Farrukh Jamal

3.2k total citations
181 papers, 2.2k citations indexed

About

Farrukh Jamal is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Global and Planetary Change. According to data from OpenAlex, Farrukh Jamal has authored 181 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 130 papers in Statistics and Probability, 77 papers in Statistics, Probability and Uncertainty and 44 papers in Global and Planetary Change. Recurrent topics in Farrukh Jamal's work include Statistical Distribution Estimation and Applications (125 papers), Probabilistic and Robust Engineering Design (72 papers) and Hydrology and Drought Analysis (44 papers). Farrukh Jamal is often cited by papers focused on Statistical Distribution Estimation and Applications (125 papers), Probabilistic and Robust Engineering Design (72 papers) and Hydrology and Drought Analysis (44 papers). Farrukh Jamal collaborates with scholars based in Pakistan, Saudi Arabia and France. Farrukh Jamal's co-authors include Christophe Chesneau, Mohammed Elgarhy, Rashad A. R. Bantan, M. H. Tahir, Aqib Ali, Ibrahim Elbatal, Samreen Naeem, Abdullah M. Almarashi, Wali Khan Mashwani and Sania Anam and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Farrukh Jamal

161 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Farrukh Jamal Pakistan 28 1.5k 837 401 252 246 181 2.2k
Christophe Chesneau France 29 2.3k 1.5× 1.1k 1.3× 479 1.2× 567 2.3× 409 1.7× 399 3.4k
M. H. Tahir Pakistan 23 1.5k 1.0× 912 1.1× 396 1.0× 212 0.8× 344 1.4× 112 1.9k
Manuel Febrero–Bande Spain 23 959 0.6× 276 0.3× 205 0.5× 97 0.4× 157 0.6× 66 2.6k
Mohammed Elgarhy Saudi Arabia 30 2.6k 1.8× 1.6k 1.9× 647 1.6× 397 1.6× 409 1.7× 244 3.0k
Thomas Mathew United States 25 1.5k 1.0× 729 0.9× 98 0.2× 48 0.2× 469 1.9× 148 2.6k
Gilberto A. Paula Brazil 26 1.6k 1.1× 383 0.5× 82 0.2× 164 0.7× 214 0.9× 92 1.9k
Reinaldo B. Arellano‐Valle Chile 27 2.1k 1.4× 609 0.7× 236 0.6× 549 2.2× 359 1.5× 99 2.6k
B. M. Golam Kibria United States 27 2.3k 1.6× 1.1k 1.3× 128 0.3× 79 0.3× 189 0.8× 142 2.9k
Artur J. Lemonte Brazil 27 2.0k 1.4× 1.0k 1.2× 346 0.9× 220 0.9× 182 0.7× 114 2.2k
Wenceslao González–Manteiga Spain 31 1.8k 1.2× 269 0.3× 246 0.6× 264 1.0× 314 1.3× 172 3.1k

Countries citing papers authored by Farrukh Jamal

Since Specialization
Citations

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

Fields of papers citing papers by Farrukh Jamal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Farrukh Jamal

This figure shows the co-authorship network connecting the top 25 collaborators of Farrukh Jamal. A scholar is included among the top collaborators of Farrukh Jamal 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 Farrukh Jamal. Farrukh Jamal 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.
Al‐Essa, Laila A., et al.. (2025). Properties and Applications of Neutrosophic Burr XII Distribution. International Journal of Computational Intelligence Systems. 18(1).
2.
3.
Almetwally, Ehab M., et al.. (2025). A novel odd Type-X family of distributions: Model, theory, and applications to medical, insurance, and engineering data sets. Journal of Radiation Research and Applied Sciences. 18(2). 101451–101451.
4.
Jamal, Farrukh, et al.. (2025). Multidimensional Impact of Smog on Respiratory and Ocular Health: A Cross‐Sectional Study With Socio‐Psychological and Public Health Prospective. Health Science Reports. 8(9). e71205–e71205. 1 indexed citations
5.
Jamal, Farrukh, et al.. (2024). The New Extended Exponentiated Burr XII distribution: Properties and applications. SHILAP Revista de lepidopterología. 18(1). 101200–101200. 2 indexed citations
6.
7.
Alsadat, Najwan, et al.. (2024). Modeling to COVID-19 and cancer data: Using the generalized Burr-Hatke model. SHILAP Revista de lepidopterología. 17(3). 100972–100972. 2 indexed citations
8.
9.
Jamal, Farrukh, et al.. (2023). A new univariate continuous distribution with applications in reliability. AIP Advances. 13(11). 3 indexed citations
10.
Fayomi, Aisha, et al.. (2022). A New Useful Exponential Model with Applications to Quality Control and Actuarial Data. Computational Intelligence and Neuroscience. 2022. 1–27. 9 indexed citations
11.
Ahmadini, Abdullah Ali H., Sohail Akhtar, Christophe Chesneau, et al.. (2021). Robust Assessing the Lifetime Performance of Products with Inverse Gaussian Distribution in Bayesian and Classical Setup. Mathematical Problems in Engineering. 2021. 1–9. 4 indexed citations
12.
Naeem, Samreen, Aqib Ali, Christophe Chesneau, et al.. (2021). The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach. Agronomy. 11(2). 263–263. 68 indexed citations
13.
Al-Babtain, Abdulhakim A., et al.. (2021). The extended Burr-R class: properties, applications and modified test for censored data. AIMS Mathematics. 6(3). 2912–2931. 8 indexed citations
14.
Bantan, Rashad A. R., Aqib Ali, Samreen Naeem, et al.. (2020). Discrimination of sunflower seeds using multispectral and texture dataset in combination with region selection and supervised classification methods. Chaos An Interdisciplinary Journal of Nonlinear Science. 30(11). 113142–113142. 15 indexed citations
15.
Ali, Aqib, Salman Qadri, Wali Khan Mashwani, et al.. (2020). Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image. Entropy. 22(5). 567–567. 59 indexed citations
16.
Naeem, Samreen, Aqib Ali, Salman Qadri, et al.. (2020). Machine-Learning Based Hybrid-Feature Analysis for Liver Cancer Classification Using Fused (MR and CT) Images. Applied Sciences. 10(9). 3134–3134. 60 indexed citations
17.
Bantan, Rashad A. R., Farrukh Jamal, Mohammed Elgarhy, et al.. (2020). Some New Facts about the Unit-Rayleigh Distribution with Applications. Mathematics. 8(11). 1954–1954. 45 indexed citations
18.
Ali, Aqib, Wali Khan Mashwani, Samir Brahim Belhaouari, et al.. (2020). Machine learning approach for the classification of corn seed using hybrid features. International Journal of Food Properties. 23(1). 1110–1124. 60 indexed citations
19.
Singh, Ranjan K., et al.. (2015). Aureobasidium pullulans - an industrially important pullulan producing black yeast.. International Journal of Current Microbiology and Applied Sciences. 4(10). 605–622. 10 indexed citations
20.
Jamal, Farrukh. (2011). Simple Approach to Reactive Dye Decolorization Using Trichosanthes dioica Proteins at Low Concentration of 1-hydroxybenzotriazole. Current Trends in Biotechnology and Pharmacy. 5(3). 1273–1281.

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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