Huma Naz

18 papers receiving 369 citations

Huma Naz's Hit Papers

Deep learning approach for diabetes prediction using PIMA Indian dataset 2020 · 208 citations
2080+2+4Years since publication50100150200

Peers

Huma Naz
Comparison fields: 5 of 90
  • Health Information Management 236
  • Complementary and alternative medicine 46
  • Health Informatics 7
  • Artificial Intelligence 167
  • Radiology, Nuclear Medicine and Imaging 81
Replace Talha Imtiaz Baig with:
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Tarun Gangil India
Ashok Kumar Dwivedi India
Shahid Mohammad Ganie India
Dehui Yin China
Abid Sarwar India
Ritika Mehra India
B. B. Tiwari India
Shujie Yu China
Baha Ihnaini China
Huma Naz relative to Talha Imtiaz Baig China Talha Imtiaz Baig's profile →
Citations per field
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Talha Imtiaz Baig · 1×
Citations per year

Countries citing papers authored by Huma Naz

Since Specialization
Citations

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

Fields of papers citing papers by Huma Naz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Huma Naz, 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 Huma Naz Line = papers co-authored together Huma Naz links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Deep learning approach for diabetes prediction using PIMA Indian dataset
Hit paper breakdown →
2020208
2 201367
3 202034
4 202115
5 202212
6 202112
7 202311
8 20249
9 20247
10 20247
11 20206
12 20232
13 20222
14 20212
15 20241
16 20251
17 20241
18 20251
19 20161
20
Renal Clearance and Urinary Excretion of Roxithromycin in Healthy AdultFemale Subjects
20170

About Huma Naz

Huma Naz is a scholar working on Health Information Management, Radiology, Nuclear Medicine and Imaging, Artificial Intelligence, Computer Vision and Pattern Recognition and Infectious Diseases, having authored 22 papers that have together received 399 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (9 papers), Retinal Imaging and Analysis (8 papers), Imbalanced Data Classification Techniques (4 papers), Digital Imaging for Blood Diseases (3 papers), Tuberculosis Research and Epidemiology (2 papers), Retinal Diseases and Treatments (2 papers), Sentiment Analysis and Opinion Mining (2 papers) and Software System Performance and Reliability (1 paper). The work is most often cited by research in Health Information Management (236 citations), Complementary and alternative medicine (46 citations), Health Informatics (7 citations), Artificial Intelligence (167 citations) and Radiology, Nuclear Medicine and Imaging (81 citations). Huma Naz has collaborated with scholars based in India, Saudi Arabia and Pakistan. Frequent co-authors include Sachin Ahuja, Neelu Jyothi Ahuja, Asimul Islam, Md. Imtaiyaz Hassan, Faizan Ahmad, Abdül Waheed, William S. Sly, Deepak Kumar, Amjad Rehman and Tanzila Saba. Their work appears in journals such as IEEE Access, Scientific Reports, Multimedia Tools and Applications, Microscopy Research and Technique and Artificial Intelligence Review.

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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