Browsing by Author "Muslihuddeen, H."

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    CISER: Customized Institute Specific Search Engine for Retrieving Research Papers
    (Institute of Electrical and Electronics Engineers Inc., 2023) Sankar, S.; Muslihuddeen, H.; Ostwal, S.; Sathvika, P.; Anand Kumar, M.
    This paper proposes a methodology of a search engine system for searching research papers customized to our institute students. Most of the courses are associated with course projects where students face difficulties in finding the best research papers associated with the course. So, here we propose a customized mechanism to search the research papers published by the faculties of the institute. The input for the proposed search engine can either be the course name or the topic itself. We give users two options: search by course name and topic. If the course name is given as input, we get the corresponding keywords for the course, and then we implement semantic similarity on the Author Keywords. If the user searches by topic, we perform semantic similarity using the given topic and the Author Keywords of the research papers. We have also created a web interface using Django. © 2023 IEEE.
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    Impact of Rhetorical Roles in Abstractive Legal Document Summarization
    (Institute of Electrical and Electronics Engineers Inc., 2024) Muhammed, A.; Muslihuddeen, H.; Sankar, S.; Anand Kumar, M.
    This paper explores the relationships between rhetorical roles and the summarization of legal documents. By employing automated interpretation techniques, we segment legal judgment documents and identify rhetorical roles, treating it as a 13-class labeling problem. Using CRFs and a BiLSTM architecture, we extract rhetorical roles. Our study extends further by employing an ensemble summarizer to examine the impact of each rhetorical role on the summarization process and evaluate the same using ROGUE and BLEU scores. Through experimentation, we explore the impact of selecting individual rhetorical roles from the documents, thereby facilitating a comprehensive analysis of its effect on various scoring metrics. Our analysis reveals that certain roles, such as FAC and ANALYSIS, contribute significantly to summary quality, while others like ISSUE, have a less pronounced impact which interestingly also tend to be included in the summary almost verbatim, underscoring their inherent utility in the summarization process. By shedding light on this process, our research aims to equip researchers with valuable insights into streamlining summarization techniques, potentially reducing the volume of text data processed. Ultimately, these findings could pave the way for the development of more efficient summarization algorithms tailored to different roles in legal documents, enhancing accessibility and comprehension for professionals and researchers alike. © 2024 IEEE.
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    Profiling Cryptocurrency Influencers using Few-shot Learning
    (CEUR-WS, 2023) Muslihuddeen, H.; Sathvika, P.; Sankar, S.; Ostwal, S.; Anand Kumar, M.
    This research provides a novel method for identifying cryptocurrency influencers on social media in a low-resource environment. The analysis focuses on English-language Twitter messages and divides influencers into impact categories ranging from minimal to massive. With a maximum of 10 English tweets per user, the dataset consists of 32 people per category. By comparing the suggested approach to two baseline models—Usercharacter Logistic Regression and t5-large (bi-encoders) using zero-shot and label tuning few-shot methods—the proposed system is evaluated using the Macro F1 measure. The findings show that the suggested approach operates effectively in low-resource environments and has the potential to be used to further in-depth studies of influencer profiling. © 2023 Copyright for this paper by its authors.