Asad Ullah

987 total citations · 1 hit paper
32 papers, 546 citations indexed

About

Asad Ullah is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Experimental and Cognitive Psychology. According to data from OpenAlex, Asad Ullah has authored 32 papers receiving a total of 546 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 5 papers in Experimental and Cognitive Psychology. Recurrent topics in Asad Ullah's work include Visual Attention and Saliency Detection (6 papers), Image and Video Quality Assessment (6 papers) and Face and Expression Recognition (6 papers). Asad Ullah is often cited by papers focused on Visual Attention and Saliency Detection (6 papers), Image and Video Quality Assessment (6 papers) and Face and Expression Recognition (6 papers). Asad Ullah collaborates with scholars based in Pakistan, China and Saudi Arabia. Asad Ullah's co-authors include Muhammad Shahid Anwar, Sadique Ahmad, Wahab Khan, Zesong Fei, Muhammad Shoaib, Tariq Hussain, Jing Wang, Babar Shah, Farman Ali and Fayadh Alenezi and has published in prestigious journals such as IEEE Access, Frontiers in Plant Science and Speech Communication.

In The Last Decade

Asad Ullah

29 papers receiving 527 citations

Hit Papers

An advanced deep learning models-based plant disease dete... 2023 2026 2024 2025 2023 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. An advanced deep learning models-based plant disease detection: A review of recent research
    2023 191 citations Muhammad Shoaib, Babar Shah et al. Frontiers in Plant Science profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Asad Ullah Pakistan 12 195 194 77 74 59 32 546
Sifat Momen Bangladesh 12 183 0.9× 226 1.2× 22 0.3× 221 3.0× 107 1.8× 42 717
Abeer Ali Alnuaim Saudi Arabia 13 97 0.5× 90 0.5× 31 0.4× 132 1.8× 43 0.7× 30 571
Morteza Zahedi Iran 12 213 1.1× 37 0.2× 155 2.0× 138 1.9× 21 0.4× 53 516
K. Meenakshi India 15 122 0.6× 69 0.4× 13 0.2× 118 1.6× 24 0.4× 86 668
Atul Sajjanhar Australia 10 210 1.1× 122 0.6× 18 0.2× 91 1.2× 6 0.1× 55 568
Richa Gupta India 12 109 0.6× 73 0.4× 14 0.2× 88 1.2× 20 0.3× 86 480
Yi Ji China 12 327 1.7× 49 0.3× 32 0.4× 56 0.8× 10 0.2× 70 526
Muhammad Munsif Pakistan 9 88 0.5× 55 0.3× 38 0.5× 54 0.7× 28 0.5× 15 286
Selina Sharmin Bangladesh 15 36 0.2× 85 0.4× 47 0.6× 228 3.1× 26 0.4× 45 652
Yuita Arum Sari Indonesia 13 67 0.3× 60 0.3× 52 0.7× 84 1.1× 49 0.8× 62 367

Countries citing papers authored by Asad Ullah

Since Specialization
Citations

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

Fields of papers citing papers by Asad Ullah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Asad Ullah

This figure shows the co-authorship network connecting the top 25 collaborators of Asad Ullah. A scholar is included among the top collaborators of Asad Ullah 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 Asad Ullah. Asad Ullah 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.
2.
Shoaib, Muhammad, Babar Shah, Shaker El–Sappagh, et al.. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science. 14. 1158933–1158933. 191 indexed citations breakdown →
3.
Shoaib, Muhammad, Babar Shah, Shaker El–Sappagh, et al.. (2023). Corrigendum: An advanced deep learning models-based plant disease detection: a review of recent research. Frontiers in Plant Science. 14. 1282443–1282443. 29 indexed citations
4.
Ullah, Asad, et al.. (2023). Augmenting the Robustness and Efficiency of Violence Detection Systems for Surveillance and Non-Surveillance Scenarios. IEEE Access. 11. 123295–123313. 8 indexed citations
5.
6.
Ullah, Asad, et al.. (2022). Comparison of Machine Learning Algorithms for Sepsis Detection. 4(1). 175–188. 1 indexed citations
7.
Ullah, Asad, Awais Aziz Shah, Jan Sher Khan, et al.. (2022). An Efficient Lightweight Image Encryption Scheme Using Multichaos. Security and Communication Networks. 2022. 1–16. 15 indexed citations
8.
Ullah, Asad, Samira Sanami, Sajjad Ahmad, et al.. (2022). Proteome Wide Screening of Potential Vaccine Targets Against Brucella Melitensis. SSRN Electronic Journal. 2 indexed citations
9.
Ullah, Asad, et al.. (2022). Unsupervised Automatic Speech Recognition: A review. Speech Communication. 139. 76–91. 41 indexed citations
10.
Ullah, Asad, et al.. (2021). Empirical Investigation of Multimodal Sensors in Novel Deep Facial Expression Recognition In‐the‐Wild. Journal of Sensors. 2021(1). 5 indexed citations
11.
Ullah, Asad, Jing Wang, Muhammad Shahid Anwar, et al.. (2021). Fusion of Machine Learning and Privacy Preserving for Secure Facial Expression Recognition. Security and Communication Networks. 2021. 1–12. 11 indexed citations
12.
13.
Anwar, Muhammad Shahid, Jing Wang, Asad Ullah, et al.. (2020). Measuring quality of experience for 360-degree videos in virtual reality. Science China Information Sciences. 63(10). 25 indexed citations
14.
Ullah, Asad, et al.. (2019). Facial Expression Recognition of Nonlinear Facial Variations Using Deep Locality De-Expression Residue Learning in the Wild. Electronics. 8(12). 1487–1487. 7 indexed citations
15.
Anwar, Muhammad Shahid, et al.. (2019). Impact of Stalling on QoE for 360-degree Virtual Reality Videos. 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). 1–6. 7 indexed citations
16.
Usman, Ahmad, et al.. (2018). Securing Insulin Pump System Using Deep Learning and Gesture Recognition. 1716–1719. 14 indexed citations
17.
18.
Ullah, Asad, et al.. (2017). Consumer’s Satisfaction Level about Grameen Phone’s (GP) Service in Bangladesh: A Case Study on Perception Analysis. Journal of Business & Financial Affairs. 6(2). 1 indexed citations
19.
Ullah, Asad, et al.. (2016). Comparative analysis of motion planning techniques for autonomous mobile robots. 23–28. 1 indexed citations
20.
Ullah, Asad. (2014). EFFECTS OF SUPPLY CHAIN COLLABORATION: A STUDY OF AUTOMOBILE MANUFACTURING COMPANIES IN INDIA. International journal of research in commerce, economics and management. 1 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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