents the limitations and possible future enhancements.
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