Muhammad Tahir

44 papers receiving 681 citations

Muhammad Tahir's Hit Papers

Students’ Perception towards E-Learning during COVID-19 Pandemic in India: An Empirical Study 2020 · 234 citations
2340+2+4Years since publication50100150200

Peers

Muhammad Tahir
Comparison fields: 5 of 123
  • Computer Science Applications 56
  • Biophysics 51
  • Media Technology 63
  • Education 151
  • Computer Vision and Pattern Recognition 100
Replace Hussam N. Fakhouri with:
Hussam N. Fakhouri Jordan
Jiangbo Shu China
Suhaila Zainudin Malaysia
Antonella Carbonaro Italy
Kavita Khanna India
Muhammad Wasif Nisar Pakistan
Arif Ahmed Sekh India
J. Fdez‐Valdivia Spain
Daniel Manrique Spain
Muhammad Tahir relative to Hussam N. Fakhouri Jordan Hussam N. Fakhouri's profile →
Citations per field
00.5×10×17×
Hussam N. Fakhouri · 1×
Citations per year

Countries citing papers authored by Muhammad Tahir

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Tahir

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 44 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Students’ Perception towards E-Learning during COVID-19 Pandemic in India: An Empirical Study
Hit paper breakdown →
2020234
2 201136
3 201433
4 202132
5 202231
6 202028
7 201924
8 201624
9 201923
10 202121
11 201621
12 201320
13 202116
14 202114
15 202214
16 201312
17 201811
18 202411
19 202110
20 202210

About Muhammad Tahir

Muhammad Tahir is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology, Artificial Intelligence, Media Technology and Electrical and Electronic Engineering, having authored 44 papers that have together received 726 indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (9 papers), Cell Image Analysis Techniques (5 papers), Image Processing Techniques and Applications (3 papers), Advanced Neural Network Applications (3 papers), Stock Market Forecasting Methods (3 papers), Energy Load and Power Forecasting (3 papers), Online and Blended Learning (2 papers) and Vehicle License Plate Recognition (2 papers). The work is most often cited by research in Computer Science Applications (56 citations), Biophysics (51 citations), Media Technology (63 citations), Education (151 citations) and Computer Vision and Pattern Recognition (100 citations). Muhammad Tahir has collaborated with scholars based in Pakistan, Saudi Arabia and China. Frequent co-authors include Vivek Vivek, Mohammed Kamalun Nabi, Mohammed Arshad Khan, Asifullah Khan, Maqsood Hayat, Nawaf N. Hamadneh, Abdul Majid, Adnan Idris, Mohammad Khalid Imam Rahmani and Sher Afzal Khan. Their work appears in journals such as IEEE Access, Computers, materials & continua/Computers, materials & continua (Print), Sustainability, Annals of Nuclear Energy and Journal of Theoretical Biology.

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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