Binish Fatimah

28 papers receiving 605 citations

Peers

Binish Fatimah
Comparison fields: 5 of 83
  • Cognitive Neuroscience 245
  • Cardiology and Cardiovascular Medicine 204
  • Signal Processing 96
  • Health Informatics 8
  • Human-Computer Interaction 28
Replace Y. Padma Sai with:
Y. Padma Sai India
Palani Thanaraj Krishnan India
Adil Deniz Duru Türkiye
Syed Khairul Bashar United States
Sajad Mousavi United States
Prathosh AP India
Yakup Kutlu Türkiye
Shivnarayan Patidar India
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Qingxue Zhang United States
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Citations per field
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Citations per year

Countries citing papers authored by Binish Fatimah

Since Specialization
Citations

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

Fields of papers citing papers by Binish Fatimah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 18 scholars most cited alongside Binish Fatimah, 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 Binish Fatimah Line = papers co-authored together Binish Fatimah links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019123
2 2020101
3 2022100
4 202156
5 202146
6 202242
7 202028
8 202025
9 202017
10 202212
11 202111
12 20228
13 20247
14 20216
15 20216
16 20234
17 20093
18 20203
19 20213
20 20223

About Binish Fatimah

Binish Fatimah is a scholar working on Cognitive Neuroscience, Signal Processing, Computer Vision and Pattern Recognition, Cardiology and Cardiovascular Medicine and Biomedical Engineering, having authored 31 papers that have together received 617 indexed citations. Recurring topics across this work include EEG and Brain-Computer Interfaces (11 papers), Blind Source Separation Techniques (7 papers), ECG Monitoring and Analysis (6 papers), Image and Signal Denoising Methods (5 papers), Speech and Audio Processing (4 papers), COVID-19 epidemiological studies (3 papers), Muscle activation and electromyography studies (3 papers) and Phonocardiography and Auscultation Techniques (3 papers). The work is most often cited by research in Cognitive Neuroscience (245 citations), Cardiology and Cardiovascular Medicine (204 citations), Signal Processing (96 citations), Health Informatics (8 citations) and Human-Computer Interaction (28 citations). Binish Fatimah has collaborated with scholars based in India and United States. Frequent co-authors include Pushpendra Singh, Amit Singhal, Ram Bilas Pachori, Shiv Dutt Joshi, Anubha Gupta, Priya Aggarwal, Pranav Soman, Abhishek Javali, G. Priyanka and Siddarth Jain. Their work appears in journals such as Biomedical Signal Processing and Control, Computers in Biology and Medicine, Journal of Applied Biomedicine, IEEE Signal Processing Magazine and Signal Processing.

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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