Muhammad Hammad Memon

1.8k citations
35 papers · 1.2k indexed · 1 hit paper · h-index 15

Impact in

Papers in

Muhammad Hammad Memon

34 papers receiving 1.1k citations

Hit Papers

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms 2018 · 393 citations
3930+2+5Years since publication100200300

Peers

Muhammad Hammad Memon
Comparison fields: 5 of 97
  • Health Information Management 554
  • Medical Laboratory Technology 68
  • Health Informatics 28
  • Artificial Intelligence 586
  • Radiology, Nuclear Medicine and Imaging 200
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Citations per field
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Citations per year

Countries citing papers authored by Muhammad Hammad Memon

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Hammad Memon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Muhammad Hammad Memon. 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 Hammad Memon. The network helps show where Muhammad Hammad Memon may publish in the future.

Co-authors

The 25 scholars most cited alongside Muhammad Hammad Memon, 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 Muhammad Hammad Memon Line = papers co-authored together Muhammad Hammad Memon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms
Hit paper breakdown →
2018393
2 2019140
3 2020108
4 201969
5 201657
6 201952
7 201751
8 201841
9 201738
10 201438
11 201935
12 202027
13 201423
14 201922
15 201518
16 201413
17 201912
18 201911
19 20209
20
PERFORMANCE EVALUATION OF CLASSIFICATION METHODS FOR HEART DISEASE DATASET
20157

About Muhammad Hammad Memon

Muhammad Hammad Memon is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Health Information Management, Media Technology and Information Systems, having authored 35 papers that have together received 1.2k indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (10 papers), Advanced Image and Video Retrieval Techniques (9 papers), Artificial Intelligence in Healthcare (8 papers), Imbalanced Data Classification Techniques (4 papers), AI in cancer detection (3 papers), Medical Image Segmentation Techniques (3 papers), Remote-Sensing Image Classification (3 papers) and COVID-19 diagnosis using AI (2 papers). The work is most often cited by research in Health Information Management (554 citations), Medical Laboratory Technology (68 citations), Health Informatics (28 citations), Artificial Intelligence (586 citations) and Radiology, Nuclear Medicine and Imaging (200 citations). Muhammad Hammad Memon has collaborated with scholars based in China, Pakistan and Saudi Arabia. Frequent co-authors include Jianping Li, Amin Ul Haq, Shah Nazir, Jalaluddin Khan, Imran Memon, Qasim Ali Arain, Muhammad Hunain Memon, Amjad Ali, Riaz Ahmed Shaikh and Wang Zhou. Their work appears in journals such as Journal of Intelligent & Fuzzy Systems, Multimedia Tools and Applications, IEEE Transactions on Computational Social Systems, Wireless Personal Communications and Complexity.

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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