NOPR Collection:http://nopr.niscpr.res.in/handle/123456789/658982026-10-09T19:42:02Z2026-10-09T19:42:02ZOpen Access Publications from University Grants Commission Funded Research: A Bibliometric StudyChaudhary, PannaGadhvi, Dr. Geetabenhttp://nopr.niscpr.res.in/handle/123456789/659082025-05-27T08:08:25Z2025-06-01T00:00:00ZTitle: Open Access Publications from University Grants Commission Funded Research: A Bibliometric Study
Authors: Chaudhary, Panna; Gadhvi, Dr. Geetaben
Abstract: Research funding agencies play a crucial role in shaping the impact and dissemination of scholarly research. As Open
Access (OA) to research findings gains momentum, understanding the impact of funding agencies on OA publications
becomes essential. This study explores the impact of open access publications resulting from research funded by the
University Grants Commission (UGC), with a focus on the Indian context. It utilizes comprehensive publication data from
the Scopus database, specifically where UGC is listed as a funding sponsor. The analysis is entirely based on Scopus data,
with results drawn from entries where the term "UGC" appears. The study examines a total of 145,158 documents published
as an outcome of UGC research funding. Among these, 36,662 (25.25%) were found to be OA publications. Despite being
one third of total publications, these OA publications garnered significant attention, receiving 928,267 citations. This
citation impact of OA publications highlights the importance and effectiveness of UGC funding in promoting open access
publishing in research. The findings underscore the need for continued support and encouragement for open-access
initiatives by research funding agencies to enhance the accessibility and impact of scholarly research.
Page(s): 109-1192025-06-01T00:00:00ZDocument Landscape and Generative AI: A Synthetic Term DHISCREMENT Postulated for Nomenclature of Scholarly Library ResourcesMallik, Dr Soumenhttp://nopr.niscpr.res.in/handle/123456789/659072025-05-27T08:06:15Z2025-06-01T00:00:00ZTitle: Document Landscape and Generative AI: A Synthetic Term DHISCREMENT Postulated for Nomenclature of Scholarly Library Resources
Authors: Mallik, Dr Soumen
Abstract: Libraries are considered as storehouse of knowledge. Library acquires resources to satisfy the thirst for knowledge of the user community. A generic term ‘library documents’ is designated to the knowledge resources of libraries. Historically, ‘documents’ were used in legal perspective. Presently the notion ‘document’ is general in scope. The document horizon is found to be ever expanding. Adoption of the notion ‘document’ to designate library resources encountered semantic challenges. Defining documents and designating library resources as documents has ever been a dilemma to the librarians. Greatest of the library scientists like Paul Otlet and S R Ranganathan were of separate opinion regarding documents. Ranganathan defined documents on the basis of involvement of human intellect but included instrument generated records in the document category. The contemporary artificial intelligence (AI) influenced information scenario and application of generative AI in information generation extending the document landscape further. A lexicological warrant enforced the librimatic mind for postulation of suitable synthetic word ‘DHISCREMENT’ to designate library document of traditional sense for the sake of restoring semantic stability amongst documents and ‘library documents’.
Page(s): 120-1242025-06-01T00:00:00ZThe Landscape of LIS Education in India: Insights and Recommendations for the FutureShukla, PratibhaJaiswal, Prof. Babitahttp://nopr.niscpr.res.in/handle/123456789/659062025-05-27T08:04:41Z2025-06-01T00:00:00ZTitle: The Landscape of LIS Education in India: Insights and Recommendations for the Future
Authors: Shukla, Pratibha; Jaiswal, Prof. Babita
Abstract: This paper examines the current landscape of (LIS) Library and Information Science education in India, focusing on the program structures of Library Science in state and central universities, the types of courses offered, and the faculty strength. It also assesses the availability and clarity of information on university websites, regarding LIS programs. This study is based on data collected from the websites of state and central universities, LIS education in India. A comprehensive analysis of data was collected from the (LIS) Library and Information Science department’s websites. The complete list of state and central universities was taken from (UGC) University Grant commission’s website. Based on the findings, the paper provides several recommendations to improve the quality and relevance of LIS programs in India. The recommendations include clarifying faculty positions, enhancing the availability and accuracy of program information, and addressing identified gaps in the educational framework. By addressing these issues, the paper aims to contribute to the advancement of (LIS) Library and Information Science education in India, ensuring that future professionals are well-prepared to meet the evolving demands of the information landscape.
Page(s): 125-1372025-06-01T00:00:00ZArtificial Intelligence and Machine Learning in Fraud Detection: A Comprehensive Bibliometric Mapping of Research Trends and DirectionsThakkar, Dr. HimanshuDatta, SaptarshiBhadra, PriyamBarot, Dr. HareshJadav, Dr. Jayendrasinhhttp://nopr.niscpr.res.in/handle/123456789/659052025-05-27T08:02:53Z2025-06-01T00:00:00ZTitle: Artificial Intelligence and Machine Learning in Fraud Detection: A Comprehensive Bibliometric Mapping of Research Trends and Directions
Authors: Thakkar, Dr. Himanshu; Datta, Saptarshi; Bhadra, Priyam; Barot, Dr. Haresh; Jadav, Dr. Jayendrasinh
Abstract: This study presents a bibliographic analysis of emerging trends in applying artificial intelligence (AI) and machine learning (ML) to the detection and prevention of financial fraud and provides insights for future research. Bibliographic analysis on fraud data analysis helps researchers gain insight on research trends, research impact, and classification. Bibliometric analysis on fraud data analytics is helpful to researchers in getting insights on research trends, research impact and classification. However, research on fraud data analytics using machine learning is limited. The main objective of this quantitative analysis is to explore emerging trends in fraud data analytics and machine learning (ML) for financial crime detection and prevention. Bibliometric data has been collected from the Scopus database. One thousand four hundred eighty-three documents from the SCOPUS database have been analysed using VOSviewer. The data analysis divulges a growing interest in leveraging these technologies to strengthen financial crime detection. Fraud data analytics, Artificial Intelligence and Machine Learning are vital in identifying complex criminal patterns, strengthening companies in preventive vigilance, and ensuring fraud elimination. The study portrays the need for vigorous frameworks for the legislature, real-time analytics systems and more powerful tools and calls for integrating governments, financial institutions, and technology providers to strengthen prevention strategies and tackle financial crimes more effectively.
Page(s): 138-1502025-06-01T00:00:00Z