Subhan Ali

471 citations
19 papers · 244 · 1 hit paper · h-index 4

Impact in

Papers in

Subhan Ali

9 papers receiving 236 citations

Subhan Ali's Hit Papers

Advancing Fake News Detection: Hybrid Deep Learning With FastText and Explainable AI 2024 · 59 citations
590+1Years since publication1020304050

Peers

Subhan Ali
Comparison fields: 5 of 93
  • Health Informatics 36
  • Artificial Intelligence 83
  • Health Information Management 11
  • Family Practice 5
  • Signal Processing 14
Replace Samer Albahra with:
Samer Albahra United States
Saeed Amal United States
Shirly Wang Canada
Farah E. Shamout United Kingdom
Ritwik Sinha United States
Mohammad Adibuzzaman United States
Loc Trinh United States
Shudi Li China
Hamed Hassanzadeh Australia
Eleonora Losiouk Italy
Subhan Ali relative to Samer Albahra United States Samer Albahra's profile →
Citations per field
00.5×3.5×
Samer Albahra · 1×
Citations per year

Countries citing papers authored by Subhan Ali

Since Specialization
Citations

This map shows the geographic impact of Subhan Ali'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 Subhan Ali with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Subhan Ali more than expected).

Fields of papers citing papers by Subhan Ali

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Subhan Ali. 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 Subhan Ali. The network helps show where Subhan Ali may publish in the future.

Co-authors

The 25 scholars most cited alongside Subhan Ali, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Subhan Ali Line = papers co-authored together Subhan Ali links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 2023146
2
Advancing Fake News Detection: Hybrid Deep Learning With FastText and Explainable AI
Hit paper breakdown →
202459
3 202327
4 20234
5 20233
6 20251
7 20231
8 20241
9 20161
10 20231
11 20250
12 20250
13 20250
14 20250
15 20250
16 20230
17 20250
18 20240
19 20240

About Subhan Ali

Subhan Ali is a scholar working on Mechanical Engineering, Biomedical Engineering, Artificial Intelligence, Health Informatics and Computational Mechanics, having authored 19 papers that have together received 244 indexed citations. Recurring topics across this work include Nanofluid Flow and Heat Transfer (4 papers), Artificial Intelligence in Healthcare and Education (3 papers), Heat Transfer Mechanisms (3 papers), Explainable Artificial Intelligence (XAI) (3 papers), Machine Learning in Healthcare (3 papers), Heat Transfer and Optimization (2 papers), Topic Modeling (2 papers) and Photovoltaic System Optimization Techniques (1 paper). The work is most often cited by research in Health Informatics (36 citations), Artificial Intelligence (83 citations), Health Information Management (11 citations), Family Practice (5 citations) and Signal Processing (14 citations). Subhan Ali has collaborated with scholars based in Pakistan, Norway and United Kingdom. Frequent co-authors include Sher Muhammad Daudpota, Ali Shariq Imran, Zenun Kastrati, Sule Yildirim Yayilgan, Muhammad Mudassar Yamin, Ehtesham Hashmi, Mohamed Abomhara, Sarah Farrukh, Xianfeng Fan and Tayyaba Nооr. Their work appears in journals such as Computers in Biology and Medicine, Scientific Reports, Materials Research Bulletin, Journal of Energy Storage and Environmental Science and Pollution Research.

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.

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