A new approach for building student model in an Adaptive and intelligent Web-Based Educational System (AIWBES) is introduced. This approach utilizes a hybrid algorithm based on Fuzzy-ART2 neural network and stochastic method called Hidden Markov Model (HMM), in order to evaluate and categorize students' knowledge status in six levels: Excellent, very good, good, fair, weak and very weak; depending on 5 parameters collected through their interactions with the system. The student model is initialized by presenting a pre-test form to students and it is updated dynamically according to their study times and assessment results. Students' knowledge status are modeled through three phases, initialization, training and recall phases. In the initialization phase, input vectors are normalized before they are categorized using unsupervised algorithm Fuzzy-ART2 in 6 clusters representing 6 knowledge status. A HMM is created for each cluster and when new students' parameters are collected, they are introduced to BaumWelch re-estimation algorithm to train the 6 HMMs and to maximize the observed sequence that is associated with a particular cluster. Forward algorithm evaluates then the likelihood of this sequence with respect to each of the HMMs and to determine the maximum value, which represents the actual knowledge status of the student. Experiment results show that the proposed approach is capable of categorizing student parameter vectors to their corresponding cluster with good accuracies. The result of such classifications would open new horizons and applications in AIWBES.
Abstract-Visual inspection by a human operator has been mostly used up till now to detect cracks in sewer pipes. In this paper, we address the problem of automated detection of such cracks. We propose a model which detects crack fractures that may occur in weak areas of a network of pipes. The model also predicts the level of dangerousness of the detected cracks among five crack levels. We evaluate our results by comparing them with those obtained by using the Canny algorithm. The accuracy percentage of this model exceeds 90% and outperforms other approaches.
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