[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124156-en":3,"doc-seo-124156-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124156,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Automated Research Review Support Using Machine Learning, Large Language Models, and Natural Language Processing","Research expands the boundaries of a subject, economy, and civilization, but peer review remains costly and slow. This work introduces a human-in-the-loop approach to support the research community by predicting quality and acceptance, recommending reviewers, and helping authors and editors evaluate papers. A comprehensive system is trained on 18,000+ research papers and includes ML models plus reviewer aspects and accept/reject decisions. Experiments reach 86% accuracy with DistilBERT and comparable results using PaLM embeddings.","1-1-2025  \nAutomated Research Review Support Using Machine Learning,Large Language Models,and Natural Language Processing  \nVishnu S.PendyalaSan Jose State University,vishnu.pendyala@sjsu.edu  \nKarnavee Kamdar  \nOracle Corporation  \nKapil Mulchandani  \nAmazon.com,Inc.  \nFollow this and additional works at:https://scholarworks.sjsu.edu/faculty_rsca  \nRecommended Citation  \nVishnu S.Pendyala,Karnavee Kamdar,and Kapil Mulchandani.\"Automated Research Review SupportUsing Machine Learning,Large Language Models,and Natural Language Processing\"Electronics(Switzerland)(2025).https://doi.org/10.3390/electronics14020256  \nThis Article is brought to you for free and open access by SJSU ScholarWorks.It has been accepted for inclusion inFaculty Research,Scholarly,and Creative Activity by an authorized administrator of SJSU ScholarWorks.For moreinformation,please contact scholarworks@sjsu.edu.  \nArticle  \nAutomated Research Review Support Using Machine Learning,Large Language Models,and Natural Language Processing  \nVishnu S.Pendyala¹*D,Karnavee Kamdar²D and Kapil Mulchandani³D  \n1 Department of Applied Data Science,San Jose State University,San Jose,CA 95192,USA2 Oracle,Austin,TX78741,USA;kamdarkarnavee@gmail.com3 Amazon,Seattle,WA98170,USA;kapilmulchandani2019@gmail.com*Correspondence:vishnu.pendyala@sjsu.edu  \nAbstract:Research expands the boundaries of a subject,economy,and civilization.Peerreview is at the heart of research and is understandably an expensive process.This work,with human-in-the-loop,aims to support the research community in multiple ways.Itpredicts quality,and acceptance,and recommends reviewers.It helps the authors andeditors to evaluate research work using machine learning models developed based on adataset comprising 18,000+research papers,some of which are from highly acclaimed,top conferences in Artificial Intelligence such as NeurIPS and ICLR,their reviews,aspectscores,and accept/reject decisions.Using machine learning algorithms such as SupportVector Machines,Deep Learning Recurrent Neural Network architectures such as LSTM,awide variety of pre-trained word vectors using Word2Vec,GloVe,FastText,transformerarchitecture-based BERT,DistilBERT,Google’s Large Language Model(LLM),PaLM2,andTF-IDF vectorizer,a comprehensive system is built.For the system to be readily usable andto facilitate future enhancements,a frontend,a Flask server in the cloud,and a NOSQLdatabase at the backend are implemented,making it a complete system.The work is novelin using a unique blend of tools and techniques to address most aspects of building asystem to support the peer review process.The experiments result in a 86%test accuracyon acceptance prediction using DistilBERT.Results from other models are comparable,withPaLM-based LLMembeddings achieving 84%accuracy.  \ncheck for  \nupdates  \nAcademic Editor:Cecilio Angulo  \nKeywords:machine learning;peer review;large language models;long short-term memory;support vector machines;natural language processing  \nReceived:28 November 2024  \nRevised:30 December2024  \nAccepted:7 January 2025  \nPublished:9January 2025  \n# 1.Introduction\n\nCitation:Pendyala,V.S.;Kamdar,K.;Mulchandani,K.Automated ResearchReview Support Using MachineLearning,Large Language Models,and NaturalLanguage Processing.Electronics 2025,14,256.https://doi.org/10.3390/  \nThe pace of innovation in the recent times is astounding.While areas like natural lan-guage processing(NLP)and Large Language Models (LLMs)are evidencing an explosivegrowth,peer reviews are taking several weeks to even months.By the time the review ofa submitted research paper is turned around,it is quite likely that the research itself willbe obsolete.There is a need to improve the situation [1].A literature review shows thata number of projects have been implemented to expedite the peer review process.Still,a number of peer reviews take weeks and months,and in the real world,the timelinessof the peer review process has not improved much.The work described in this pap","cbCaigPSwwESOOmB","https://ap.wps.com/l/cbCaigPSwwESOOmB","pdf",7581063,1,27,"English","en",105,"# 1. 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