Articles written in Sadhana
Volume 39 Issue 2 April 2014 pp 283-302
In this paper, we propose a new pattern classification system by combining Temporal features with Fuzzy Min–Max (TFMM) neural network based classifier for effective decision support in medical diagnosis. Moreover, a Particle Swarm Optimization (PSO) algorithm based rule extractor is also proposed in this work for improving the detection accuracy. Intelligent fuzzy rules are extracted from the temporal features with Fuzzy Min–Max neural network based classifier, and then PSO rule extractor is used to minimize the number of features in the extracted rules. We empirically evaluated the effectiveness of the proposed TFMM-PSO system using the UCI Machine Learning Repository Data Set. The results are analysed and compared with other published results. In addition, the detection accuracy is validated by using the ten-fold cross validation.
Volume 40 Issue 2 April 2015 pp 379-394 Electrical and Computer Sciences
he e-learners' excellence can be improved by recommending suitable e-contents available in e-learning servers that are based on investigating their learning styles. The learning styles had to be predicted carefully, because the psychological balance is variable in nature and the e-learners are diversified based on the learning patterns, environment, time and their mood. Moreover, the knowledge about the learners used for learning style prediction is uncertain in nature. This paper identifies Felder–Silverman learning style model as a suitable model for learning style prediction, especially in web environments and proposes to use Fuzzy rules to handle the uncertainty in the learning style predictions. The evaluations have used the Gaussian membership function based fuzzy logic for 120 students and tested for learning of C programming language and it has been observed that the proposed model improved the accuracy in prediction significantly.