Semantic Scholar – Knowledge and References

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Domain-Specific Journal Recommendation Using a Feed Forward Neural Network

Published in Himansu Das, Jitendra Kumar Rout, Suresh Chandra Moharana, Nilanjan Dey, Applied Intelligent Decision Making in Machine Learning, 2020

S. Nickolas, K. Shobha

For the notion that motivated this work, we use Google Scholar, Semantic Scholar, Scopus, Web of Science, and Microsoft Academic data to provide customizable recommendations for individuals. The aim is to support the research community by recommending the most suitable article from a curated list of publications for their domain of interest. For this drive, we developed a method to assess the publication attainment from a set of researchers who are involved in the identical research domain. The method comprises a scraper to acquire the required data. Figure 5.1 provides an overview of the tool.

Parsing AUC Result-Figures in Machine Learning Specific Scholarly Documents for Semantically-enriched Summarization

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Journal Information

Published in Applied Artificial Intelligence, 2022

Iqra Safder, Hafsa Batool, Raheem Sarwar, Farooq Zaman, Naif Radi Aljohani, Raheel Nawaz, Mohamed Gaber, Saeed-Ul Hassan

Our dataset is a subset of data corpus used by FiguresSeer (Siegel et al. 2016) that contained over 22,000 full-text documents belonging from top computer science conferences; CVPR, ICML, ACL, CHI, and AAAI, indexed by Semantic Scholar. From among these, we randomly selected 1000 documents and obtained around 12,146 figures belonging to different classes (graph plots, bar charts, flow charts, etc.). We also extracted 5,804 subfigures from these documents. Later, we used a random sample of 1000 line graphs for parsing that yields 1272 axes, 2183 legend entries, and plots.