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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: June 2026
Volume 11 | Issue 6
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Impact Factor : 8.14
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| Paper Title: | Nexus ML: A Content-Based Recommendation and Classification Engine for Scientific Abstracts |
| Authors Name: | Manish Sharma , Himanshu Sharma |
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IJRTI_212693
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| Published Paper Id: | IJRTI2605098 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | The exponential growth of academic literature across digital repositories has fundamentally altered the landscape of scholarly research. While this proliferation ensures diverse scientific inquiries are documented, it introduces severe information overload for researchers and academicians. Finding highly relevant literature and accurately categorizing new, unlabelled pre-prints have become daunting tasks that traditional search engines handle inefficiently. To mitigate this challenge, this paper presents the design, development, and evaluation of a dual-purpose Research Paper Classifier and Recommender. This integrated machine learning system bridges the gap between document discovery and automated taxonomy by utilizing advanced natural language processing (NLP) and neural network architectures. The proposed system features two primary operational pipelines accessible via a unified web interface. The first pipeline is a multi-label classifier engineered to predict academic subject areas (such as Machine Learning, Artificial Intelligence, and Computer Vision) directly from raw abstract text. By utilizing a customized Term Frequency-Inverse Document Frequency (TF-IDF) vectorization layer integrated directly into a TensorFlow/Keras MultiLayer Perceptron (MLP), the system effectively captures local word contexts and heavily weights domain-specific terminology. The second pipeline is a semantic recommendation engine that transcends simple lexical matching. By analyzing paper titles, it identifies and retrieves contextually adjacent literature, successfully grouping documents by their underlying methodologies and architectural frameworks. The system was trained and rigorously evaluated on a comprehensive, deduplicated dataset of over 41,000 papers from arXiv.The classification module achieved an exceptional categorical accuracy of 99.45% on the testing subset, demonstrating highly stable convergence and minimal loss. Furthermore, these backend machine learning models were successfully deployed into a streamlined, interactive Streamlit web application. This frontend provides a frictionless experience, allowing researchers to intuitively paste text and receive instantaneous categorizations and curated reading lists |
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| Cite Article: | "Nexus ML: A Content-Based Recommendation and Classification Engine for Scientific Abstracts ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a790-a795, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605098.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2605098
Registration ID:212693
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: a790-a795 Country: Raipur, Chhattisgarh, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605098 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605098 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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