Shamila Nasreen

7 papers receiving 838 citations

Hit Papers

A survey of feature selection and feature extraction tech...20142026201820222014250500750

Peers

Shamila Nasreen
Comparison fields: 5 of 144
  • Artificial Intelligence 377
  • Computer Vision and Pattern Recognition 161
  • Computer Networks and Communications 93
  • Information Systems 92
  • Signal Processing 92
Replace Samina Khalid with:
Samina Khalid Pakistan
Jeff Heaton United States
Miloš Antonijević Serbia
Maria Presa Reyes United States
Jwan Najeeb Saeed Iraq
Yilin Yan United States
Brian C. Van Essen United States
Jafar Tanha Iran
Muhammad Aamir Malaysia
Maruthi Rohit Ayyagari United States
Shamila Nasreen relative to Samina Khalid Pakistan Samina Khalid's profile →
Citations per field
00.5×1.5×
Samina Khalid · 1×
Citations per year

Countries citing papers authored by Shamila Nasreen

Since Specialization
Citations

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

Fields of papers citing papers by Shamila Nasreen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shamila Nasreen

This figure shows the co-authorship network connecting the top 25 collaborators of Shamila Nasreen. A scholar is included among the top collaborators of Shamila Nasreen 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 Shamila Nasreen. Shamila Nasreen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

7 of 7 papers shown
#WorkIndexed citations
1 4
2 12
3 30
4 12
5 7
6
A survey of feature selection and feature extraction techniques in machine learningbreakdown →
777
7 35

About Shamila Nasreen

Shamila Nasreen is a scholar working on Ophthalmology, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 7 papers that have together received 877 indexed citations. Recurring topics across this work include IoT and Edge/Fog Computing (1 paper), Retinal Imaging and Analysis (1 paper) and Emotion and Mood Recognition (1 paper). The work is most often cited by research in Artificial Intelligence (377 citations), Signal Processing (92 citations) and Computer Vision and Pattern Recognition (161 citations). Shamila Nasreen has collaborated with scholars based in Pakistan, United Kingdom and Slovenia. Frequent co-authors include Samina Khalid, Tehmina Khalil, Julian Hough, Matthew Purver, Usman Naeem, Mustansar Ali Ghazanfar, Muhammad Awais Azam, Khurram Shehzad, Arslan Shaukat and Muhammad Usman Akram. Their work appears in journals such as Arabian Journal for Science and Engineering, International Journal of Imaging Systems and Technology and Frontiers in Computer Science.

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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