The academic institutions are focusing more on improving the performance of students using various data mining techniques. Prediction models are designed to predict the performance of students at a very early stage so that preventive measures can be taken beforehand. Various parameters (academic as well as non-academic) are considered to predict the student performance using different classifiers. Normally, academic parameters are given more weightage in predicting the academic performance of a student. This paper compares the two models: one built using academic parameters only and another using both academic and non-academic (demographic) parameters. The primary data set of students has been taken from a technical college in India, which consists of data of 6,807 students containing attributes. Synthetic minority oversampling technique filter is applied to deal with the skewed data set. The models are built using eight classification algorithms that are then compared to find the parameters that help to give the most appropriate model to classify a student based on his performance.
Ancient scripts provide a captivating insight into the knowledge of ancestors which needs to be preserved for future generations. Therefore, there is a need to convert the digital script available in degraded format into textual format. To accomplish this model is being proposed in the paper that comprises of binarization using selection encoder decoder techniques. The results indicate the binarization accuracy as 74.24% approximately and F-measure is 75% (approximately) which comes out to be greater than other previously developed model. The binarized images are being further segmented using Seam Carbel method at character level and are manually compared with the vocabulary, the segmentation accuracy (A s) comes out to be 70% approximately. Further, characters are recognized using a three layer Convolutional Neural Network and the recognition accuracy (Ar) is found to be 73% approximately, the recognized images are further converted into text using one to one mapping, to be further used for translation into universally acceptable language like English.
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