Shadman Sakib

56 total papers · 644 total citations
27 papers, 176 citations indexed

About

Shadman Sakib is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Shadman Sakib has authored 27 papers receiving a total of 176 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Shadman Sakib's work include COVID-19 diagnosis using AI (5 papers), AI in cancer detection (4 papers) and Anomaly Detection Techniques and Applications (3 papers). Shadman Sakib is often cited by papers focused on COVID-19 diagnosis using AI (5 papers), AI in cancer detection (4 papers) and Anomaly Detection Techniques and Applications (3 papers). Shadman Sakib collaborates with scholars based in Bangladesh, United States and Japan. Shadman Sakib's co-authors include Khan Md. Hasib, Mohammad Shafiul Alam, Md Rafiqul Islam, Imran Razzak, Md. Abu Bakr Siddique, Tonmoy Hossain, Jubayer Al Mahmud, Md. Saifur Rahman, Faisal Muhammad Shah and Md Shahriar Mahbub and has published in prestigious journals such as IEEE Access, Advances in Space Research and Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery.

In The Last Decade

Shadman Sakib

24 papers receiving 170 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Shadman Sakib	83	37	27	23	15	27	176
Hussain A. Younis	84 1.0×	29 0.8×	30 1.1×	18 0.8×	9 0.6×	21	261
Abu Kaisar Mohammad Masum	213 2.6×	12 0.3×	14 0.5×	11 0.5×	8 0.5×	33	289
Liyakathunisa Syed	66 0.8×	14 0.4×	57 2.1×	9 0.4×	37 2.5×	23	243
Md Humaion Kabir Mehedi	83 1.0×	24 0.6×	38 1.4×	10 0.4×	4 0.3×	49	227
Jérémie Clos	100 1.2×	57 1.5×	30 1.1×	14 0.6×	13 0.9×	27	219
Kogilavani Shanmugavadivel	153 1.8×	41 1.1×	35 1.3×	16 0.7×	6 0.4×	19	264
Mirela T. Cazzolato	51 0.6×	26 0.7×	76 2.8×	31 1.3×	7 0.5×	40	236
Ivan Donadello	175 2.1×	7 0.2×	31 1.1×	8 0.3×	4 0.3×	22	252
Udayan Ghose	124 1.5×	7 0.2×	18 0.7×	7 0.3×	5 0.3×	32	231
J.C. Schryver	88 1.1×	8 0.2×	25 0.9×	13 0.6×	38 2.5×	27	243

Countries citing papers authored by Shadman Sakib

Since Specialization

Citations

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

Fields of papers citing papers by Shadman Sakib

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shadman Sakib

This figure shows the co-authorship network connecting the top 25 collaborators of Shadman Sakib. A scholar is included among the top collaborators of Shadman Sakib based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shadman Sakib. Shadman Sakib is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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.

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