Kaniz Fatema

22 papers and 201 indexed citations i.

About

Kaniz Fatema is a scholar working on Plant Science, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Kaniz Fatema has authored 22 papers receiving a total of 201 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Plant Science, 4 papers in Artificial Intelligence and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Kaniz Fatema’s work include Digital Imaging for Blood Diseases (4 papers), Topic Modeling (3 papers) and Essential Oils and Antimicrobial Activity (3 papers). Kaniz Fatema is often cited by papers focused on Digital Imaging for Blood Diseases (4 papers), Topic Modeling (3 papers) and Essential Oils and Antimicrobial Activity (3 papers). Kaniz Fatema collaborates with scholars based in Bangladesh, Australia and United States. Kaniz Fatema's co-authors include Sami Azam, Mohaimenul Azam Khan Raiaan, Md. Saddam Hossain Mukta, Sadman Sakib, Mohammed Eunus Ali, Nur Mohammad Fahad, Mirjam Jonkman, Friso De Boer, Md. Zahid Hasan and Sidratul Montaha and has published in prestigious journals such as PLoS ONE, Sensors and IEEE Access.

In The Last Decade

Co-authorship network of co-authors of Kaniz Fatema i

Fields of papers citing papers by Kaniz Fatema

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kaniz Fatema. 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 Kaniz Fatema. The network helps show where Kaniz Fatema may publish in the future.

Countries citing papers authored by Kaniz Fatema

Since Specialization
Citations

This map shows the geographic impact of Kaniz Fatema'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 Kaniz Fatema with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaniz Fatema more than expected).

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.

Explore authors with similar magnitude of impact

Rankless by CCL
2025