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IMPACT FACTOR 3.394 an Open Access Journal by MDPI IMPACT FACTOR 3.394 an Open Access Journal by MDPI Editor-in-Chief Message from the Editorial Board Prof. Dr. Stephen D. Meriney You are invited to contribute a research article or a comprehensive review for consideration and publication in Brain Sciences (ISSN 2076-3425). Brain Sciences is an open access, peer-reviewed scientific journal that publishes original articles, critical reviews, research notes, and short communications on neuroscience. The scientific community and the general public can access the content free of charge as soon as it is published. We would be pleased to welcome you as one of our authors. Author Benefits Open Access Unlimited and free access for readers No Copyright Constraints Retain copyright of your work and free use of your article Thorough Peer-Review Coverage by Leading Indexing Services SCIE - Science Citation Index Expanded (Clarivate Analytics), Pubmed (NLM), Scopus (Elsevier), PsycINFO (American Psychological Association), EMBASE (Elsevier) No Space Constraints, No Extra Space or Color Charges No restriction on the length of the papers, number of figures or colors Discounts on Article Processing Charges (APC) If you belong to an institute that participates with the MDPI Institutional Open Access Program (IOAP) Aims and Scope Brain Sciences is a peer-reviewed scientific journal that publishes original articles, critical reviews, research notes, and short communications in the neuroscience areas. The scope of Brain Sciences includes : Cognitive neuroscience Clinical neuroscience Developmental neuroscience Systems neuroscience Molecular and cellular neuroscience Neural engineering Theoretical and computational neuroscience Neuroimaging Neurobiology of Language Neuroglia Environmental neuroscience Educational neuroscience Behavioral neuroscience Editorial Office Brain Sciences Editorial Office [email protected] MDPI, St. Alban-Anlage 66 4052 Basel, Switzerland Tel: +41 61 683 77 34 Fax: +41 61 302 89 18 www.mdpi.com mdpi.com/journal/brainsci MDPI is a member of Follow Us facebook.com/MDPIOpenAccessPublishing twitter.com/MDPIOpenAccess linkedin.com/company/mdpi weibo.com/mdpicn Wechat: MDPI-China blog.mdpi.com www.mdpi.com mdpi.com/journal/brainsci See www.mdpi.com for a full list of offices and contact information. MDPI is a company registered in Basel, Switzerland, No. CH-270.3.014.334-3, whose registered office is at St. Alban-Anlage 66, CH-4052 Basel, Switzerland. Basel, July 2021.

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In-Text Citation's Frequencies-Based Recommendations of Relevant
In-text citation's frequencies-based recommendations of relevant research papers Abdul Shahid1, Muhammad Tanvir Afzal2, Abdullah Alharbi3, Hanan Aljuaid4 and Shaha Al-Otaibi5 1 Institute of Computing, Kohat University of Science & Technology, Kohat, Pakistan 2 Department of Computer Science, NAMAL Institute, Mianwali, Pakistan 3 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia 4 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University (PNU), Riyadh, Saudi Arabia 5 Information Systems Department, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia ABSTRACT From the past half of a century, identification of the relevant documents is deemed an active area of research due to the rapid increase of data on the web. The traditional models to retrieve relevant documents are based on bibliographic information such as Bibliographic coupling, Co-citations, and Direct citations. However, in the recent past, the scientific community has started to employ textual features to improve existing models' accuracy. In our previous study, we found that analysis of citations at a deep level (i.e., content level) can play a paramount role in finding more relevant documents than surface level (i.e., just bibliography details). We found that cited and citing papers have a high degree of relevancy when in-text citations frequency of the cited paper is more than five times in the citing paper's text. This paper is an extension of our previous study in terms of its evaluation of a comprehensive dataset. Moreover, the study results are also compared with other state-of-the-art approaches i.e., content, metadata, and bibliography.
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