M.T. Musavi

63 papers receiving 1.1k citations

Peers

M.T. Musavi
Comparison fields: 5 of 126
  • Artificial Intelligence 480
  • Computer Vision and Pattern Recognition 273
  • Control and Systems Engineering 270
  • Media Technology 99
  • Signal Processing 108
Replace Shen-Shyang Ho with:
Shen-Shyang Ho United States
Ping Zhong China
Senjian An Australia
Věra Kůrková Czechia
M. J. Nigam India
Pai-Hsuen Chen Taiwan
Maria Gabriella Xibilia Italy
Andrew R. Webb United Kingdom
Dennis DeCoste United States
M.T. Musavi relative to Shen-Shyang Ho United States Shen-Shyang Ho's profile →
Citations per field
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Citations per year

Countries citing papers authored by M.T. Musavi

Since Specialization
Citations

This map shows the geographic impact of M.T. Musavi'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 M.T. Musavi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M.T. Musavi more than expected).

Fields of papers citing papers by M.T. Musavi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M.T. Musavi. 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 M.T. Musavi. The network helps show where M.T. Musavi may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 70 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1992411
2 2001104
3 198997
4 198488
5 199454
6 200848
7 199446
8 200441
9 201825
10 201524
11 198823
12 201218
13
Comparison of MLP neural network and Kalman filter for localization in wireless sensor networks
200717
14 199316
15 199216
16 199514
17 201114
18
Double Self-Organizing Maps to Cluster Gene Expression Data
200213
19 201113
20 199811

About M.T. Musavi

M.T. Musavi is a scholar working on Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition and Molecular Biology, having authored 70 papers that have together received 1.2k indexed citations. Recurring topics across this work include Neural Networks and Applications (19 papers), Power System Optimization and Stability (13 papers), Gene expression and cancer classification (11 papers), Power Systems Fault Detection (9 papers), Face and Expression Recognition (6 papers), Blind Source Separation Techniques (6 papers), Water Quality Monitoring Technologies (5 papers) and Target Tracking and Data Fusion in Sensor Networks (5 papers). The work is most often cited by research in Artificial Intelligence (480 citations), Computer Vision and Pattern Recognition (273 citations), Control and Systems Engineering (270 citations), Media Technology (99 citations) and Signal Processing (108 citations). M.T. Musavi has collaborated with scholars based in United States and Australia. Frequent co-authors include D.M. Hummels, K.H. Chan, W. Ahmed, N. Narasimhamurthi, Mukul V. Shirvaikar, Peggy Agouris, Peter Doucette, Anthony Stefanidis, Yifeng Zhu and Habtom W. Ressom. Their work appears in journals such as Neural Networks, IEEE Transactions on Geoscience and Remote Sensing, Pattern Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Power Systems.

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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