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.
Federated Learning Meets Blockchain in Decentralized Data Sharing: Healthcare Use Case
202447 citationsSaeed Hamood Alsamhi, Raushan Myrzashova et al.IEEE Internet of Things Journalprofile →
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 Edward Curry'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 Edward Curry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Edward Curry more than expected).
This network shows the impact of papers produced by Edward Curry. 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 Edward Curry. The network helps show where Edward Curry may publish in the future.
Co-authorship network of co-authors of Edward Curry
This figure shows the co-authorship network connecting the top 25 collaborators of Edward Curry.
A scholar is included among the top collaborators of Edward Curry 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 Edward Curry. Edward Curry is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Alsamhi, Saeed Hamood, Raushan Myrzashova, Ammar Hawbani, et al.. (2024). Federated Learning Meets Blockchain in Decentralized Data Sharing: Healthcare Use Case. IEEE Internet of Things Journal. 11(11). 19602–19615.47 indexed citations breakdown →
Khan, Muhammad Jaleed & Edward Curry. (2020). Neuro-symbolic Visual Reasoning for Multimedia Event Processing: Overview, Prospects and Challenges..6 indexed citations
13.
Porwol, Lukasz, et al.. (2019). Towards a Temporal Deep Learning Model to Support Sustainable Agricultural Practices.. 152–163.
14.
Curry, Edward, et al.. (2019). A Comparison of Deep Learning Models in Human Activity Recognition and Behavioural Prediction on the MHEALTH Dataset.. 212–223.10 indexed citations
15.
Freitas, André, Siegfried Handschuh, & Edward Curry. (2015). Distributional-Relational Models: Scalable Semantics for Databases.. National Conference on Artificial Intelligence.
16.
Freitas, André, et al.. (2013). Distributional Relational Networks. National Conference on Artificial Intelligence.2 indexed citations
17.
Hassan, Umair ul, Seán Ó Riain, & Edward Curry. (2012). Towards Expertise Modelling for Routing Data Cleaning Tasks within a Community of Knowledge Workers.. 58–69.7 indexed citations
18.
Curry, Edward, et al.. (2012). Developing a sustainable IT capability: lessons from Intel's journey. MIS Quarterly Executive. 11(2). 3.19 indexed citations
19.
Lindner, Maik, Fiona McDonald, Gerard Conway, & Edward Curry. (2011). Understanding Cloud Requirements - A Supply Chain Lifecycle Approach. MURAL - Maynooth University Research Archive Library (National University of Ireland, Maynooth).6 indexed citations
20.
Curry, Edward, et al.. (2004). Enterprise Service Facilitation within Agent Environments. 601–606.1 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.