Israr Hussain
- Computer Vision and Pattern Recognition top 5%
- Plant Science
- Artificial Intelligence
- Aerospace Engineering
- Analytical Chemistry top 10%
- Co-authors
- Hujun YinShunquan TanDostdar HussainAhmed HussainSyed Sajid UllahImran MirJishen ZengT.R. Reed
- Topics
- Smart Agriculture and AI (6 papers)Digital Media Forensic Detection (6 papers)Image and Signal Denoising Methods (5 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE Transactions on Image ProcessingExpert Systems with Applications
- Partner nations
- PakistanChinaUnited States
In The Last Decade
Israr Hussain
33 papers receiving 322 citations
Peers
Comparison fields: 5 of 65
- Computer Vision and Pattern Recognition 153
- Plant Science 88
- Artificial Intelligence 57
- Aerospace Engineering 46
- Analytical Chemistry 46
Countries citing papers authored by Israr Hussain
This map shows the geographic impact of Israr Hussain'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 Israr Hussain with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Israr Hussain more than expected).
Fields of papers citing papers by Israr Hussain
This network shows the impact of papers produced by Israr Hussain. 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 Israr Hussain. The network helps show where Israr Hussain may publish in the future.
Co-authorship network of co-authors of Israr Hussain
This figure shows the co-authorship network connecting the top 25 collaborators of Israr Hussain. A scholar is included among the top collaborators of Israr Hussain 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 Israr Hussain. Israr Hussain is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 4 | |
| 6 | 6 | |
| 7 | 1 | |
| 8 | 18 | |
| 9 | 5 | |
| 10 | 0 | |
| 11 | 23 | |
| 12 | 6 | |
| 13 | 27 | |
| 14 | CNN Transfer Learning for Automatic Fruit Recognition for Future Class of Fruit | 4 |
| 15 | 26 | |
| 16 | 33 | |
| 17 | 40 | |
| 18 | 0 | |
| 19 | 5 | |
| 20 | 0 |
About Israr Hussain
Israr Hussain is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Analytical Chemistry, having authored 40 papers that have together received 336 indexed citations. Recurring topics across this work include Smart Agriculture and AI (6 papers), Digital Media Forensic Detection (6 papers) and Image and Signal Denoising Methods (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (153 citations), Analytical Chemistry (46 citations) and Media Technology (29 citations). Israr Hussain has collaborated with scholars based in Pakistan, China and United States. Frequent co-authors include Hujun Yin, Shunquan Tan, Dostdar Hussain, Ahmed Hussain, Syed Sajid Ullah, Imran Mir, Jishen Zeng, T.R. Reed, Saddam Hussain and Roobaea Alroobaea. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Image Processing and Expert Systems with 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.