Muhammad Summair Raza

450 total citations
23 papers, 291 citations indexed

About

Muhammad Summair Raza is a scholar working on Computational Theory and Mathematics, Information Systems and Signal Processing. According to data from OpenAlex, Muhammad Summair Raza has authored 23 papers receiving a total of 291 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computational Theory and Mathematics, 15 papers in Information Systems and 5 papers in Signal Processing. Recurrent topics in Muhammad Summair Raza's work include Rough Sets and Fuzzy Logic (16 papers), Data Mining Algorithms and Applications (12 papers) and Multi-Criteria Decision Making (5 papers). Muhammad Summair Raza is often cited by papers focused on Rough Sets and Fuzzy Logic (16 papers), Data Mining Algorithms and Applications (12 papers) and Multi-Criteria Decision Making (5 papers). Muhammad Summair Raza collaborates with scholars based in Pakistan, United Kingdom and Saudi Arabia. Muhammad Summair Raza's co-authors include Usman Qamar, Muhammad Ibrahim, Said Nabi, Khaled Salah, Hosam Alhakami, Karim Djemame, Abdullah Baz, Altaf Hussain, Rasheed Hussain and S. M. Ahsan Kazmi and has published in prestigious journals such as IEEE Access, Pattern Recognition and Information Sciences.

In The Last Decade

Muhammad Summair Raza

23 papers receiving 277 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Muhammad Summair Raza Pakistan 11 175 171 108 61 46 23 291
Igor Chikalov Saudi Arabia 11 116 0.7× 201 1.2× 177 1.6× 21 0.3× 31 0.7× 54 319
Saïd Jabbour France 8 80 0.5× 140 0.8× 160 1.5× 144 2.4× 17 0.4× 41 293
Hideya Iwasaki Japan 11 97 0.6× 91 0.5× 195 1.8× 176 2.9× 35 0.8× 40 397
Beata Zielosko Poland 11 156 0.9× 218 1.3× 201 1.9× 10 0.2× 37 0.8× 54 308
Duy-Tai Dinh Japan 10 155 0.9× 70 0.4× 181 1.7× 24 0.4× 10 0.2× 18 288
Jingwei Cheng China 11 123 0.7× 33 0.2× 264 2.4× 64 1.0× 54 1.2× 41 339
Bruno Crémilleux France 9 141 0.8× 84 0.5× 147 1.4× 30 0.5× 10 0.2× 25 258
Javier G. Marı́n-Blázquez Spain 10 46 0.3× 51 0.3× 192 1.8× 63 1.0× 76 1.7× 20 292
Dietmar Seipel Germany 8 125 0.7× 40 0.2× 186 1.7× 39 0.6× 11 0.2× 60 291
Olaf Owe Norway 11 98 0.6× 158 0.9× 315 2.9× 217 3.6× 15 0.3× 73 450

Countries citing papers authored by Muhammad Summair Raza

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Summair Raza

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Muhammad Summair Raza

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

All Works

20 of 20 papers shown
1.
Khan, Ali Haider, et al.. (2024). K‐Means Centroids Initialization Based on Differentiation Between Instances Attributes. International Journal of Intelligent Systems. 2024(1). 2 indexed citations
2.
Qamar, Usman & Muhammad Summair Raza. (2024). Applied Text Mining. 1 indexed citations
3.
Qamar, Usman, et al.. (2024). An incremental approach for calculating dominance-based rough set dependency. Soft Computing. 28(5). 3757–3781. 2 indexed citations
4.
Qamar, Usman & Muhammad Summair Raza. (2023). Data Science Concepts and Techniques with Applications. 4 indexed citations
5.
Qamar, Usman & Muhammad Summair Raza. (2023). A computationally efficient approximation calculation method in dominance-based rough set approach. Applied Soft Computing. 148. 110926–110926. 2 indexed citations
6.
Qamar, Usman, et al.. (2022). A parallel rule-based approach to compute rough approximations of dominance based rough set theory. Engineering Applications of Artificial Intelligence. 115. 105285–105285. 4 indexed citations
7.
Ibrahim, Muhammad, Said Nabi, Abdullah Baz, et al.. (2020). An In-Depth Empirical Investigation of State-of-the-Art Scheduling Approaches for Cloud Computing. IEEE Access. 8. 128282–128294. 29 indexed citations
8.
Qamar, Usman & Muhammad Summair Raza. (2020). Data Science Concepts and Techniques with Applications. 6 indexed citations
9.
Qamar, Usman, et al.. (2020). An optimized method to calculate approximations in Dominance based Rough Set Approach. Applied Soft Computing. 97. 106731–106731. 11 indexed citations
10.
Ibrahim, Muhammad, Said Nabi, Rasheed Hussain, et al.. (2020). A Comparative Analysis of Task Scheduling Approaches in Cloud Computing. Explore Bristol Research. 681–684. 23 indexed citations
11.
Raza, Muhammad Summair, et al.. (2020). An Improved Approach for Finding Rough Set Based Dynamic Reducts. IEEE Access. 8. 173008–173023. 5 indexed citations
12.
Raza, Muhammad Summair & Usman Qamar. (2019). Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. 11 indexed citations
13.
Raza, Muhammad Summair & Usman Qamar. (2019). A parallel approach to calculate lower and upper approximations in dominance based rough set theory. Applied Soft Computing. 84. 105699–105699. 18 indexed citations
14.
Raza, Muhammad Summair & Usman Qamar. (2017). Feature selection using rough set-based direct dependency calculation by avoiding the positive region. International Journal of Approximate Reasoning. 92. 175–197. 32 indexed citations
15.
Raza, Muhammad Summair & Usman Qamar. (2017). Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications. DIAL (Catholic University of Leuven). 12 indexed citations
16.
Raza, Muhammad Summair & Usman Qamar. (2017). Redefining core preliminary concepts of classic Rough Set Theory for feature selection. Engineering Applications of Artificial Intelligence. 65. 375–387. 10 indexed citations
17.
Raza, Muhammad Summair & Usman Qamar. (2017). A parallel rough set based dependency calculation method for efficient feature selection. Applied Soft Computing. 71. 1020–1034. 23 indexed citations
18.
Raza, Muhammad Summair & Usman Qamar. (2016). An incremental dependency calculation technique for feature selection using rough sets. Information Sciences. 343-344. 41–65. 51 indexed citations
19.
Raza, Muhammad Summair & Usman Qamar. (2016). A Rough Set Based Feature Selection Approach Using Random Feature Vectors. 47. 229–234. 2 indexed citations
20.
Raza, Muhammad Summair & Usman Qamar. (2016). A hybrid feature selection approach based on heuristic and exhaustive algorithms using Rough set theory. 1–7. 10 indexed citations

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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