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This study aims to analyze India's publications on organic chemistry and related fields at the micro, meso, and macro levels. The study attempts to map and visualize the publications in organic chemistry from 2016 to 2020 to identify the country co-authorship, author co-authorship, bibliographic coupling of authors, keyword co-occurrence, etc. Performance analysis techniques incorporating publication-related metrics, citation-related metrics, and citation and publication-related metrics (number of cited publications, citations per publication, h-index) were utilized to analyze the publications. Further, science mapping techniques are used to investigate citation analysis, bibliographic coupling, and co-authorship analysis. Moreover, enriched bibliometric techniques meant for computational evaluation like network analysis, including cluster analysis and visualization, were used in later phases using the software VOSviewer and Biblioshiny. VOSviewer, a bibliometric mapping tool, is extensively used in scientometric studies. For analysis of performance and visualization of research hotspots in Organic Chemistry, Biblioshiny web interface through R-studio is used, which is performing intensive data mining. A total of 1804 journals are encountered in the period taken for study which obeys a non-exponential growth with the highest number of papers (370) in the year 2017 and the least (344) in the year 2020 and as per the forecasting, the highest number of publications are expected in the year 2021 (349). Analysis of prolific authors reveals that A Kumar is the most productive author in the period with 47 publications and an h-index of 12 and KR Prabhu of IISc. Bangalore is the most impactful author with 387 citations and 27.64 CPP. Journal of Organic Chemistry is the most productive journal. Trending topics of research like azo dyes and metal-organic frameworks indicate the industrial and commercial importance of organic compounds. No similar studies have been found with matching objectives with this study. [ABSTRACT FROM AUTHOR]

Introduction: Workplace accidents in the petroleum industry can cause catastrophic damage to people, property, and the environment. Earlier studies in this domain indicate that the majority of the accident report information is available in unstructured text format. Conventional techniques for the analysis of accident data are time-consuming and heavily dependent on experts' subject knowledge, experience, and judgment. There is a need to develop a machine learning-based decision support system to analyze the vast amounts of unstructured text data that are frequently overlooked due to a lack of appropriate methodology., Method: To address this gap in the literature, we propose a hybrid methodology that uses improved text-mining techniques combined with an un-bias group decision-making framework to combine the output of objective weights (based on text mining) and subjective weights (based on expert opinion) of risk factors to prioritize them. Based on the contextual word embedding models and term frequencies, we extracted five important clusters of risk factors comprising more than 32 risk sub-factors. A heterogeneous group of experts and employees in the petroleum industry were contacted to obtain their opinions on the extracted risk factors, and the best-worst method was used to convert their opinions to weights., Conclusions and Practical Applications: The applicability of our proposed framework was tested on the data compiled from the accident data released by the petroleum industries in India. Our framework can be extended to accident data from any industry, to reduce analysis time and improve the accuracy in classifying and prioritizing risk factors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 National Safety Council and Elsevier Ltd. All rights reserved.)

Introduction: Data mining in healthcare is a nascent arena of research in healthcare. Heterogeneity of Diabetes Mellitus in terms of clinical presentation calls for newer methods of research to study potential risk factors. Aim: The paper aims to use clustering techniques to identify the relationship between the four variables, namely the pre-prandial and postprandial sugar level, age and sex. Methods: The data was taken from a diagnostic laboratory in Wagholi, Pune. We conducted K-mean algorithm, EM algorithm, model-based clustering and t-mixture model. Results: It is evidenced that the data was best fitted to the t-mixture model. Our 50% samples were people with diabetes, 17% had prediabetes. Trivial correlation existed between age and sugar level. Males and females were equally at risk of having diabetes. Data presented concludes that age and sex have no effect on the risk of having diabetes. Data mining can be used to deduce meaningful clusters to drive plan-based interventions in the population. Conclusion: Methods of data mining can be used to deduce meaningful clusters in a heterogeneous dataset thus providing policymakers and healthcare researchers with novel information that will potentially contribute in formulating evidence-based policies. [ABSTRACT FROM AUTHOR]

Educational system is significantly changing in today's world. Recently, the New Education Policy (NEP)-2020 isstarted implementing in India. Students can have various options for getting education according to their choices and requirements. NEP-2020 is more student-centric rather than making them compulsory to get the degree with prescribed syllabus. AI has a major role in NEP-2020. Data mining technology plays a vital role in this new higher education system. As the Higher Education Institutions are growing rapidly, it is necessary for them to impart quality education for enrollment of students. Institutions can maintain educational quality by improving their results. This can be achieved by predicting student academic performance with the help of data mining algorithms. Classification, clustering, regression and association rule mining are the data mining techniques which can be implemented on student dataset to predict the final grade. This study focuses on prediction of student performance using classification and regression data mining techniques. The aim of this literature review is to study various data mining tools, algorithms and the important attributes that affect the student academic performance. [ABSTRACT FROM AUTHOR]