Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Urban planning and building smart cities based on the Internet of Things using Big Data analytics
2016607 citationsAnand Paul, Seungmin Rho et al.profile →
Convolutional Neural Networks Based Fire Detection in Surveillance Videos
2018379 citationsKhan Muhammad, Jamil Ahmad et al.IEEE Accessprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Seungmin Rho'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 Seungmin Rho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seungmin Rho more than expected).
This network shows the impact of papers produced by Seungmin Rho. 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 Seungmin Rho. The network helps show where Seungmin Rho may publish in the future.
Co-authorship network of co-authors of Seungmin Rho
This figure shows the co-authorship network connecting the top 25 collaborators of Seungmin Rho.
A scholar is included among the top collaborators of Seungmin Rho 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 Seungmin Rho. Seungmin Rho is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ramasamy, Lakshmana Kumar, et al.. (2020). Handling Failures in Semantic Web Service Composition Through Replacement Policy in Healthcare Domain. 網際網路技術學刊. 21(3). 733–741.1 indexed citations
Muhammad, Khan, Jamil Ahmad, Irfan Mehmood, Seungmin Rho, & Sung Wook Baik. (2018). Convolutional Neural Networks Based Fire Detection in Surveillance Videos. IEEE Access. 6. 18174–18183.379 indexed citations breakdown →
13.
Jabbar, Sohail, et al.. (2016). Heuristic Approach for Stagnation Free Energy Aware Routing in Wireless Sensor Networks.. Ad Hoc & Sensor Wireless Networks. 31. 21–45.9 indexed citations
14.
Paul, Anand, Naveen Chilamkurti, Alfred Daniel, & Seungmin Rho. (2016). Intelligent Vehicular Networks and Communications: Fundamentals, Architectures and Solutions. CERN Document Server (European Organization for Nuclear Research).24 indexed citations
Chong, Woon Kian, et al.. (2015). Key factors affecting user experience of mobile recommendation systems. International MultiConference of Engineers and Computer Scientists. 724–728.7 indexed citations
Kim, Daehoon, et al.. (2013). Detecting trend and bursty keywords using characteristics of Twitter stream data. International Journal of Smart Home. 7(1). 209–220.9 indexed citations
Rho, Seungmin, Eenjun Hwang, & Minkoo Kim. (2005). An implementation of QoS adaptive multimedia content delivery. 9(6). 316–321.1 indexed citations
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.