Bibliometric Survey on Incremental Learning in Text Classification Algorithms for False Information Detection

The false information or misinformation over the web has severe effects on people, business and society as a whole. Therefore, detection of misinformation has become a topic of research among many researchers. Detecting misinformation of textual articles is directly connected to text classification problem. With the massive and dynamic generation of unstructured textual documents over the web, incremental learning in text classification has gained more popularity. This survey explores recent advancements in incremental learning in text classification and review the research publications of the area from Scopus, Web of Science, Google Scholar, and IEEE databases and perform quantitative analysis by using methods such as publication statistics, collaboration degree, research network analysis, and citation analysis. The contribution of this study in incremental learning in text classification provides researchers insights on the latest status of the research through literature survey, and helps the researchers to know the various applications and the techniques used recently in the field

Bibliometric Analysis of Nutrition and Dietetics Research Activities in India using Web of Science database: A Trend across Forty Years

The present bibliometric analysis studies the nutrition research activity in India. Research articles published during the years 1980-2020, under the “nutrition and dietetics” domain were retrieved from ISI Web of Science database. Entirely, 2,704 articles by 12,514 authors were identified. Karnataka published maximum papers, while USA emerged as the prominent collaborating country. An increasing trend for the number of publications and International collaborations was observed over the years. Currently, as an outcome of these nutrition research activities and with the advent of technology, IT systems along with popular mobile apps are used. Continuous innovative research will boost up IT systems developments in future and few trends are discussed here

Bibliometric Survey on Incremental Learning in Text Classification Algorithms for False Information Detection

The false information or misinformation over the web has severe effects on people, business and society as a whole. Therefore, detection of misinformation has become a topic of research among many researchers. Detecting misinformation of textual articles is directly connected to text classification problem. With the massive and dynamic generation of unstructured textual documents over the web, incremental learning in text classification has gained more popularity. This survey explores recent advancements in incremental learning in text classification and review the research publications of the area from Scopus, Web of Science, Google Scholar, and IEEE databases and perform quantitative analysis by using methods such as publication statistics, collaboration degree, research network analysis, and citation analysis. The contribution of this study in incremental learning in text classification provides researchers insights on the latest status of the research through literature survey, and helps the researchers to know the various applications and the techniques used recently in the field

The art of selecting PhD students: Combination of Bibliometric and AHP Approach

For the PhD guide or the advisor selecting the accurate PhD scholar is the most elephantine task. It actually requires an art for the perfect selection; as the length, breadth, depth and volume of PhD work is spread across the years and this relationship between the scholar and the guide should start and flourish positively for the immense experience throughout the PhD process. Hence it was essential to understand bibliometric details including how many researchers have already published their contributions in the form of papers and patents, in the Scopus database. In addition to the bibliometric details, in this study, we also have incorporated the Analytical Hierarchical Process (AHP) model, based on primary and secondary characteristics related to aspiring PhD candidate(s). These characteristics are taken from the literature as well as based on the experience of various PhD guides, over the years of mentoring students. Hence this paper not only discusses the bibliometric details related to the topic but also provide guidelines for appropriate selection of the PhD student

The art of selecting PhD students: Combination of Bibliometric and AHP Approach

For the PhD guide or the advisor selecting the accurate PhD scholar is the most elephantine task. It actually requires an art for the perfect selection; as the length, breadth, depth and volume of PhD work is spread across the years and this relationship between the scholar and the guide should start and flourish positively for the immense experience throughout the PhD process. Hence it was essential to understand bibliometric details including how many researchers have already published their contributions in the form of papers and patents, in the Scopus database. In addition to the bibliometric details, in this study, we also have incorporated the Analytical Hierarchical Process (AHP) model, based on primary and secondary characteristics related to aspiring PhD candidate(s). These characteristics are taken from the literature as well as based on the experience of various PhD guides, over the years of mentoring students. Hence this paper not only discusses the bibliometric details related to the topic but also provide guidelines for appropriate selection of the PhD student

Social Media & HR: A Bibliometric Analysis

From an HRM standpoint, the better an organisation is able to manage their workforce, the better their performance will be. In doing so, social media nowadays play a major role to this end. Be it talent acquisition, employee engagement, talent management or enhancing employer value proposition, social media profiles are playing a big role for organisations. The onus of which lies to a large extent on the shoulders of the HR department. Merely having pages on social media platforms do not suffice. Proper management and audience engagement become the key for HR. It is not sufficient to just have a page on main social media platforms. A blend of Social Media, HR practices and a perfect strategy to implement these functionalities can help organizations reach their goals. Lot of research is being done in this direction and this paper sought to evaluate the same. Through systematic searches from Scopus, Web of Science and Google Scholar database, the data was analyzed and represented through prisma charts, tree diagrams and graphical representations pertaining to the research that has been done in the area of social media and HR