Frequency Of Keywords Research Articles

Impact of the ventral hernia working group’s publication: a bibliometric analysis

PurposeThe Ventral Hernia Working Group (VHWG) proposed a ventral hernia grading guideline, primarily supported by expert opinion, recommending biologic mesh placement in high-risk patients. We investigated the relationship between this industry-sponsored guideline and discourse around ventral hernia repair (VHR).MethodsMedline platform from Web of Science’s database identified publications “pre-VHWG”(1999-01-01 to 2009-12-31), and “post-VHWG”(2010-01-01 to 2020-12-31) describing VHR and complications or recurrence of VHR with the following comorbidities: COPD, smoking, diabetes, immunosuppression, or obesity. Poisson regression analyzed keyword frequency over time using logarithmically transformed data.ResultsOf 1291 VHR publications identified pre-VHWG and 3041 publications identified post-VHWG, 172 (13.3%) and 642 (21.1%) publications respectively included prespecified keywords. The keyword groups “biologic”(IRR 3.39,95%CI1.34-11.4,p = 0.022) and “comorbid”(IRR 1.95, 95%CI1.09-3.74,p = 0.033) significantly increased with frequency after publication of the VHWG.ConclusionThe VHWG publication likely contributed to a focus on comorbidities and biologic mesh in the ensuing literature within the field of VHR.

Research areas and trends in family-centered care in the 21st century: a bibliometric review.

Family-centered care (FCC) is a model of care provision that sees a patient's loved ones as essential partners to the health care team and positively influences the psychological safety of patients and loved ones. This review aims to present an overview of impactful publications, authors, institutions, journals, countries, fields of application and trends of FCC in the 21st century as well as suggestions on further research. The Web of Science Database was searched for publications on FCC between January 2000 and Dezember 2023. After screening for duplicates, VOS Viewer and CiteSpace were used to analyze and visualize the data. Scientific interest in FCC has grown and resulted in the scientific output of 4,836 publications originating from 103 different countries. Based on the frequent author keywords, FCC was of greatest interest in neonatology and pediatrics, nursing, critical and intensive care, end-of-life and palliative care, and patient-related outcomes. The recent research hotspots are "patient engagement," "qualitative study," and "health literacy." FCC has gained recognition and spread from the pediatric to the adult palliative, intensive, end-of-life and geriatric care settings. This is a very reassuring development since adults, especially when older, want and need the assistance of their social support systems. Recent research directions include the involvement of patients in the development of FCC strategies, health literacy interventions and the uptake of telemedicine solutions.

A bibliometric analysis of immune-related adverse events in cancer patients and a meta-analysis of immune-related adverse events in patients with hepatocellular carcinoma.

Immunotherapy has become the standard treatment for hepatocellular carcinoma (HCC), but it carries a risk of immune-related adverse events (irAEs) that can be life-threatening. This study employs bibliometric analysis to understand global scientific research on irAEs in cancer, focusing on characteristics and areas of interest. Additionally, a meta-analysis provides a comprehensive overview of irAEs in HCC patients receiving immune checkpoint inhibitor (ICI)-based therapies. We conducted a thorough search of Web of Science Core Collection (WoSCC) publications from 1999 to 2022. R and VOSviewer software were used for analysis. A meta-analysis was performed using data from PubMed, Embase, and the Cochrane Library databases up to March 22, 2022. Trials with HCC patients reporting irAE incidence were included. Quality assessment followed Cochrane risk of bias, Newcastle-Ottawa Scale (NOS), and Methodological Index for Non-Randomized Studies (MINORS). We used random-effects or fixed-effects models based on I2 values. Primary outcomes included any-grade irAEs and grade ≥ 3 irAEs. This review and meta-analysis are registered in PROSPERO as CRD42022318885. In bibliometric analysis, we included 2946 papers, showing a consistent rise in annual publications on irAEs in cancer research. Frequent keywords were "nivolumab", "immune checkpoint inhibitor", and "immune-related adverse event". "Hepatocellular carcinoma" emerged as a prominent research focus linked to irAEs. We conducted a comprehensive meta-analysis on irAE incidence in HCC patients, including 29 studies. The overall incidence of any-grade irAEs was 61.0% (95% CI 38.5%-81.3%), and grade ≥ 3 irAEs was 13.2% (95% CI 7.9%-19.6%). Treatment-related mortality occurred in 3.1% (95% CI 0.8%-6.3%), with treatment discontinuation at 10.7% (95% CI 6.3%-16.0%). Reactive cutaneous capillary endothelial proliferation (RCCEP) was the most common any-grade irAE, while elevated aspartate aminotransferase (AST) was the most common grade ≥ 3 irAE. Treatment strategies were independently associated with specific irAEs, as indicated by multivariable analysis. This study provides valuable insights into the current research landscape of irAEs in cancer and ofers a comprehensive overview of irAEs in HCC patients undergoing ICI-based therapy. The relatively high incidence of irAEs and their association with treatment strategies emphasize the need for careful management by clinicians when treating HCC patients. These findings offer significant guidance for optimizing care and treatment for HCC patients.

Developing the Potential of Women Foodpreneur by Using the Skill Will Matrix: A Case Study of Dapur Mawar Program

Small and medium-scale entrepreneurs mainly consist of women. Women's small businesses in developing communities can drive the economy for generations. To succeed in the business world, women entrepreneurs need to have high skills and knowledge. Entrepreneurship training and skills are essential, especially for potential entrepreneurs who want to develop and expand a business. Thus, it is essential to identify and evaluate existing and foreseeable skills and abilities. Entrepreneurial skills can be learned, formed, and mastered by an individual through the entrepreneurial education process or experience. The Dapur Mawar project aims to provide training and education for women foodprenuers. Before the training module's implementation, the trainers employed a skill-will matrix to examine the willingness and skills of trainees. This study uses a semi-structured interview method. Twenty-three women foodprenuers have been selected as key informants using purposive sampling. Deductive content analysis for analyzing qualitative data was employed to quantify the frequency of keywords. Pre-existing quadrants are applied to the data through a deductive approach. The findings revealed that critical informants could be categorized into two categories: 1) an entrepreneur with high skill and high motivation and 2) an entrepreneur with low skill and high motivation. The result of the analysis is used to prepare the Dapur Mawar program. The implications of the study and future research suggestions are also justified.

Ecoanxiety and mental health unveiled: a bibliometric analysis

Ecoanxiety, which encompasses the psychological impacts of environmental change, has emerged as a pressing global concern. However, the complex interrelationship between environmental factors and mental health in the context of ecoanxiety remains underexplored. This bibliometric analysis examines the evolution of international research on ecoanxiety and mental health using the Scopus and Web of Science databases. Parameters analyzed include publication trends over time, contributing countries, research foci, and keyword frequencies related to climate change, ecoanxiety, mental health, and solastalgia. The findings reveal surging scholarship in recent years, exponential publication growth, and increasing international collaborations. The total of 214 documents initially retrieved, 122 peer-reviewed publications met the inclusion criteria after pre-processing. The analysis provides a comprehensive overview of the current state of ecoanxiety and mental health research. It elucidates patterns in the emergence and progression of this burgeoning field to inform future research directions. Specifically, the elucidated features regarding keyword usage and research trends establish a foundation to advance investigations on the nexus between environmental issues and psychological well-being. This bibliometric study synthesizes existing knowledge and unveils fruitful avenues to progress understanding of the psychological ramifications of ecological crises.

Analyzing Media Coverage Trends on Undocumented Migrant Children in South Korea

Objectives: This study examined the impact of media coverage on the public perceptions of undocumented migrant children in South Korea, highlighting their societal exclusion due to their undocumented status. Thise research explores how media shapes the awareness of these vulnerable minors, which is crucial for both their development and societal progress.Methods: We analyzed 302 articles from BIGKinds, a news service of the Korea Press Foundation, published from 1990 to 2023. These articles contained terms related to ‘undocumented migrant children,’ totaling 69,278 keywords. The analysis was conducted using R 4.3.1 with packages such as wordcloud2 and ggplot2 to assess keyword frequency and trends over the decades.Results: From the analysis, 69,240 keywords were identified, with the most common being “children, arrest, parents, foreigner, illegal, resident, rights, protection, birth, offspring, support, school”. Media trends have evolved, with an overall increase in coverage, particularly during significant societal events.Conclusion: Media portrayals have shifted from viewing these children merely as dependents of foreign workers to recognizing them as individuals. Residency remained a persistent theme, highlighting it as an unresolved issue. This study highlights the critical role of the media in informing the public about marginalized individuals and influencing societal engagement and perceptions.

CASH WAQF FOR MICROFINANCE: A BIBLIOMETRIC ANALYSIS

Introduction: Waqf is one of the Islamic social financial instruments, having the distinctive characteristic that waqf assets are donated voluntarily by the wakif and are permanently designated for social benefits. Waqf can be used for productive or commercial activities to generate profits by Sharia law. Cash waqf can play a role in supporting the overall development process, both in human resource development, as well as in economic and social development. This study tries to explore the role of cash waqf for microfinance which can subsequently help the people's economy. Methods: The research method used in this research is quantitative research with bibliometric analysis. Data sources collected secondary data obtained from the Google Scholar database. Data were obtained using Publish or Perish (POP) software and analyzed using VOSviewer software. Results: The results of this research show that, from data searches using the keywords "cash waqf", "microfinance" and "sharia microfinance" by limiting 500 papers in the 2010-2024 time period, the words "microfinance institutions" are the most frequent keywords, indicated by a darker color than other keywords. Conclusion and suggestion: Based on the results and analysis, it can be concluded that, from year to year, research on the topic of cash waqf for microfinance is most often used. However, research on the topic of cash waqf for Sharia microfinance is still limited. Therefore, future research can be carried out regarding cash waqf for sharia microfinance. Cash waqf plays an important role in supporting the overall development process, both in human resource development, as well as in economic and social development. The potential of cash waqf can be used as a means of economic empowerment for lower-middle-class communities with the role played by microfinance institutions.

유튜브 댓글 분석을 통한 클래식 음악 시청자 반응 연구

This study analyzed Korean-language comments on YouTube videos of pianist Lim Yunchan’s performance of Rachmaninoff’s Piano Concerto No. 3 during the 2022 Van Cliburn International Piano Competition finals, exploring domestic viewers’ reactions and emotional perceptions of classical music. The research methodology involved selecting only Korean comments collected via the YouTube Data API from June 20, 2022, to December 1, 2023, refining the data, and conducting keyword frequency analysis, LDA Topic Modeling, and sentiment analysis using R. The analysis indicated that viewers expressed deep emotional resonance and admiration for the artistic excellence of Lim’s performance through the identified keywords. The LDA Topic Modeling yielded three themes: ‘the Van Cliburn Competition’, ‘viewer responses’, and ‘the bright future of K-Classic’. Although the sentiment analysis showed some limitations, predominantly positive responses were observed. This study provides substantive data on how viewing classical music performances on YouTube can elicit emotional resonance and admiration for artistic excellence in viewers. Additionally, it confirmed that YouTube plays a crucial role in enhancing the accessibility of classical music and serving as a conduit for appreciation and communication. This research contributes to understanding the current landscape of Korea’s classical music consumption and offers insights into the consumption patterns of classical music in the digital media era.

Bibliometric and trend analysis of the top 100 most-cited articles on lateral interbody fusion (LIF).

Lateral interbody fusion (LIF) is an increasingly popular minimally-invasive spine procedure. This study identifies notable trends in LIF literature and provides a detailed review of the bibliometric aspects of the top 100 most-cited articles. Articles were queried from the Web of Science database. Inclusion criteria consisted of peer-reviewed articles, full-text availability, and LIF focus. Network analysis including co-authorship mapping and bibliographic coupling were complemented by trend analysis to determine prominent contributors and themes. Analyses were conducted using VOSviewer and Bibliometrix (RStudio). There has been a rapid increase in LIF publication and citation count since 1998. Leading journals were Spine (n = 24), Journal of Neurosurgery Spine (n = 22), and European Spine Journal (n = 12). NuVasive funded the most publications (n = 17), followed by DePuy Synthes Spine (n = 4). The United States was the most represented country (n = 81); however, trend analysis suggests a steadily growing international contribution. The most prolific author was J.S. Uribe (n = 16), followed by a tie in second place by E. Dakwar and L. Pimenta (n = 8). The most frequent keywords, "complication" (n = 34), "surgery" (n = 30), and "outcomes" (n = 24), demonstrated a patient-centric theme. This bibliometric analysis provides in-depth insights into the evolution and trends of LIF over the last two decades. The trends and themes identified demonstrate the innovative, collaborative, and patient-focused characteristics of this subfield. Future researchers can use this as a foundation for understanding the past and present state of LIF research while designing investigations.

Effectiveness of Emergency Response to Typhoons: Lessons Learned from Historical Typhoon Disasters in Zhuhai, China

Typhoons significantly impact socio-economics, varying in impact due to unique characteristics. However, limited studies explore why similar intensity typhoons affect regions differently. This study compares the emergency response measures of Zhuhai, Guangdong Province, China, in response to typhoon Hato (2017), Mangkhut (2018), and Higos (2020). These three similar typhoons offer an empirical opportunity to assess the effectiveness of emergency management. This research builds a timeline of Zhuhai's emergency response measures to the typhoons, collects Weibo data 72 hours before and after the typhoon landfall, performs keyword frequency analysis on contents and analyzes in detail the content of weibo postings, and changes in sentiment. Results show that timely and systematic emergency response measures may reduce disaster losses, which means emergency management is crucial in mitigating typhoon impacts, offering valuable lessons for other coastal cities.

대학 강의평가 서술형 문항의 감정분석 및 의미연결망 분석

Objectives The purpose of this study was to derive directions and implications for quality of education through sentiment analysis and semantic network analysis of university descriptive course evaluations. Methods The analysis data consisted of 243,900 descriptive lecture evaluations from the first semester of 2020 to the first semester of 2023 at University A. Emotion words were classified in the order of positive, negative, and neutral, and the frequency and ratio of descriptive lecture evaluation contents by year, subject, and college were calculated. N-gram and clustering were analyzed using Textome, and network centrality between keywords was analyzed and visualized using Ucinet 6. Results First, in the sentiment analysis trend for descriptive lecture evaluation questions by year, course com-pleted, and university, positive opinions were found to be dominant. Second, as a result of the keyword frequency and N-gram analysis of the lecture evaluation contents, the frequency of ‘class’ and ‘thank you’ was high in pos-itive evaluations, and the frequency of ‘class’, ‘assignment’, and ‘unsatisfactory’ was high in negative descriptive course evaluations. In addition, ‘class’ was expanded around the keywords ‘content’, ‘progress’, ‘time’, and ‘regrettable’. Third, the results of semantic network analysis between keywords in the lecture evaluation contents showed that in the positive type, ‘gratitude’, ‘class’, ‘hard work’, ‘explanation’, ‘student’, ‘kindness’, ‘help’, ‘understanding’, ‘assignment’, ‘time’ were the most important and connected keywords. In the negative course evaluation, keywords including ‘class’, ‘lecture’, ‘professor’, ‘assignment’, ‘disappointing’, ‘difficult’, ‘anxiety of test’ were analyzed as the most connected. Conclusions This study showed the possibility of suggesting directions for education improvement through de-scriptive lecture evaluation using sentiment analysis and semantic network analysis.

Gear Train Optimization of High Speed Machinery by using CATIA and ANSYS

This project aims to optimize gear trains for high-speed machinery using CATIA and ANSYS. Gear trains are crucial in various mechanical systems, especially in high-speed applications where efficiency and reliability are essential. The objective is to combine CATIA for designing and modelling gear trains with ANSYS for analysing their performance under high rotational speeds. Helical gear train use for Reverses Motion design as per requiment of power and motor speed. Same design check for maximium load by load on pinion taken as step of 13K, 15K, 20K, 25K and 30K Newtons are applied on helical gear and check the stress and other parameters material High speed steel. Dynamic analysis model also be carried for mode frequency Keywords: Gear trains, Finite element analysis, CATIA, ANSYS.

Research on China's End-of-Life Care Policy from the Perspective of Aging Population: A Textual Analysis of 30 Policies from 2017 to 2022

As the proportion of elderly population increases and the level of social development continues to rise, the issue of end-of-life care for the elderly has gradually gained societal attention. End-of-life care, as an emerging field, relies crucially on optimizing top-level policy design for its healthy development. This study focuses on 30 national-level end-of-life care policy texts from 2017 to 2022. Utilizing analysis tools such as ROST CM 6 software, NLPIR Chinese word segmentation system, and SPSS, the research employs a three-dimensional framework of keyword frequency analysis, semantic network analysis, and "policy tools-policy time-policy intensity" to comprehensively explore the current state of China's end-of-life care policy. The study reveals existing issues in the policy's practical operability, uneven application of policy tools, neglected funding and information technology support, and weak regulatory aspects. It emphasizes the need to prioritize a people-centered approach, enhance the overall balance in policy tools, promote the standardization of end-of-life care, and improve a diversified service system. These findings broaden the research perspective in the field of end-of-life care, providing insights for optimizing policy design and advancing nationwide end-of-life care efforts.

Analyzing the evolution of machine learning integration in educational research: a bibliometric perspective

Machine learning, a subset of artificial intelligence, has experienced rapid advancements and applications across various domains. In education, its integration holds great potential to revolutionize teaching, learning, and educational outcomes. Despite the growing interest, there needs to be more comprehensive bibliometric analyses that track the trajectory of machine learning’s integration into educational research. This study addresses this gap by providing a nuanced perspective derived from bibliometric insights. Using a dataset from 1986 to 2022, consisting of 449 documents from 145 sources retrieved from the Web of Science (WoS), the research employs network analysis to unveil collaborative clusters and identify influential authors. A temporal analysis of annual research output sheds light on evolving trends, while a thematic content analysis explores prevalent research themes through keyword frequency. The findings reveal that co-authorship network analysis exposes distinct clusters and influential figures shaping the landscape of machine learning in educational research. Scientific production over time reveals a significant surge in research output, indicating the field’s maturation. The co-occurrence analysis emphasizes a collective focus on student-centric outcomes and technology integration, with terms like “online” and “analytics” prevailing. This study provides a nuanced understanding of the collaborative and thematic fabric characterizing machine learning in educational research. The implications derived from the findings guide strategic collaborations, emphasizing the importance of cross-disciplinary engagement. Recommendations include investing in technological infrastructure and prioritizing student-centric research. The study contributes foundational insights to inform future endeavors in this ever-evolving field.

Analyzing the Worldwide Wetland Parks Research: A Spectral-Cluster Algorithm Latent Semantic Index Approach

This study reviews worldwide wetland park research from 1996 to 2022. A bibliometric analysis is conducted on 591 wetland park studies indexed in the Web of Science and Scopus databases. The study utilizes CiteSpace and VOSviewer tools to visualize and explore influential research focuses, themes, directions, and countries. The citation burst indicates that from 1996 to 2022, research on wetland parks transited from exploring basic community structures to complex ecosystem service assessments and the formulation of management strategies. Furthermore, over the past three years, wetland park research has seen a significant surge in studies investigating water quality, ecosystem services, and spatiotemporal analysis. Notably, the three most frequent keywords in research on wetland parks were China, South Africa, and biodiversity. These keywords reflect regions that enhance biodiversity via wetland parks. The spectral-clustering algorithm identifies carbon sequestration as a research focus, highlighting the vital role of wetlands in the carbon cycle. Most authors work in developed countries’ institutions, but some are from developing countries like China, South Africa, and India. The findings suggest that economic development is crucial in wetland park construction and significantly influences related research. Developed countries may offer more PhD positions to developing countries’ researchers in the field and raise their awareness about wetland conservation. Given the holistic requirements of wetlands, this research recommends that educators should adopt an interdisciplinary approach in the future when nurturing wetland staff. Additionally, the study maps out the primary areas of interest in wetland park research, including environmental science, ecological economics, forestry, wetlands, tourism, and management. New artificial intelligence and digital technologies should be developed for wetland park research. This study fills a research gap: quantitative and visualized knowledge-mapping and bibliometrics on wetland parks are scarce. Additionally, no previous study has explored the relationship between wetland park research and the economic development of countries.

Video Summarization for Marathi Language

The Video Summarization Platform using Python Flask is a comprehensive tool designed to summarize Marathi and English videos while providing summaries in Hindi, Marathi, and English languages. Leveraging machine learning and natural language processing (NLP) techniques, this platform offers a sophisticated solution for efficiently extracting key information from videos. The platform begins by transcribing the audio content of the video into text using automatic speech recognition (ASR) technology. This transcription process ensures that the platform can accurately analyze and summarize the video's content. Next, the text is translated into the target languages, namely Hindi, Marathi, and English, enabling users from diverse linguistic backgrounds to access the summarized content. To generate concise and informative summaries, advanced NLP algorithm is applied. This algorithm analyze the transcribed text to identify the most significant phrases, sentences, and concepts. By considering factors such as keyword frequency, semantic relevance, and context, the platform effectively distils the video's content into digestible summaries. Additionally, machine learning models are employed to classify the type of video content. These models are trained on diverse datasets encompassing various video genres and topics. By recognizing patterns and features within the video content, the platform can accurately categorize videos into distinct types, such as news, interviews, tutorials, or entertainment. The platform's user interface, powered by Python Flask, offers a seamless experience for users to upload videos, select their preferred language for summarization, and receive concise summaries in their chosen languages. The intuitive design ensures accessibility and ease of use, catering to both novice and advanced users. Overall, the Video Summarization Platform serves as a valuable resource for individuals seeking efficient ways to consume multimedia content. Whether for educational, informational, or entertainment purposes, this platform empowers users to access summarized video content in multiple languages, facilitated by cutting-edge machine learning and NLP technologies. Key Words: Transcription, Marathi-speaking users, Marathi YouTube videos, video content, transcription, summary, translation, Natural Language Toolkit (NLTK), content comprehension, user interaction data, past summaries, recommendation

EPSMR: An efficient privacy-preserving semantic-aware multi-keyword ranked search scheme in cloud

Traditional privacy-preserving search schemes have commonly used the TF-IDF model, which relies on keyword frequency statistics but neglects the semantic relationships between and documents. In recent years, a few secure multi-keyword ranked search schemes over encrypted cloud data have considered the data semantics. However, the search efficiency of these schemes can still be improved. This paper presents an efficient privacy-preserving semantic-aware multi-keyword ranked search scheme (EPSMR) over encrypted cloud data. EPSMR utilizes the LDA topic model to discover hidden semantic information in documents and convert documents into vector representations. vector encryption by using the secure inner product is employed to implement the privacy-preserving semantic relevance score computation between search keywords and documents. To achieve efficient multi-keyword searches, the bisecting k-means algorithm and search tree structure are introduced to design a novel ω-way balanced search tree index which benefits finding search results quickly. to further improve the search efficiency, a filtering-threshold lookup table is presented, which can be used to prune unqualified sub-trees in advance. The experimental results show the superior performance of the proposed scheme compared to existing schemes in terms of both semantic search precision and search time cost.

Anemia in Heart Failure: A Perspective from 20-Year Bibliometric Analysis.

Anemia in patients with heart failure (HF) is a growing concern. However, there has no bibliometric analysis in this area up to now. The aim of this study is to explore the status and trends in the field of anemia in HF through the bibliometric analysis, and to provide an outlook on future research. We retrieved publications from the Web of Science Core Collection database, and the following data analysis and visualization tools were utilized to perform data processing, statistical computing and graphics generation: VOSviewer (v.1.6.18), CiteSpace (v.6.2 R5), Scimago Graphica (v.1.0.36), Biblimatrix and Microsoft Excel. We identified a total of 3490 publications from 2004 to 2023. The publications in the field of anemia in HF are growing steadily. The United States, the United Kingdom, and Italy were the leading countries in this area. Stefan D Anker, as the most influential author, held the most total citations and publications. Harvard University was the most productive institution in this area. The European Journal of Heart Failure had published the most papers. Through the analysis of co-citations, 14 major clusters based on cluster labels were identified. Keyword analysis showed that mortality, outcome, prevalence, and risk were the most frequent keywords, and the potential research hotspots in the future will be intravenous iron and iron deficiency. This study provides a comprehensive analysis of countries, authors, institutions, journals, co-cited references, and keywords in the field of anemia in HF through bibliometric analysis and data visualization. The status, hotspots and future trends in this field offer a reference for in-depth research. Further studies are necessary in the future to broaden the spectrum of research in this field, to evaluate comprehensive approaches to treating anemia in patients with HF, and to find rational targets for the management of anemia.

Detecting Upcoming Patent Keywords by Predicting Keyword Trends Using Patent Keyword Network

In this ever-changing technological landscape, the ability to quickly predict technological trends becomes crucial for any company or institute engaged in informed decision-making and strategic planning. Data for predicting technological trends can come from various sources such as patent data, which is easily accessible to the public due to the nature of patents. This research is aimed at patent analysis, focusing on combining the keyword- based method, social network analysis (SNA) method, and neural network prediction to propose a feasible keyword trend prediction method based on patent analysis by targeting upcoming keyword trends. More specifically, we utilize Long Short-Term Memory (LSTM) to predict changes in keyword frequency using keyword centralities as input. To assess the effectiveness of the proposed method, we constructed the input dataset using the USPTO patent database in the Information and Communication Technology (ICT) field. We then experimented to compare the proposed method with the benchmark method. Furthermore, to counteract the unbalanced nature of patent data, the SMOGN method is introduced. The results demonstrate its potential for application in broader contexts.

A bibliometric analysis of the Quality Management Journal: 10 years of publications and the pathway forward

The purpose of this paper is to examine the trends in research published by the Quality Management Journal (QMJ) based on Scopus Indexed publications for a period ranging from 2013 to 2023. The paper intends to offer a bibliometric analysis of the journal by highlighting the contributions made by the journal through document citations, prominent authors, most frequent keywords, and publication trends for the last decade. The analysis provides insight into how the QMJ has contributed to the development of the field of quality management. The journal has remained unwavering in its commitment to uphold both quality and comprehensiveness, thereby leading to widespread acclaim and robust indexing As analyzed from the author keyword analysis the contributions made to the journal can be categorized into seven themes, i.e., Leadership Essentials for Quality Management, Six Sigma & Quality, Quality Management in Services, Stakeholder Involvement for Quality Management, TQM & Continuous Improvement in Manufacturing, and Service Quality and Quality Management in Healthcare. A key theme to emerge from this study is that future research and applications must be robustly framed in relevant theory, to recognize, understand, and articulate implications for practice. Experiential quality will move further along the spectrum of socio-technical or socio-materiality.