Muhammad Aamir
- Neurology top 2%
- Brain Tumor Detection and Classification 12
-
- Advanced Neural Network Applications 13
-
- COVID-19 diagnosis using AI 3
- Artificial Intelligence top 5%
- AI in cancer detection 5
- Machine Learning and ELM 4
- Analytical Chemistry top 10%
-
- Network Security and Intrusion Detection 4
-
- Smart Agriculture and AI 3
-
- Forensic Toxicology and Drug Analysis 3
- Co-authors
- Ahmad ShafMuhammad IrfanTariq AliWaheed Ahmed AbroZhihua HuYurong GuanZia-ur RahmanZaheer Ahmed Dayo
- Journals
- Computers, materials & continua/Computers, materials & continua (Print) (5 papers)Sensors (4 papers)PeerJ Computer Science (2 papers)
- Partner nations
- PakistanSaudi ArabiaChina
In The Last Decade
Muhammad Aamir
56 papers receiving 728 citations
Hit Papers
Peers
Comparison fields: 5 of 114
- Neurology 362
- Computer Vision and Pattern Recognition 342
- Radiology, Nuclear Medicine and Imaging 157
- Artificial Intelligence 203
- Analytical Chemistry 39
Countries citing papers authored by Muhammad Aamir
This map shows the geographic impact of Muhammad Aamir'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 Aamir with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad Aamir more than expected).
Fields of papers citing papers by Muhammad Aamir
This network shows the impact of papers produced by Muhammad Aamir. 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 Aamir. The network helps show where Muhammad Aamir may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Muhammad Aamir, 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 | 2024 | 1 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 5 | |
| 4 | 2023 | 50 | |
| 5 | 2023 | 0 | |
| 6 | 2023 | 0 | |
| 7 | 2023 | 0 | |
| 8 | 2022 | 4 | |
| 9 | 2022 | 2 | |
| 10 | 2022 | 1 | |
| 11 | 2021 | 2 | |
| 12 | 2021 | 3 | |
| 13 | 2021 | 9 | |
| 14 | 2021 | 32 | |
| 15 | 2020 | 0 | |
| 16 | 2020 | 5 | |
| 17 | TO DETERMINE FREQUENCY OF TUBAL BLOCKAGE IN INFERTILITY PATIENTS UNDERGOING X-RAYS HYSTEROSALPINGOGRAPHY | 2018 | 1 |
| 18 | CORRELATION OF PLACENTAL THICKNESS WITH GESTATIONAL AGE IN NORMAL PREGNANT WOMEN WITH SINGLETON PREGNANCY VISITING TO A TERTIARY CARE HOSPITAL | 2018 | 0 |
| 19 | Metabolic syndrome in acute coronary syndrome | 2011 | 1 |
| 20 | CAUSES AND MANAGEMENT OF HIGH FETAL HEAD IN PRIMIGRAVIDAE AT TERM | 2008 | 1 |
About Muhammad Aamir
Muhammad Aamir is a scholar working on Neurology, Toxicology and Computer Vision and Pattern Recognition, having authored 69 papers that have together received 787 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (13 papers), Brain Tumor Detection and Classification (12 papers), AI in cancer detection (5 papers), Network Security and Intrusion Detection (4 papers), Machine Learning and ELM (4 papers), Smart Agriculture and AI (3 papers), COVID-19 diagnosis using AI (3 papers) and Forensic Toxicology and Drug Analysis (3 papers). The work is most often cited by research in Neurology (362 citations), Computer Vision and Pattern Recognition (342 citations) and Radiology, Nuclear Medicine and Imaging (157 citations). Muhammad Aamir has collaborated with scholars based in Pakistan, Saudi Arabia and China. Frequent co-authors include Ahmad Shaf, Muhammad Irfan, Tariq Ali, Waheed Ahmed Abro, Zhihua Hu, Yurong Guan, Zia-ur Rahman, Zaheer Ahmed Dayo, Abdullah A. Asiri and M. Irfan Uddin. Their work appears in journals such as Computers, materials & continua/Computers, materials & continua (Print), Sensors, PeerJ Computer Science, Scientific Reports and Analytical Biochemistry.
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.