Will Serrano
Impact in
- Media Technology top 5%
- Smart Cities and Technologies
- Information Systems top 5%
- Blockchain Technology Applications and Security
Papers in
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- IoT and Edge/Fog Computing 6
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- Blockchain Technology Applications and Security 8
- Recommender Systems and Techniques 5
- Co-authors
- Erol Gelenbe (6 shared papers)Philip Treleaven (2 shared papers)Yonghua Yin (1 shared paper)Sumarga Kumar Sah Tyagi (1 shared paper)Elias Pimenidis (1 shared paper)Sanjeev Jain (1 shared paper)
- Journals
- Neural Computing and Applications (4 papers)Neurocomputing (3 papers)Buildings (2 papers)Big Data and Cognitive Computing (1 paper)Journal of Network and Computer Applications (1 paper)
- Partner nations
- United KingdomIndiaChina
In The Last Decade
Will Serrano
41 papers receiving 474 citations
Peers
Comparison fields: 5 of 83
- Media Technology 67
- Information Systems 167
- Transportation 46
- Management Information Systems 46
- Building and Construction 63
Countries citing papers authored by Will Serrano
This map shows the geographic impact of Will Serrano'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 Will Serrano with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Will Serrano more than expected).
Fields of papers citing papers by Will Serrano
This network shows the impact of papers produced by Will Serrano. 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 Will Serrano. The network helps show where Will Serrano may publish in the future.
Co-authors
The 6 scholars most cited alongside Will Serrano, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 107 | |
| 2 | 2020 | 70 | |
| 3 | 2018 | 23 | |
| 4 | 2021 | 22 | |
| 5 | 2022 | 20 | |
| 6 | 2022 | 17 | |
| 7 | 2019 | 16 | |
| 8 | 2021 | 16 | |
| 9 | 2018 | 16 | |
| 10 | 2019 | 15 | |
| 11 | 2022 | 15 | |
| 12 | 2021 | 13 | |
| 13 | 2016 | 12 | |
| 14 | 2017 | 12 | |
| 15 | 2019 | 12 | |
| 16 | 2023 | 10 | |
| 17 | 2017 | 10 | |
| 18 | 2024 | 9 | |
| 19 | 2017 | 8 | |
| 20 | 2016 | 8 |
About Will Serrano
Will Serrano is a scholar working on Computer Networks and Communications, Information Systems, Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering, having authored 42 papers that have together received 504 indexed citations. Recurring topics across this work include Blockchain Technology Applications and Security (8 papers), Neural Networks and Applications (8 papers), Stock Market Forecasting Methods (6 papers), IoT and Edge/Fog Computing (6 papers), Energy Load and Power Forecasting (5 papers), Recommender Systems and Techniques (5 papers), Advanced Image and Video Retrieval Techniques (5 papers) and Advanced Memory and Neural Computing (4 papers). The work is most often cited by research in Media Technology (67 citations), Information Systems (167 citations), Transportation (46 citations), Management Information Systems (46 citations) and Building and Construction (63 citations). Will Serrano has collaborated with scholars based in United Kingdom, India and China. Frequent co-authors include Erol Gelenbe, Philip Treleaven, Yonghua Yin, Sumarga Kumar Sah Tyagi, Elias Pimenidis and Sanjeev Jain. Their work appears in journals such as Neural Computing and Applications, Neurocomputing, Buildings, Big Data and Cognitive Computing and Journal of Network and Computer Applications.
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.