Shah Khalid

736 total citations · 1 hit paper
32 papers, 395 citations indexed

About

Shah Khalid is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Shah Khalid has authored 32 papers receiving a total of 395 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 14 papers in Information Systems and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Shah Khalid's work include Topic Modeling (10 papers), Web Data Mining and Analysis (7 papers) and Information Retrieval and Search Behavior (7 papers). Shah Khalid is often cited by papers focused on Topic Modeling (10 papers), Web Data Mining and Analysis (7 papers) and Information Retrieval and Search Behavior (7 papers). Shah Khalid collaborates with scholars based in Pakistan, China and Saudi Arabia. Shah Khalid's co-authors include Shengli Wu, Irfan Ullah, Jawad Khan, Farman Ali, Nasser Tairan, Fang Zhang, Hasan Ali Khattak, Habib Shah, Shah Khusro and Youngmoon Lee and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Frontiers in Plant Science.

In The Last Decade

Shah Khalid

32 papers receiving 368 citations

Hit Papers

Leveraging deep learning for plant disease and pest detec... 2025 2026 2025 5 10 15
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. Leveraging deep learning for plant disease and pest detection: a comprehensive review and future directions
    2025 15 citations Muhammad Shoaib, Abolghasem Sadeghi‐Niaraki et al. Frontiers in Plant Science profile →

Peers

Shah Khalid
Xinyu Tang China
Shiting Wen China
A. Vinaya Babu India
Jayakrushna Sahoo India
M. Suresha India
Aakanksha Sharaff India
Abdullah M. Baqasah Saudi Arabia
Vishan Kumar Gupta India
Komal Kumar Bhatia India
Ce Yang China
Xinyu Tang China
Shah Khalid
Citations per year, relative to Shah Khalid Shah Khalid (= 1×) peers Xinyu Tang

Countries citing papers authored by Shah Khalid

Since Specialization
Citations

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

Fields of papers citing papers by Shah Khalid

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shah Khalid

This figure shows the co-authorship network connecting the top 25 collaborators of Shah Khalid. A scholar is included among the top collaborators of Shah Khalid 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 Shah Khalid. Shah Khalid 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.
Shoaib, Muhammad, Abolghasem Sadeghi‐Niaraki, Farman Ali, Irfan Hussain, & Shah Khalid. (2025). Leveraging deep learning for plant disease and pest detection: a comprehensive review and future directions. Frontiers in Plant Science. 16. 1538163–1538163. 15 indexed citations breakdown →
2.
Khalid, Shah, et al.. (2025). Next Generation Human Action Recognition: A Comprehensive Review of State-of-the-Art Signal Processing Techniques. IEEE Access. 13. 135609–135633. 2 indexed citations
3.
4.
Azam, M., Shah Khalid, Hasan Ali Khattak, et al.. (2025). Current Trends and Advances in Extractive Text Summarization: A Comprehensive Review. IEEE Access. 13. 28150–28166. 1 indexed citations
5.
Khalid, Shah, et al.. (2025). Depression Detection in Social Media: A Comprehensive Review of Machine Learning and Deep Learning Techniques. IEEE Access. 13. 12789–12818. 4 indexed citations
6.
Khan, Jawad, et al.. (2024). DIAFM: An Improved and Novel Approach for Incremental Frequent Itemset Mining. Mathematics. 12(24). 3930–3930. 1 indexed citations
7.
Khalid, Shah, et al.. (2024). Human Action Recognition Systems: A Review of the Trends and State-of-the-Art. IEEE Access. 12. 36372–36390. 34 indexed citations
8.
Khalid, Shah, et al.. (2024). HADE: Exploiting Human Action Recognition Through Fine-Tuned Deep Learning Methods. IEEE Access. 12. 42769–42790. 9 indexed citations
9.
Khalid, Shah, et al.. (2024). Utilizing Ant Colony Optimization for Result Merging in Federated Search. Engineering Technology & Applied Science Research. 14(4). 14832–14839. 1 indexed citations
10.
Khalid, Shah, et al.. (2023). Understanding the impact of query expansion on federated search. Multimedia Tools and Applications. 83(4). 10393–10407. 6 indexed citations
11.
Khalid, Shah, et al.. (2022). BERT-based Extractive Text Summarization of Scholarly Articles: A Novel Architecture. 1–5. 25 indexed citations
12.
Zamli, Kamal Z., Fakhrud Din, Hussam S. Alhadawi, et al.. (2022). Exploiting an Elitist Barnacles Mating Optimizer implementation for substitution box optimization. ICT Express. 9(4). 619–627. 15 indexed citations
13.
Khalid, Shah, Shengli Wu, Abdul Wahid, Aftab Alam, & Irfan Ullah. (2021). An Effective Scholarly Search by Combining Inverted Indices and Structured Search With Citation Networks Analysis. IEEE Access. 9. 120210–120226. 17 indexed citations
14.
Ullah, Khalil & Shah Khalid. (2021). Automatic Detection of Noisy Signals in sEMG Grids Using Statistical Thresholding. 58(1). 1 indexed citations
15.
Khalid, Shah, et al.. (2020). Embedding based learning for collection selection in federated search. Data Technologies and Applications. 54(5). 703–717. 8 indexed citations
16.
Khan, Faheem, et al.. (2019). An Algorithmic Approach for Core Election in Mobile Ad-hoc Network. 網際網路技術學刊. 20(4). 1099–1111. 19 indexed citations
17.
Khalid, Shah, Shengli Wu, Aftab Alam, & Irfan Ullah. (2019). Real-time feedback query expansion technique for supporting scholarly search using citation network analysis. Journal of Information Science. 47(1). 3–15. 15 indexed citations
18.
Khusro, Shah, Aftab Alam, & Shah Khalid. (2017). Social question and answer sites: the story so far. Program electronic library and information systems. 51(2). 170–192. 7 indexed citations
19.
Bahadur, Ali, Zubeda Chaudhry, Mohammad Danish, et al.. (2016). Nutritional and elemental analyses of some selected fodder species used in traditional medicine. International Journal of Medicine and Medical Sciences. 8(1). 1–7. 5 indexed citations
20.
Bahadur, Ali, Zubeda Chaudhry, Mohammad Danish, et al.. (2011). Nutritional and elemental analyses of some selected fodder species used in traditional medicine. African Journal of Pharmacy and Pharmacology. 5(8). 1157–1161. 20 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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