Published Paper Details:

Computer science and many of its applications are about developing, analysing, and applying algorithms. Efficient solutions to important problems in various disciplines other than computer science usually involve transforming the problems into algorithmic ones on which standard algorithms are applied. Scholarly Digital documents are increasing day by day. To automatically find and extract these algorithms in this vast collection of documents that enable algorithm indexing, searching, discovery, and analysis.AlgorithmSeer, a search engine for algorithms, has been investigated as part of CiteSeerX with the intent of providing a large algorithm database. A novel set of scalable techniques used by AlgorithmSeerto identify and extract algorithm representations in a heterogeneous pool of scholarly documents is proposed. Along with this,anyone with different levels of knowledge can access the platform and highlight portions of textual content which are particularly important and relevant. The highlighted documents can be shared with others in support of lectures and self-learning. But the high-lighted part of text cannot be useful to different levels of learners. We solve the problem of predicting new highlights of partly highlighted e-learning documents.