Smart Visualization for Online Aids Image Retrieval

Visual aids can be considered as a motivational tool in enhancing students’ attention and create positive perceptions. The use of new technologies has opened new possibilities to integrate online visual aids in the teaching process, which produce positive learning effects. In this paper, a novel technique employed to retrieve specific images based on the kind of query classification. The semantic dictionary built based on the specific classification correlate with the query intention. Singular Value Decomposition SVD training technique have been used to select the effective key templates in order to link the query with the web annotation directly. The present method can be considered as a strategic tool in the E-learning technique, which can provide variety of clustered images to help the students in creative and critical thinking skills and prevent the indoctrination method in learning the students. The qualitative results achieved high True Positive (TP) retrieved images that respect to the effectiveness of the E-learning task. Also, it provides a good 92% of learning reaction and superior learning behavior level.

Performance Evaluation of Policy-Based SQL Query Classification for Data-Privacy Compliance

Data science must respect privacy in many situations. We have built a query repository with automatic SQL query classification according to data-privacy directives. It can intercept queries that violate the directives, since a JDBC proxy driver inserted between the end-users’ SQL tooling and the target data consults the repository for the compliance of each query. Still, this slows down query processing. This paper presents two optimizations implemented to increase classification performance and describes a measurement environment that allows quantifying the induced performance overhead. We present measurement results and show that our optimized implementation significantly reduces classification latency. The query metadata (QM) is stored in both relational and graph-based databases. Whereas query classification can be done in a few ms on average using relational QM, a graph-based classification is orders of magnitude more expensive at 137 ms on average. However, the graphs contain more precise information, and thus in some cases the final decision requires to check them, too. Our optimizations considerably reduce the number of graph-based classifications and, thus, decrease the latency to 0.35 ms in $$87\%$$ 87 % of the classification cases.

Students Query Classification System

A University or educational institute generally receives a bulk of complaints posted by students every day. The issues relate to their academics or any issues related to their education or related to exam sections etc., because of these bulk of complaints received from the students every day, makes it difficult for the university to sort out them and classify them and send them to their respective departments for resolving the issues. In this project, we work on classifying these complaints based on the classes or departments they belong to, using. By using TF-IDF (term frequency-inverse document frequency) it finds terms which are more related to a specific document by converting to vectors. By capturing some keywords in the complaints, adding some weight to the keywords and using different Machine Learning classification’s we are classifying the complaint based on these keywords. This classification makes the works easier for the university and saves time which is used to sort them and gives better service for the students. Now they can directly send the complaints to the respective departments with ease.