Muhammad Shayan
Impact in
- Artificial Intelligence top 5%
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Stochastic Gradient Optimization Techniques
- Adversarial Robustness in Machine Learning
- Information Systems top 5%
- Blockchain Technology Applications and Security
Papers in
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- Gas Sensing Nanomaterials and Sensors 1
-
- Advanced Photocatalysis Techniques 2
- Co-authors
- Clement Fung (1 shared paper)Chris J. M. Yoon (1 shared paper)Ivan Beschastnikh (1 shared paper)Imran Khan (3 shared papers)Salman Ali Khan (3 shared papers)Abdullah N. Alodhayb (2 shared papers)Muhammad Rizwan (2 shared papers)Runcheng Liu (1 shared paper)
- Journals
- IEEE Transactions on Parallel and Distributed Systems (1 paper)Solar RRL (1 paper)Joule (1 paper)Preventive Medicine (1 paper)ACS Applied Nano Materials (1 paper)
- Partner nations
- ChinaPakistanSaudi Arabia
In The Last Decade
Muhammad Shayan
9 papers receiving 279 citations
Peers
Comparison fields: 5 of 49
- Artificial Intelligence 207
- Information Systems 121
- Health Informatics 6
- Computer Science Applications 23
- Renewable Energy, Sustainability and the Environment 34
Countries citing papers authored by Muhammad Shayan
This map shows the geographic impact of Muhammad Shayan'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 Muhammad Shayan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad Shayan more than expected).
Fields of papers citing papers by Muhammad Shayan
This network shows the impact of papers produced by Muhammad Shayan. 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 Muhammad Shayan. The network helps show where Muhammad Shayan may publish in the future.
Co-authors
The 25 scholars most cited alongside Muhammad Shayan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 231 | |
| 2 | 2024 | 14 | |
| 3 | 2022 | 11 | |
| 4 | 2024 | 10 | |
| 5 | 2024 | 9 | |
| 6 | 2024 | 8 | |
| 7 | 2023 | 2 | |
| 8 | 2016 | 2 | |
| 9 | 2023 | 1 |
About Muhammad Shayan
Muhammad Shayan is a scholar working on Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment, Artificial Intelligence, Electronic, Optical and Magnetic Materials and Surgery, having authored 9 papers that have together received 288 indexed citations. Recurring topics across this work include Advanced Photocatalysis Techniques (2 papers), Advanced Text Analysis Techniques (1 paper), Gas Sensing Nanomaterials and Sensors (1 paper), Ga2O3 and related materials (1 paper), Gun Ownership and Violence Research (1 paper), Energy and Environment Impacts (1 paper), Injury Epidemiology and Prevention (1 paper) and Covalent Organic Framework Applications (1 paper). The work is most often cited by research in Artificial Intelligence (207 citations), Information Systems (121 citations), Health Informatics (6 citations), Computer Science Applications (23 citations) and Renewable Energy, Sustainability and the Environment (34 citations). Muhammad Shayan has collaborated with scholars based in China, Pakistan and Saudi Arabia. Frequent co-authors include Clement Fung, Chris J. M. Yoon, Ivan Beschastnikh, Imran Khan, Salman Ali Khan, Abdullah N. Alodhayb, Muhammad Rizwan, Runcheng Liu, Cong Liu and Shouzhen Jiang. Their work appears in journals such as IEEE Transactions on Parallel and Distributed Systems, Solar RRL, Joule, Preventive Medicine and ACS Applied Nano Materials.
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.