Ghulum Bakiri

2.9k citations
5 papers · 1.8k indexed · 1 hit paper · h-index 4
Topics
Imbalanced Data Classification Techniques (2 papers)Neural Networks and Applications (2 papers)Machine Learning and Algorithms (2 papers)

In The Last Decade

Ghulum Bakiri

4 papers receiving 1.6k citations

Hit Papers

Solving Multiclass Learning Problems via Error-Correcting...1995202620052015199550010001.5k

Peers

Ghulum Bakiri
Comparison fields: 5 of 121
  • Artificial Intelligence 1.1k
  • Computer Vision and Pattern Recognition 713
  • Signal Processing 180
  • Information Systems 138
  • Molecular Biology 137
Replace Douglas E. Zongker with:
Douglas E. Zongker United States
Francesco Masulli Italy
Michael Gray United States
Mineichi Kudo Japan
Maurizio Filippone United Kingdom
Bernd Fritzke Germany
Kap Luk Chan Singapore
Kenneth W. Bauer United States
Tao Xiong United States
Il-Seok Oh South Korea
Ghulum Bakiri relative to Douglas E. Zongker United States Douglas E. Zongker's profile →
Citations per field
00.5×1.5×
Douglas E. Zongker · 1×
Citations per year

Countries citing papers authored by Ghulum Bakiri

Since Specialization
Citations

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

Fields of papers citing papers by Ghulum Bakiri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ghulum Bakiri

This figure shows the co-authorship network connecting the top 25 collaborators of Ghulum Bakiri. A scholar is included among the top collaborators of Ghulum Bakiri based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ghulum Bakiri. Ghulum Bakiri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

5 of 5 papers shown
#WorkIndexed citations
1
Solving Multiclass Learning Problems via Error-Correcting Output Codesbreakdown →
1698
2 21
3 41
4
Error-Correcting Output Codes: A General Method for Improving
0
5
Converting English text to speech: a machine learning approach
10

About Ghulum Bakiri

Ghulum Bakiri is a scholar working on Artificial Intelligence, Signal Processing and Information Systems, having authored 5 papers that have together received 1.8k indexed citations. Recurring topics across this work include Imbalanced Data Classification Techniques (2 papers), Neural Networks and Applications (2 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (713 citations), Artificial Intelligence (1.1k citations) and Signal Processing (180 citations). Ghulum Bakiri has collaborated with scholars based in Bahrain, United States and Germany. Frequent co-authors include Tom Dietterich, Thomas G. Dietterich and Hermann Hild. Their work appears in journals such as Machine Learning and Journal of Artificial Intelligence Research.

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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