This map shows the geographic impact of Jojo Moolayil's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jojo Moolayil with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jojo Moolayil more than expected).
This network shows the impact of papers produced by Jojo Moolayil. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jojo Moolayil. The network helps show where Jojo Moolayil may publish in the future.
Co-authors
The 1 scholars most cited alongside Jojo Moolayil, linked wherever they
have co-authored with each other. Click a name or a connecting line to browse the papers
they share.
Border = papers with Jojo MoolayilLine = papers co-authored togetherJojo Moolayil links everyone, so they are left out of the graph.
Jojo Moolayil is a scholar working on Artificial Intelligence, Insect Science, Renewable Energy, Sustainability and the Environment, Obstetrics and Gynecology and Anesthesiology and Pain Medicine, having authored 17 papers that have together received 583 indexed citations. Recurring topics across this work include Computational Physics and Python Applications (1 paper). The work is most often cited by research in Artificial Intelligence (175 citations), Computer Vision and Pattern Recognition (90 citations), Signal Processing (42 citations), Health Information Management (14 citations) and Human-Computer Interaction (16 citations). Frequent co-authors include Nikhil S. Ketkar. Their work appears in journals such as CERN Document Server (European Organization for Nuclear Research) and Apress eBooks.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.