PurposeAI has rapidly changed academic libraries with applications like chatbots, automated cataloging, classification and data-driven decision-making. Artificial intelligence (AI) anxiety is a new type of technostress that library services are experiencing as a result of the rapid adoption of AI technologies, even with its advantages of library. The purpose of this study was to conduct a comprehensive study of existing research on AI anxiety in academic libraries and analyze it using bibliometrics.Design/methodology/approachThe study employs the bibliometrics and content analysis approach, which uses both numerical and visualization techniques to identify the extant research retrieved from the Web of Science and Scopus databases from 1996 to 2025. Author and country collaboration networks, hotspot distribution clustering and historical citation networks associated with AI anxiety were visualized by VOS viewer and R Studio.FindingsA total of 128 literature were eventually included. The number of AI anxiety publications has increased drastically since 2023. Growth trends, geographical area, leading journals and authors are identified. Top documents are indicated, with main and trending topics, which provide suggestions for researchers who conduct research in this field. Numerous studies on AI anxiety in academic libraries have been published in high-impact sources.Originality/valueTo the best of our knowledge, this is the first comprehensive and in-depth bibliometric research of trends and developments on AI anxiety literature. This study offers useful insights for policymakers, library professionals and researchers by identifying unexplored areas and outlining a strategy for reducing AI anxiety in academic library environments.

Migration, identity, and rights are significant issues of academic literature on Indian migrants that shape the sociopolitical discourse. However, there is limited research that analyses academic contributions related to this perspective. The present study attempts to fill that gap by evaluating global academic scholarship on Indian migration during the period 2000-2025. The study investigates publishing trends, prolific authors, sources of impact, active affiliations, and author's keywords. The study analyzes 848 articles obtained from Scopus and Web of Science, using Bibliometrix and VOSviewer for data analysis and visualization. The results show that publication trends began to grow significantly after 2018. The most active sources are Contemporary South Asia and Journal of Ethnic and Migration Studies. The most prominent country was India, followed by the USA, the United Kingdom, and Canada. Keyword co-occurrence analysis examines major thematic clusters centered on "India," "citizenship," "identity," "migration," "Indian diaspora," and "gender," reflecting the evolving intellectual structure of the topic. The present study provides a comprehensive quantitative mapping of academic literature on Indian migrants and contributes to a deeper understanding of its development, thematic evolution, and research gaps, offering directions for future research.

Folk medicine remains an important component of health practices in Saudi Arabia and continues to attract growing scholarly attention. This study maps the scholarly landscape of folk medicine research related to Saudi Arabia by examining publication trends, author keywords, collaboration among institutions and countries, and the impact of authors and sources. Publications were retrieved from the Scopus and Web of Science databases using Saudi Arabia as the country affiliation. After PRISMA-guided identification, screening, deduplication, and eligibility assessment, 2527 publications published between 1989 and 2024 were retained for analysis. Bibliometrix was used for bibliometric computation and visualization, and VOSviewer was used to map country co-authorship and keyword co-occurrence networks. The results show sustained growth in publication output, strong participation by Saudi institutions, and broad international collaboration. The most prominent research themes were antioxidant activity, antimicrobial properties, herbal medicine, and traditional medicine. These findings provide a bibliometric overview of knowledge production in this area and may inform future systematic, clinical, and policy-oriented research on traditional medicine in Saudi Arabia.

This research examines the impact of unfulfilled publication promises on Indian library professionals' perceptions of academic conferences, focusing on credibility and transparency. A cross-sectional survey of 184 respondents explores demographic influences, conference participation patterns, and attitudes toward organizers. Findings highlight the significant effects of experience and library type on attendance, while unkept publication commitments reduce conference value and credibility. Factor analysis identifies two key dimensions, diminished value and credibility, accounting for 69.4 per cent of response variance. Broken promises are perceived as misleading and harmful to conference reputations, influencing professionals' willingness to attend or recommend future events. The study stresses organizers' ethical obligations to ensure transparency in publication processes, vital for sustaining credibility and trust among professionals. It offers insights for improving conference management and maintaining integrity in the library and information science field, benefiting both organizers and attendees.

Advanced glycation end-products (AGEs) arise from non-enzymatic reactions between reducing sugars and proteins, contributing to oxidative stress and metabolic dysfunction. Excessive AGE accumulation is implicated in chronic diabetic complications and may also be relevant to acute metabolic disturbances encountered in emergency medicine. N-acetylcysteine (NAC), a naturally occurring antioxidant found in Allium species, has demonstrated potential to attenuate oxidative and glycation-mediated damage. The effect of NAC on d-ribose-induced glycation of bovine serum albumin (BSA) was investigated using multiple physicochemical and spectroscopic techniques. AGE formation was assessed by measuring hyperchromicity, early glycation products (ketoamines), carbonyl content, hydroxymethylfurfural (HMF) levels, and fluorescent AGEs. The protective effect of NAC was further evaluated by determining free lysine and arginine contents. Protein aggregation and conformational changes were analyzed using Congo Red binding and fluorescence assays including thioflavin-T and 1-anilinonaphthalene-8-sulfonic acid. NAC significantly inhibited d-ribose-mediated glycation of BSA in a concentration-dependent manner. Treatment with NAC resulted in reduced hyperchromicity, decreased ketoamine formation, and lower carbonyl, HMF, and fluorescent AGE levels. NAC preserved protein integrity by maintaining higher free lysine and arginine contents. In addition, NAC markedly attenuated glycation-induced protein aggregation, as evidenced by reduced Congo Red binding and diminished thioflavin-T and ANS fluorescence, with maximal protection observed at 300 μM. NAC exhibits pronounced anti-glycation and anti-aggregation effects by limiting oxidative stress and glycation-mediated protein modification. These findings demonstrate that NAC effectively attenuates d-ribose-induced glycation and protein aggregation in vitro and provide mechanistic insights into its anti-glycation properties through multiple complementary biochemical mechanisms. Further studies are warranted to evaluate its biological relevance in more complex experimental models.

PurposeDeepfakes, which first appeared in 2017, use artificial intelligence (AI) to generate modified but incredibly realistic digital content, including political satire, posing serious moral, legal, and societal concerns. The purpose of the study is to identify the evolving landscape of deepfake technology, with a focus on its psychological and societal impacts.Design/methodology/approachThe researchers carried out a comprehensive bibliometric analysis of research from 2019 to 2024, using data obtained from the Web of Science and Scopus bibliographic databases. After combining 688 bibliographic entries from both databases using Bibliometrix R. After eliminating non-English publications and studies irrelevant to the bibliometric scope of deepfake research, a total of 463 articles were included for further study.FindingsBased on our results, the researchers found that academic scholarships on detection techniques, misinformation, and social impact has grown rapidly, particularly in China, the United States, and India. Six main study areas have emerged, such as deepfake detection methods, information integrity, machine learning, social media influences, forensic analysis, and facial manipulation. The findings reveal that the increasing complexity of deepfake generation and its consequences for ethical concerns significantly affects digital trust, privacy infringement, and the spread of misinformation.Originality/valueMore interdisciplinary techniques combining AI, social sciences and ethics are required despite notable gains in detection. To reduce the risks associated with deepfakes, the study highlights the significance of strong detection technologies, regulatory frameworks and public awareness. The psychological effects of deepfake exposure and the creation of moral standards for appropriate AI use should be the focus of future studies.

Electronic medical records (EMRs) are critical, highly sensitive private information in health sector, and manage patient health information. The purpose of the study identifies a unique opportunity to develop a secure for maternal child health care. This work uses the Scopus, PubMed, and Web of Science databases to analyse the most productive authors, journals, institutions, and countries, as well as the most cited papers and the citing articles. The investigation used bibliometric analysis to identify the bibliographic data from 501 articles published from 2011 and 2025 and implementation of EMRs for maternal and child healthcare. Based on the findings, the issue is multifaceted and involves various stakeholders, including health care providers, patients, and technological issues within the health sectors. The proposed work can significantly reduce the turnaround time for EMRs sharing, improve decision making for medical care, and reduce the overall cost of medical instruments.

PurposeThis study aims to assess AI literacy and attitudes among medical students and explore their implications for integrating AI into healthcare practice.Design/methodology/approachA quantitative research design was employed to comprehensively evaluate AI literacy and attitudes among 374 Lusaka Apex Medical University medical students. Data were collected from April 3, 2024, to April 30, 2024, using a closed-ended questionnaire. The questionnaire covered various aspects of AI literacy, perceived benefits of AI in healthcare, strategies for staying informed about AI, relevant AI applications for future practice, concerns related to AI algorithm training and AI-based chatbots in healthcare.FindingsThe study revealed varying levels of AI literacy among medical students with a basic understanding of AI principles. Perceptions regarding AI’s role in healthcare varied, with recognition of key benefits such as improved diagnosis accuracy and enhanced treatment planning. Students relied predominantly on online resources to stay informed about AI. Concerns included bias reinforcement, data privacy and over-reliance on technology.Originality/valueThis study contributes original insights into medical students' AI literacy and attitudes, highlighting the need for targeted educational interventions and ethical considerations in AI integration within medical education and practice.

The growing prevalence of retracted articles has heightened concerns about research integrity and the reliability of scholarly resources. This study examines the awareness and responsibilities of library professionals in India regarding managing retracted articles. Using a quantitative cross-sectional survey, data were gathered from 193 professionals (librarians, assistant librarians, deputy librarians and library assistants). The survey assessed familiarity with retraction processes, management practices and mitigation strategies. Findings indicate moderate awareness influenced by designation, experience and education. Common practices include updating catalogue metadata, flagging retracted content and notifying users; however, limited visibility in institutional repositories undermines credibility. To improve practical uptake, we propose a concise four-pillar framework – Policy and Governance, Capacity Building, Technology and Infrastructure and User Support and Engagement – with phased, resource-sensitive actions to strengthen retraction management and research integrity.

Cancer prognosis of complications like metastasis, recurrence, and side effects of treatments is important to enhance patient prognosis. There is great potential in the use of ML on lifetime data for improving prediction accuracy in oncology; however, there is no systematic review of the subject. This SRMA is intended to assess the accuracy of ML models based on longitudinal studies for the estimation of cancer-related complications. The articles were identified from PubMed, Google Scholar, and IEEE Xplore databases for the years 2020 to 2024. Seven of the studies reviewed in the paper analyzed ML models that employed longitudinal data for cancer complication prognosis. The risk of bias of included studies was assessed using the Cochrane Risk of Bias tool, and for diagnostic accuracy, the QUADES 2 tool was used. Information on ML techniques, prediction accuracy, and results was obtained. The pooled area under the curve (AUC) for immune-related adverse events prediction was 0.78 (95% CI: 0.73-0.83). For cancer recurrence and mortality prediction, pooled AUCs ranged from 0.70 to 0.75. Machine learning models integrating clinical, genomic, and imaging data demonstrated superior predictive accuracy across various cancer types. Models predicting quality of life deterioration during treatment showed an AUC of 0.82. ML models applying longitudinal data effectively predict cancer complications with improved accuracy when integrating multimodal data. These models offer promising tools for clinical decision-making in oncology.

This study explores the perceptions, effectiveness, and ethical implications of AI tools in academic writing among PhD scholars in India. It evaluates the comparative effectiveness of AI tools versus traditional methods for paraphrasing, plagiarism detection, and citation improvement. The research also investigates user confidence in AI tools’ adaptability to various academic disciplines and their potential to support critical thinking and academic integrity. A structured quantitative approach was employed, utilizing validated surveys with 184 respondents from the University of Mumbai. Findings indicate that while AI tools enhance clarity, flow, and efficiency, they raise concerns about over-reliance, originality, and ethical transparency. Advanced-year scholars valued citation improvements but were critical of originality-related functions. The study emphasizes responsible AI use, advocating for its role as a complementary, supportive tool to foster creativity and intellectual rigor. These findings contribute to the ongoing discourse on integrating AI responsibly into academic practices.

With IoT networks expected to exceed 29 billion connected devices by 2030, the risk of cyberattacks has never been higher. As more devices come online, the attack surface for hackers continues to expand, making cybersecurity a pressing concern. Intrusion Detection Systems (IDS) are essential for identifying and mitigating these threats in real-time. However, a significant challenge IDS faces is dealing with imbalanced datasets, where attack instances are significantly underrepresented compared to normal traffic. Training models on such skewed data leads to a bias toward majority-class patterns, reducing their ability to detect intrusions effectively. To address this issue, this work introduces CSMCR (Cosine Similarity-based Majority Class Reduction), a novel technique that selectively removes redundant majority-class samples while preserving dataset integrity. Unlike traditional approaches like SMOTE (oversampling) or random undersampling, CSMCR ensures that the retained majority instances remain diverse by analyzing feature-wise similarity. This prevents unnecessary data duplication and minimizes information loss. Additionally, we developed a hybrid deep learning model integrating RegNet and FBNet architectures to enhance feature extraction and classification performance. Experimental results on multiple IDS datasets confirm that balancing the dataset to a 1:1 ratio optimally prevents overfitting and improves model interpretability. The proposed model achieved an F1-score of 0.9758 on RT-IoT2022 and 0.9275 on UNSW Bot-IoT, outperforming SMOTE-based methods in accuracy and computational efficiency. Notably, CSMCR reduced training time by 53% compared to conventional oversampling techniques. Incremental training evaluations reveal that bias formation reduces performance beyond a 1:2 majority-to-minority ratio. These findings establish CSMCR as a robust, scalable, and computationally efficient IDS balancing strategy tailored for IoT network security.

Purpose - In a period of rapid information dissemination, the spread of misinformation poses an important threat to public understanding and democratic processes. This study aims to examine the development of generative artificial intelligence (AI) models with the objective of automating the fact-checking of reports and reducing the impact of misinformation. Design/methodology/approach - The study employed Bibliometrix R and VOS viewer tools to perform a thorough assessment of collaborative networks, research trends and important themes in the field of AI fact-checking journalism. The data was retrieved from the Scopus database that includes 282 publications between 2022 and 2024 to provide a bibliometric study on influence of AI on fact-checking in journalism. Findings - The results show a notable increase in scholarly research output and examined the top prominent authors, active organizations and nations/countries that are advancing AI fact-checking in journalism research. The study identifies commonly explored key themes, such as automation, disinformation and ethical problems and co-authorship networks. Originality/value - This study seeks to fill knowledge gaps by mapping the conceptual structure of AI tools in journalism articles, thereby offering an in-depth understanding of AI's evolving influence in journalism ethics and fact-checking.

The study explores the role of Indian academic library professionals in advancing Open Science (OS), focusing on their familiarity with OS practices, challenges faced, and training needs. Open Science, encompassing transparency, accessibility, and collaboration in research, requires active participation from libraries as facilitators. A survey among 362 library professionals from Indian academic institutions reveals that while professionals exhibit moderate familiarity with Open Science tools and practices such as open-access publishing, data sharing, and collaborative platforms, significant gaps persist in technical expertise and institutional support. Challenges include limited resources, insufficient infrastructure, lack of awareness, and stakeholder resistance to change. The study highlights the evolving roles of librarians in promoting OS, including data curation, policy advocacy, and researcher training. However, inconsistent adoption of OS practices and barriers like inadequate funding and unclear institutional policies hinder progress. Training in technical skills, advocacy, and open data management is critical to empower librarians to support OS initiatives effectively. The findings underscore the importance of institutional support, infrastructural improvements, and targeted training programs to enhance the library profession’s contribution to the Open Science movement in India.

This study examines the influence of diamond open access (DOA) publishing on research engagement among library professionals in India. Using a quantitative approach, data were collected through a structured online survey administered to 106 library professionals who had published in DOA journals. The findings reveal a strong consensus on the benefits of DOA, particularly its capacity to ensure equitable access to scholarly content by eliminating article processing charges (APCs) and enhancing the visibility and impact of research outputs. Respondents emphasized DOA's potential to promote inclusivity, especially for researchers in under-resourced settings, and its role in fostering collaboration and diversity in scholarly publishing. However, the study also identifies critical challenges that hinder the widespread adoption of DOA. These include limited journal availability, concerns about journal quality and prestige, and the uncertain sustainability of the fee-free model. Despite these concerns, statistical analyses indicate that library professionals regard DOA as a viable and impactful alternative to gold open access, especially in terms of long-term benefits to scholarly communication. The study highlights the evolving role of library professionals as not just information custodians but also active contributors and advocates in the open access movement. It recommends institutional recognition and support for library professionals as strategic stewards of DOA publishing initiatives. The findings underscore the need for sustained advocacy, infrastructure development, and collaborative funding models to realize the full potential of DOA. Ultimately, this research contributes valuable insights into how DOA can support a more inclusive, accessible, and sustainable academic publishing landscape in the Global South.

As digital thesis repositories increasingly support academic transparency, the inclusion of handwritten signatures in open-access doctoral theses has raised growing concerns about data privacy and identity security. In India, although the Shodhganga repository does not mandate public display of such signatures, many universities upload scanned approval pages containing them often without explicit consent. This study investigates the perceptions of PhD students, recent alumni, and faculty supervisors regarding the visibility of handwritten signatures in Shodhganga. Based on responses from 129 participants across central and state universities, the findings reveal that respondents from state universities expressed significantly greater discomfort with signature exposure. Most participants cited risks of identity theft, forgery, and misuse of personal identifiers, and showed strong support for alternative authentication methods such as digital watermarking, encrypted verification systems, DOI-based tracking, and institutional approval seals. In conclusion, the study confirms a widespread stakeholder preference for secure, consent-based alternatives that preserve thesis authenticity while protecting researcher identity. These findings emphasize the urgent need for policy reforms in India's digital thesis authentication infrastructure to align with global privacy and security standards.

PurposeThis study aims to explore the ethical readiness and awareness of data reuse among library and information science (LIS) professionals in India. With the global research landscape increasingly embracing open science, this study seeks to assess the understanding, engagement and preparedness of Indian LIS professionals regarding ethical data reuse practices and international frameworks, such as FAIR, CARE and general data protection regulation.Design/methodology/approachA quantitative, cross-sectional survey design was used, gathering responses from 178 LIS professionals, academics and research scholars across universities, colleges and special libraries in India. A structured online questionnaire measured conceptual awareness, policy knowledge and ethical preparedness. Descriptive statistics, t-tests, ANOVA and reliability analysis were used to interpret the data and identify statistically significant differences across demographic groups.FindingsFindings reveal a moderate level of awareness regarding ethical data reuse, with higher understanding correlated with academic role, professional experience and educational qualifications. However, awareness and preparedness were not significantly influenced by gender, age or institution type. Early-career and highly experienced professionals exhibited greater ethical readiness, likely due to recent training or accumulated experience. Major gaps were identified in licensing knowledge, institutional policy support and training access, underscoring systemic challenges to ethical data stewardship in India.Originality/valueTo the best of the authors' knowledge, this study is among the first to systematically evaluate ethical awareness and readiness for data reuse within the LIS profession in India. It offers localized insights into institutional and educational shortcomings and proposes actionable strategies for integrating global ethical standards into national LIS practices. The research contributes to bridging the gap between global data governance frameworks and their practical implementation in developing contexts, reinforcing the importance of ethics in open science initiatives.

This study explores the role of gamification in enhancing academic library services in India by surveying library leaders across various institutions. Using game-like elements in non-game contexts, gamification can boost user engagement and improve services such as information literacy and research consultations. Findings reveal moderate awareness and generally positive perceptions of gamification's effectiveness. However, challenges like insufficient staff expertise, infrastructure, and limited funding hinder implementation. The study emphasises the need for additional resources, including staff training and technological upgrades, to unlock the full potential of gamification in academic libraries.

This study investigates the perceived impact of procrastination on academic performance among undergraduate BBA students and evaluates the effectiveness of AI tools in mitigating procrastination. A structured questionnaire was administered to 202 respondents from St. Joseph’s College of Commerce (Autonomous), Bengaluru. Results indicate that procrastination is prevalent, with assignments (58.9 %) and research tasks (53 %) being the most frequently delayed. The primary causes of procrastination include lack of motivation (54 %), distractions (47 %), and overwhelming workload (31.2 %). AI tools such as ChatGPT and Grammarly were widely used, with factor analysis identifying two key dimensions of AI effectiveness: Reduce Distractions and Task Completion. While no significant gender-based differences were observed, older students perceived AI tools as more effective in enhancing task management (p = 0.041). Simplified task breakdowns, task prioritization, and AI-generated summaries were identified as the most beneficial AI features. The study highlights the need for increased integration of AI tools into academic workflows, along with structured guidelines and digital literacy programs to maximize their perceived impact on reducing procrastination and enhancing academic performance.