Muhammad Sohail Ibrahim
- Electrical and Electronic Engineering
- Control and Systems Engineering top 10%
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Signal Processing
- Co-authors
- Wei DongQiang YangImran NaseemShujaat KhanMuhammad UsmanMansoor EbrahimJeong–A LeeAbdulrahman A. Alshdadi
- Topics
- Advanced Neural Network Applications (5 papers)Machine Learning in Bioinformatics (4 papers)Biometric Identification and Security (4 papers)
- Cited by
- Control and Systems EngineeringElectrical and Electronic EngineeringSafety, Risk, Reliability and Quality
- Partner nations
- South KoreaPakistanChina
In The Last Decade
Muhammad Sohail Ibrahim
17 papers receiving 340 citations
Hit Papers
Peers
Comparison fields: 5 of 61
- Electrical and Electronic Engineering 227
- Control and Systems Engineering 96
- Artificial Intelligence 68
- Computer Vision and Pattern Recognition 56
- Signal Processing 31
Countries citing papers authored by Muhammad Sohail Ibrahim
This map shows the geographic impact of Muhammad Sohail Ibrahim'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 Sohail Ibrahim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad Sohail Ibrahim more than expected).
Fields of papers citing papers by Muhammad Sohail Ibrahim
This network shows the impact of papers produced by Muhammad Sohail Ibrahim. 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 Sohail Ibrahim. The network helps show where Muhammad Sohail Ibrahim may publish in the future.
Co-authorship network of co-authors of Muhammad Sohail Ibrahim
This figure shows the co-authorship network connecting the top 25 collaborators of Muhammad Sohail Ibrahim. A scholar is included among the top collaborators of Muhammad Sohail Ibrahim 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 Muhammad Sohail Ibrahim. Muhammad Sohail Ibrahim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 3 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 0 | |
| 8 | 2 | |
| 9 | 2 | |
| 10 | 8 | |
| 11 | 7 | |
| 12 | 2 | |
| 13 | 15 | |
| 14 | 8 | |
| 15 | Machine learning driven smart electric power systems: Current trends and new perspectivesbreakdown → | 276 |
| 16 | 5 | |
| 17 | 5 | |
| 18 | 2 | |
| 19 | 9 | |
| 20 | 4 |
About Muhammad Sohail Ibrahim
Muhammad Sohail Ibrahim is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Hardware and Architecture, having authored 20 papers that have together received 354 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (5 papers), Machine Learning in Bioinformatics (4 papers) and Biometric Identification and Security (4 papers). The work is most often cited by research in Control and Systems Engineering (96 citations), Electrical and Electronic Engineering (227 citations) and Safety, Risk, Reliability and Quality (30 citations). Muhammad Sohail Ibrahim has collaborated with scholars based in South Korea, Pakistan and China. Frequent co-authors include Wei Dong, Qiang Yang, Imran Naseem, Shujaat Khan, Muhammad Usman, Mansoor Ebrahim, Jeong–A Lee, Abdulrahman A. Alshdadi, Faris Kateb and Abdul Wahab. Their work appears in journals such as Applied Energy, IEEE Access and Knowledge-Based Systems.
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.