Muhammad Shafay

408 total citations
10 papers, 203 citations indexed

About

Muhammad Shafay is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Muhammad Shafay has authored 10 papers receiving a total of 203 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 2 papers in Computer Networks and Communications. Recurrent topics in Muhammad Shafay's work include Advanced Neural Network Applications (3 papers), Prostate Cancer Diagnosis and Treatment (2 papers) and Machine Learning and ELM (2 papers). Muhammad Shafay is often cited by papers focused on Advanced Neural Network Applications (3 papers), Prostate Cancer Diagnosis and Treatment (2 papers) and Machine Learning and ELM (2 papers). Muhammad Shafay collaborates with scholars based in United Arab Emirates, United States and Australia. Muhammad Shafay's co-authors include Naoufel Werghi, Taimur Hassan, Raja Wasim Ahmad, Mohammed Omar, Ibrar Yaqoob, Khaled Salah, Raja Jayaraman, Ernesto Damiani, Samet Akçay and Mohammed Bennamoun and has published in prestigious journals such as Scientific Reports, Sensors and Computers in Biology and Medicine.

In The Last Decade

Muhammad Shafay

10 papers receiving 195 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Muhammad Shafay United Arab Emirates	6	73	60	59	30	27	10	203
M. Adimoolam India	9	78 1.1×	38 0.6×	51 0.9×	51 1.7×	42 1.6×	26	232
Uddagiri Sirisha India	7	79 1.1×	17 0.3×	78 1.3×	17 0.6×	34 1.3×	20	280
G. Sajiv India	8	53 0.7×	20 0.3×	75 1.3×	37 1.2×	26 1.0×	55	263
M. A. El-Dosuky Egypt	8	58 0.8×	18 0.3×	41 0.7×	18 0.6×	20 0.7×	30	219
Kamal Kant Hiran India	9	63 0.9×	32 0.5×	30 0.5×	73 2.4×	25 0.9×	23	236
Vinoth Kumar Venkatesan India	6	90 1.2×	37 0.6×	29 0.5×	32 1.1×	11 0.4×	9	238
R. Sabitha India	8	59 0.8×	28 0.5×	42 0.7×	79 2.6×	30 1.1×	55	241
Khaled Badran Egypt	10	175 2.4×	44 0.7×	22 0.4×	31 1.0×	20 0.7×	29	301
Majed Alsafyani Saudi Arabia	9	84 1.2×	43 0.7×	37 0.6×	44 1.5×	11 0.4×	29	241
Khaled Alhazmi Saudi Arabia	8	57 0.8×	59 1.0×	34 0.6×	96 3.2×	11 0.4×	16	249

Countries citing papers authored by Muhammad Shafay

Since Specialization

Citations

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

Fields of papers citing papers by Muhammad Shafay

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Muhammad Shafay

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

All Works

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10 of 10 papers shown

1.

Shafay, Muhammad, Muhammad Owais, Irfan Hussain, et al.. (2025). Recent advances in plant disease detection: challenges and opportunities. Plant Methods. 21(1). 140–140. 1 indexed citations

2.

Shafay, Muhammad, et al.. (2024). Semi-supervised Segmentation-driven Classification Pipeline for Grading Cassava Leaf Diseases. 1–6. 1 indexed citations

3.

Khan, Asim, et al.. (2023). Tomato maturity recognition with convolutional transformers. Scientific Reports. 13(1). 22885–22885. 28 indexed citations

4.

Shafay, Muhammad, et al.. (2023). Programmable broad learning system for baggage threat recognition. Multimedia Tools and Applications. 83(6). 16179–16196. 2 indexed citations

5.

Hassan, Taimur, Muhammad Shafay, Bilal Hassan, et al.. (2022). Knowledge distillation driven instance segmentation for grading prostate cancer. Computers in Biology and Medicine. 150. 106124–106124. 13 indexed citations

6.

Shafay, Muhammad, Raja Wasim Ahmad, Khaled Salah, et al.. (2022). Blockchain for deep learning: review and open challenges. Cluster Computing. 26(1). 197–221. 95 indexed citations

7.

Hassan, Taimur, Bilal Hassan, Muhammad Shafay, et al.. (2022). Incremental Instance Segmentation for the Gleason Tissues Driven Prostate Cancer Prognosis. 65. 1–6. 1 indexed citations

8.

Shafay, Muhammad, et al.. (2022). Programmable Broad Learning System to Detect Concealed and Imbalanced Baggage Threats. 1–6. 5 indexed citations

9.

Shafay, Muhammad, Taimur Hassan, Ernesto Damiani, & Naoufel Werghi. (2021). Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats. 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 232–237. 14 indexed citations

10.

Hassan, Taimur, Muhammad Shafay, Samet Akçay, et al.. (2020). Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats. Sensors. 20(22). 6450–6450. 43 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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