Insu Song

103 total papers · 863 total citations
37 papers, 396 citations indexed

About

Insu Song is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing. According to data from OpenAlex, Insu Song has authored 37 papers receiving a total of 396 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 6 papers in Signal Processing. Recurrent topics in Insu Song's work include Phonocardiography and Auscultation Techniques (5 papers), Logic, Reasoning, and Knowledge (4 papers) and Innovative Teaching and Learning Methods (4 papers). Insu Song is often cited by papers focused on Phonocardiography and Auscultation Techniques (5 papers), Logic, Reasoning, and Knowledge (4 papers) and Innovative Teaching and Learning Methods (4 papers). Insu Song collaborates with scholars based in Singapore, Australia and China. Insu Song's co-authors include Joachim Diederich, Baiying Lei, Roger Ho, Carol C. Choo, Abhishek Bhati, Nigel V. Marsh, Peter Yellowlees, M.K.H. Leung, Ickjai Lee and Tam T. Nguyen and has published in prestigious journals such as Expert Systems with Applications, Neurocomputing and Composite Structures.

In The Last Decade

Insu Song

33 papers receiving 377 citations

Author Peers

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

Author						Papers	Cites
Insu Song	79	61	49	48	46	37	396
Khanh Nguyen-Trong	53 0.7×	51 0.8×	11 0.2×	18 0.4×	13 0.3×	34	440
Femke De Backere	112 1.4×	62 1.0×	6 0.1×	28 0.6×	16 0.3×	51	421
K. Rajeswari	30 0.4×	154 2.5×	62 1.3×	19 0.4×	19 0.4×	66	439
Luı́s Alfredo Vidal de Carvalho	39 0.5×	70 1.1×	19 0.4×	19 0.4×	10 0.2×	61	374
Lianting Hu	24 0.3×	71 1.2×	20 0.4×	32 0.7×	13 0.3×	35	335
Nicola L. Ritter	204 2.6×	36 0.6×	9 0.2×	12 0.3×	53 1.2×	24	439
Luciano Nocera	46 0.6×	45 0.7×	12 0.2×	7 0.1×	24 0.5×	31	346
Waqar Ali	150 1.9×	172 2.8×	6 0.1×	24 0.5×	61 1.3×	32	432
Uzair Shah	45 0.6×	62 1.0×	17 0.3×	13 0.3×	11 0.2×	28	381
Farhana Sarker	22 0.3×	38 0.6×	7 0.1×	11 0.2×	19 0.4×	41	408

Countries citing papers authored by Insu Song

Since Specialization

Citations

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

Fields of papers citing papers by Insu Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Insu Song

This figure shows the co-authorship network connecting the top 25 collaborators of Insu Song. A scholar is included among the top collaborators of Insu Song 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 Insu Song. Insu Song 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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