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The effect of the COVID-19 pandemic has prompted a large number of studies targeted at understanding, monitoring, and containing the disease. However, it is still unclear whether the studies performed so far have filled existing knowledge gaps. We used computational intelligence (CI)/Machine Learning (ML) technologies and alliance areas to analyse this massive amount of information at scale. This chapter assesses the scholarly progress and prominent research domains in the use of CI/ML technologies in COVID-19 research, focusing on the specific literature on computational intelligence and related fields that have been employed for “diagnosis and treatment” of COVID-19 patients.The “Web of Science” database was used to retrieve all existing and highly cited papers published up to November 2020. Based on bibliometric indicators, a search query (“Computational Intelligence or Neural Networks or Fuzzy Systems or Evolutionary Computation & Diagnosis or Treatment & Coronavirus or Corona Virus or COVID-19”) was used to retrieve the data sets. The growth of research publications, elements of research activities, publication patterns, and research focus tendencies were computed using ‘Biblioshiny’ software and data visualization software ‘VOS viewer.’ Further, bibliometric/scientometrics techniques were incorporated to know the most productive countries, most preferred sources & their impact, three-field plot, and the most cited papers. This analysis provides a comprehensive overview of the “COVID-19” and CI-related research, helping researchers, policymakers, and practitioners better understand COVID-19 related CI research and its possible practical impact. Future CI / ML Studies should be committed to filling the gap between CI / ML research.

The Higher Education System Rankings measure national higher education systems and meet a long-standing need to shift the discussion from the ranking of the NIRF top institutions to the best overall systems in each country, to reflect the country’s overall performance in NIRF rankings we propose a new Excellence/Quality indicator based on the excellence level reached by their Top Pharma education institutions within the Top positions of the NIRF weighted by the country’s size population. In the present study, we analyzed the Top 10 Pharma education institutions of the NIRF Ranking 2020. The first rank positioned by Hamdard University NIRF score of (80.5). SCOPUS database was used to extract the data and the study was limited to five years (2016-2019) which resulted in 7172 documents. The data analysis was performed using Biblioshiny, Microsoft excel, and VOS Viewer software, further data were explored using the bibliometrics tools and techniques. The study attempt to measure the top 10 Pharma Education Institution’s and their publications, Year-Wise distribution of research Output, document type, Highly Prolific Authors, Most Preferred Sources, Funding Agencies, Most Cited Papers, Most Productive and Most Cited Countries, and Highly Prolific Keywords based on the collected data. The analysis of the study indicates the highest publications with 2129, published by Institute of Chemical Technology-Mumbai; the most the productive year 2017 with 1508 publications; most of the publications are published as articles (6067); highly prolific author Sekar N with 194 papers, total citation 1954, h-index 22; preferred source title RSC Advance, 217 paper, total citation 2508, h-index 24; top funding agency University Grant Commission (UGC) 609 papers; top cited paper Shao Y, 2015, Molecular Physics; most productive and most cited country the USA.