Mohammad Akbari

1.0k total citations
33 papers, 663 citations indexed

About

Mohammad Akbari is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mohammad Akbari has authored 33 papers receiving a total of 663 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 10 papers in Information Systems and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mohammad Akbari's work include Natural Language Processing Techniques (6 papers), Human Mobility and Location-Based Analysis (5 papers) and Topic Modeling (5 papers). Mohammad Akbari is often cited by papers focused on Natural Language Processing Techniques (6 papers), Human Mobility and Location-Based Analysis (5 papers) and Topic Modeling (5 papers). Mohammad Akbari collaborates with scholars based in Iran, Singapore and United States. Mohammad Akbari's co-authors include Tat‐Seng Chua, Liqiang Nie, Jialie Shen, Mostafa Haghi Kashani, Yiliang Zhao, Aleksandr Farseev, Luming Zhang, Ebrahim Mahdipour, Xuemeng Song and Xia Hu and has published in prestigious journals such as Expert Systems with Applications, Sensors and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Mohammad Akbari

33 papers receiving 620 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mohammad Akbari Iran 14 326 172 140 97 88 33 663
Manos Papagelis Canada 12 250 0.8× 194 1.1× 101 0.7× 132 1.4× 93 1.1× 46 726
Xiaorui Liu United States 13 918 2.8× 216 1.3× 184 1.3× 133 1.4× 69 0.8× 30 1.1k
James G. Shanahan United States 10 599 1.8× 299 1.7× 80 0.6× 84 0.9× 108 1.2× 34 901
Xiangyu Song China 11 538 1.7× 142 0.8× 160 1.1× 52 0.5× 60 0.7× 28 746
Anatole Gershman United States 14 545 1.7× 153 0.9× 108 0.8× 51 0.5× 62 0.7× 48 842
Hsin-Chang Yang Taiwan 15 376 1.2× 218 1.3× 157 1.1× 64 0.7× 31 0.4× 84 672
Kan Li China 19 429 1.3× 270 1.6× 197 1.4× 242 2.5× 115 1.3× 99 984
Giuseppe Rizzo Italy 18 778 2.4× 374 2.2× 113 0.8× 56 0.6× 56 0.6× 64 1.0k
Jiuxin Cao China 19 430 1.3× 412 2.4× 157 1.1× 151 1.6× 224 2.5× 104 940
Mohamed Aly United States 13 659 2.0× 345 2.0× 269 1.9× 90 0.9× 185 2.1× 28 1.1k

Countries citing papers authored by Mohammad Akbari

Since Specialization
Citations

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

Fields of papers citing papers by Mohammad Akbari

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mohammad Akbari

This figure shows the co-authorship network connecting the top 25 collaborators of Mohammad Akbari. A scholar is included among the top collaborators of Mohammad Akbari 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 Mohammad Akbari. Mohammad Akbari 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.
Abkenar, Sepideh Bazzaz, et al.. (2025). Machine learning for Parkinson’s disease: a comprehensive review of datasets, algorithms, and challenges. npj Parkinson s Disease. 11(1). 187–187. 2 indexed citations
2.
Abkenar, Sepideh Bazzaz, Mostafa Haghi Kashani, Mohammad Akbari, & Ebrahim Mahdipour. (2023). Learning textual features for Twitter spam detection: A systematic literature review. Expert Systems with Applications. 228. 120366–120366. 19 indexed citations
3.
Akbari, Mohammad, et al.. (2023). DrugRep-KG: Toward Learning a Unified Latent Space for Drug Repurposing Using Knowledge Graphs. Journal of Chemical Information and Modeling. 63(8). 2532–2545. 14 indexed citations
4.
Taghikhany, Touraj, et al.. (2023). A novel spatiotemporal 3D CNN framework with multi-task learning for efficient structural damage detection. Structural Health Monitoring. 23(4). 2270–2287. 4 indexed citations
5.
Akbari, Mohammad, et al.. (2022). Bagging Supervised Autoencoder Classifier for credit scoring. Expert Systems with Applications. 213. 118991–118991. 37 indexed citations
6.
Kashani, Mostafa Haghi, et al.. (2021). Leveraging big data in smart cities: A systematic review. Concurrency and Computation Practice and Experience. 33(21). 39 indexed citations
7.
Rawassizadeh, Reza, Chelsea Dobbins, Mohammad Akbari, & Michael J. Pazzani. (2019). Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering. Sensors. 19(3). 448–448. 16 indexed citations
8.
Han, Jianglei & Mohammad Akbari. (2018). Vertical Domain Text Classification: Towards Understanding IT Tickets Using Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1). 8 indexed citations
9.
Akbari, Mohammad, et al.. (2018). Socio-spatial Self-organizing Maps. Proceedings of the ACM on Human-Computer Interaction. 2(CSCW). 1–23. 12 indexed citations
10.
Akbari, Mohammad, et al.. (2018). From the User to the Medium: Neural Profiling Across Web Communities. Proceedings of the International AAAI Conference on Web and Social Media. 12(1). 4 indexed citations
11.
Akbari, Mohammad, Xia Hu, Fei Wang, & Tat‐Seng Chua. (2017). Wellness Representation of Users in Social Media: Towards Joint Modelling of Heterogeneity and Temporality. IEEE Transactions on Knowledge and Data Engineering. 29(10). 2360–2373. 10 indexed citations
12.
Akbari, Mohammad, Xia Hu, Liqiang Nie, & Tat‐Seng Chua. (2016). Towards organizing health knowledge on community-based health services. PubMed. 2016(1). 18–18. 1 indexed citations
13.
Akbari, Mohammad, Xia Hu, Liqiang Nie, & Tat‐Seng Chua. (2016). From Tweets to Wellness: Wellness Event Detection from Twitter Streams. Proceedings of the AAAI Conference on Artificial Intelligence. 30(1). 44 indexed citations
14.
Farseev, Aleksandr, Liqiang Nie, Mohammad Akbari, & Tat‐Seng Chua. (2015). Harvesting Multiple Sources for User Profile Learning. 235–242. 62 indexed citations
15.
Nie, Liqiang, et al.. (2014). A Joint Local-Global Approach for Medical Terminology Assignment. International ACM SIGIR Conference on Research and Development in Information Retrieval. 24–27. 32 indexed citations
16.
Nie, Liqiang, Tao Li, Mohammad Akbari, Jialie Shen, & Tat‐Seng Chua. (2014). WenZher. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 1245–1246. 33 indexed citations
17.
Nie, Liqiang, Yiliang Zhao, Mohammad Akbari, Jialie Shen, & Tat‐Seng Chua. (2014). Bridging the Vocabulary Gap between Health Seekers and Healthcare Knowledge. IEEE Transactions on Knowledge and Data Engineering. 27(2). 396–409. 101 indexed citations
18.
19.
Azmi, Reza, et al.. (2010). LGL-DIR: Layout graph for layout based document image retrieval. V4–262. 3 indexed citations
20.
Akbari, Mohammad, et al.. (2010). Vulnerability detector using parse tree annotation. V4–257. 5 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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