Amal Bukhari

584 total citations · 1 hit paper
23 papers, 158 citations indexed

About

Amal Bukhari is a scholar working on Artificial Intelligence, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Amal Bukhari has authored 23 papers receiving a total of 158 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 7 papers in Computer Networks and Communications and 5 papers in Information Systems. Recurrent topics in Amal Bukhari's work include Sentiment Analysis and Opinion Mining (4 papers), Big Data and Business Intelligence (3 papers) and Artificial Intelligence in Healthcare (3 papers). Amal Bukhari is often cited by papers focused on Sentiment Analysis and Opinion Mining (4 papers), Big Data and Business Intelligence (3 papers) and Artificial Intelligence in Healthcare (3 papers). Amal Bukhari collaborates with scholars based in Saudi Arabia, Pakistan and United Arab Emirates. Amal Bukhari's co-authors include Ali Daud, Riad Alharbey, Ali Haider Khan, Rabia Javed, Saba Mahmood, Afzal Badshah, Muhammad Kamran Malik, Ameen Banjar, Anam Naz and Hikmat Ullah Khan and has published in prestigious journals such as Scientific Reports, IEEE Access and Artificial Intelligence Review.

In The Last Decade

Amal Bukhari

18 papers receiving 147 citations

Hit Papers

BERT applications in natural language processing: a review 2025 2026 2025 5 10 15 20
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. BERT applications in natural language processing: a review
    2025 22 citations Ali Daud, Muhammad Kamran Malik et al. Artificial Intelligence Review profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Amal Bukhari Saudi Arabia 7 50 35 30 25 19 23 158
Ritika Mehra India 7 67 1.3× 22 0.6× 21 0.7× 25 1.0× 7 0.4× 22 231
Benedikt Kämpgen Germany 7 68 1.4× 48 1.4× 36 1.2× 24 1.0× 13 0.7× 19 144
Krishna Mridha India 8 113 2.3× 45 1.3× 14 0.5× 11 0.4× 9 0.5× 29 282
Pradeeban Kathiravelu Portugal 9 42 0.8× 39 1.1× 110 3.7× 66 2.6× 9 0.5× 35 217
Nitesh Pradhan India 10 107 2.1× 62 1.8× 12 0.4× 15 0.6× 11 0.6× 24 223
Bjarne Pfitzner Germany 7 160 3.2× 26 0.7× 24 0.8× 31 1.2× 6 0.3× 16 234
Najah Al-shanableh Jordan 7 62 1.2× 26 0.7× 15 0.5× 31 1.2× 6 0.3× 20 175
Shahid Munir Shah Pakistan 6 157 3.1× 84 2.4× 7 0.2× 18 0.7× 14 0.7× 14 224
Taminul Islam United States 9 96 1.9× 33 0.9× 6 0.2× 26 1.0× 4 0.2× 20 206
Muhammad Mateen Yaqoob Pakistan 11 129 2.6× 40 1.1× 51 1.7× 24 1.0× 3 0.2× 15 246

Countries citing papers authored by Amal Bukhari

Since Specialization
Citations

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

Fields of papers citing papers by Amal Bukhari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amal Bukhari

This figure shows the co-authorship network connecting the top 25 collaborators of Amal Bukhari. A scholar is included among the top collaborators of Amal Bukhari 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 Amal Bukhari. Amal Bukhari 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.
Daud, Ali, et al.. (2025). BERT applications in natural language processing: a review. Artificial Intelligence Review. 58(6). 22 indexed citations breakdown →
2.
Khan, Hikmat Ullah, et al.. (2025). Mental Health Sentiment Analysis: Exploring an Optimized BERT with Deep Encodings. Engineering Technology & Applied Science Research. 15(5). 26242–26248.
3.
Badshah, Afzal, et al.. (2025). Regional computing approach for educational big data. Scientific Reports. 15(1). 7619–7619. 2 indexed citations
4.
Al‐Khasawneh, Mahmoud Ahmad, et al.. (2025). Secure e-health framework using artificial intelligence and blockchain technology. International Journal of ADVANCED AND APPLIED SCIENCES. 12(2). 52–61.
5.
Mahmood, Saba, et al.. (2024). Healthcare insurance fraud detection using data mining. BMC Medical Informatics and Decision Making. 24(1). 112–112. 7 indexed citations
6.
Ali, Mushtaq, et al.. (2024). Fault tolerant & priority basis task offloading and scheduling model for IoT logistics. Alexandria Engineering Journal. 110. 400–419.
7.
Alsini, Raed, Anam Naz, Hikmat Ullah Khan, et al.. (2024). Using deep learning and word embeddings for predicting human agreeableness behavior. Scientific Reports. 14(1). 29875–29875. 10 indexed citations
8.
Jagatheesaperumal, Senthil Kumar, et al.. (2024). QCASBC: An algorithm for hardware-in-the-loop simulation of 3-link RRR robotic manipulator. Alexandria Engineering Journal. 103. 12–20.
9.
Daud, Ali, et al.. (2024). Time Series Reconstruction With Feature-Driven Imputation: A Comparison of Base Learning Algorithms. IEEE Access. 12. 85511–85530. 3 indexed citations
10.
Mahmood, Saba, et al.. (2024). A Lightweight and Secure Authentication Scheme for Remote Monitoring of Patients in IoMT. IEEE Access. 12. 73004–73020. 8 indexed citations
11.
Badshah, Afzal, et al.. (2024). Big data applications: overview, challenges and future. Artificial Intelligence Review. 57(11). 12 indexed citations
12.
Javed, Rabia, et al.. (2024). Deep learning for lungs cancer detection: a review. Artificial Intelligence Review. 57(8). 44 indexed citations
13.
14.
Mahmood, Saba, et al.. (2024). Anomaly-based threat detection in smart health using machine learning. BMC Medical Informatics and Decision Making. 24(1). 347–347. 12 indexed citations
15.
Shah, Asghar Ali, et al.. (2024). DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation. BMC Medical Informatics and Decision Making. 24(1). 198–198. 5 indexed citations
16.
Abbas, Tahir, Ali Haider Khan, Ali Daud, et al.. (2024). IoMT-Based Healthcare Systems: A Review. Computer Systems Science and Engineering. 0(0). 1–10. 6 indexed citations
17.
Naeem, Khalid, et al.. (2024). Machine Learning and Deep Learning Optimization Algorithms for Unconstrained Convex Optimization Problem. IEEE Access. 13. 1817–1833. 1 indexed citations
18.
Anoop, V. S., et al.. (2024). Climate Change Sentiment Analysis Using Domain Specific Bidirectional Encoder Representations From Transformers. IEEE Access. 12. 114912–114922. 5 indexed citations
19.
Alharbey, Riad, et al.. (2024). Digital twin technology for enhanced smart grid performance: integrating sustainability, security, and efficiency. Frontiers in Energy Research. 12. 5 indexed citations
20.
Daud, Ali, et al.. (2023). A deep co-evolution architecture for anomaly detection in dynamic networks. Multimedia Tools and Applications. 83(14). 40489–40508. 2 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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