Ahmed Abbasi

416 total citations
9 papers, 215 citations indexed

About

Ahmed Abbasi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Ahmed Abbasi has authored 9 papers receiving a total of 215 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Information Systems. Recurrent topics in Ahmed Abbasi's work include Sentiment Analysis and Opinion Mining (3 papers), Music and Audio Processing (2 papers) and Topic Modeling (2 papers). Ahmed Abbasi is often cited by papers focused on Sentiment Analysis and Opinion Mining (3 papers), Music and Audio Processing (2 papers) and Topic Modeling (2 papers). Ahmed Abbasi collaborates with scholars based in Pakistan, United Arab Emirates and Slovakia. Ahmed Abbasi's co-authors include Abdul Rehman Javed, Zunera Jalil, Farkhund Iqbal, Natalia Kryvinska, Muhammad Badruddin Khan, Thippa Reddy Gadekallu, Mozaherul Hoque Abul Hasanat, Khalid Mahmood Malik, Abdul Khader Jilani Saudagar and Usman Tariq and has published in prestigious journals such as Scientific Reports, IEEE Access and Frontiers in Public Health.

In The Last Decade

Ahmed Abbasi

8 papers receiving 201 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ahmed Abbasi Pakistan	7	129	47	42	41	33	9	215
Hui Yin China	8	139 1.1×	45 1.0×	25 0.6×	52 1.3×	67 2.0×	19	257
Piyush Vyas United States	8	66 0.5×	39 0.8×	20 0.5×	41 1.0×	27 0.8×	33	199
Maria Chiara Caschera Italy	8	112 0.9×	59 1.3×	32 0.8×	68 1.7×	22 0.7×	21	205
Geetanjali Garg India	6	251 1.9×	70 1.5×	24 0.6×	34 0.8×	20 0.6×	7	324
Michal Shmueli-Scheuer Israel	10	158 1.2×	52 1.1×	26 0.6×	22 0.5×	33 1.0×	32	247
Pengzhou Zhang China	9	151 1.2×	53 1.1×	46 1.1×	14 0.3×	22 0.7×	53	236
Aasish Pappu United States	8	168 1.3×	44 0.9×	19 0.5×	20 0.5×	17 0.5×	29	217
Kazutaka Shimada Japan	9	150 1.2×	48 1.0×	42 1.0×	33 0.8×	11 0.3×	50	216
Fabian Bohnert Australia	7	157 1.2×	92 2.0×	47 1.1×	19 0.5×	18 0.5×	16	243
Shah Rukh Humayoun Germany	8	47 0.4×	67 1.4×	75 1.8×	24 0.6×	18 0.5×	54	183

Countries citing papers authored by Ahmed Abbasi

Since Specialization

Citations

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

Fields of papers citing papers by Ahmed Abbasi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ahmed Abbasi

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

All Works

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9 of 9 papers shown

1.

Iqbal, Farkhund, et al.. (2025). Real-time active-learning method for audio-based anomalous event identification and rare events classification for audio events detection. Frontiers in Computer Science. 7.

2.

Iqbal, Farkhund, Ahmed Abbasi, Abdul Rehman Javed, et al.. (2023). Data Augmentation-based Novel Deep Learning Method for Deepfaked Images Detection. ACM Transactions on Multimedia Computing Communications and Applications. 20(11). 1–15. 27 indexed citations

3.

Jalil, Zunera, Ahmed Abbasi, Abdul Rehman Javed, et al.. (2022). COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques. Frontiers in Public Health. 9. 812735–812735. 45 indexed citations

4.

Abbasi, Ahmed, Abdul Rehman Javed, Farkhund Iqbal, Natalia Kryvinska, & Zunera Jalil. (2022). Deep learning for religious and continent-based toxic content detection and classification. Scientific Reports. 12(1). 17478–17478. 27 indexed citations

5.

Abbasi, Ahmed, Abdul Rehman Javed, Farkhund Iqbal, et al.. (2022). Authorship identification using ensemble learning. Scientific Reports. 12(1). 9537–9537. 33 indexed citations

6.

Abbasi, Ahmed, Abdul Rehman Javed, Amanullah Yasin, et al.. (2022). A Large-Scale Benchmark Dataset for Anomaly Detection and Rare Event Classification for Audio Forensics. IEEE Access. 10. 38885–38894. 28 indexed citations

7.

Iqbal, Farkhund, Rabia Batool, Benjamin C. M. Fung, et al.. (2021). Toward Tweet-Mining Framework for Extracting Terrorist Attack-Related Information and Reporting. IEEE Access. 9. 115535–115547. 15 indexed citations

8.

Abbasi, Ahmed. (2010). Intelligent Feature Selection for Opinion Classification.. IEEE Intelligent Systems. 25(1). 75–79. 37 indexed citations

9.

Fu, Tianjun, Ahmed Abbasi, & Hsinchun Chen. (2007). Interaction Coherence Analysis for Dark Web Forums. 17. 342–349. 3 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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