Mehdi Assefi

731 total citations
7 papers, 88 citations indexed

About

Mehdi Assefi is a scholar working on Artificial Intelligence, Molecular Biology and Information Systems. According to data from OpenAlex, Mehdi Assefi has authored 7 papers receiving a total of 88 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 2 papers in Molecular Biology and 2 papers in Information Systems. Recurrent topics in Mehdi Assefi's work include Web Data Mining and Analysis (2 papers), Software Engineering Research (2 papers) and Biomedical Text Mining and Ontologies (2 papers). Mehdi Assefi is often cited by papers focused on Web Data Mining and Analysis (2 papers), Software Engineering Research (2 papers) and Biomedical Text Mining and Ontologies (2 papers). Mehdi Assefi collaborates with scholars based in United States and Japan. Mehdi Assefi's co-authors include Ahmad P. Tafti, Ratnesh Sharma, Hossein Hosseini, Hamid R. Arabnia, David Page, John Mayer, AnHai Doan, Eric LaRose, Peggy Peissig and Mary R. Galinski and has published in prestigious journals such as .

In The Last Decade

Mehdi Assefi

6 papers receiving 85 citations

Peers

Mehdi Assefi
Mehdi Assefi
Citations per year, relative to Mehdi Assefi Mehdi Assefi (= 1×) peers Güneş Aluç

Countries citing papers authored by Mehdi Assefi

Since Specialization
Citations

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

Fields of papers citing papers by Mehdi Assefi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mehdi Assefi

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

All Works

7 of 7 papers shown
1.
Assefi, Mehdi, et al.. (2022). Word Embedding Neural Networks to Advance Knee Osteoarthritis Research. 289–292. 2 indexed citations
2.
Assefi, Mehdi, Mehdi Bahrami, Thiab R. Taha, et al.. (2022). An Intelligent Data-Centric Web Crawler Service for API Corpus Construction at Scale. 385–390. 1 indexed citations
3.
Bahrami, Mehdi, et al.. (2020). Deep SAS: A Deep Signature-based API Specification Learning Approach. 12. 1994–2001. 1 indexed citations
4.
Assefi, Mehdi, et al.. (2018). Battery Degradation Temporal Modeling Using LSTM Networks. 33. 853–858. 6 indexed citations
5.
Trippe, Elizabeth D., Mustafa V. Nural, Mehdi Assefi, et al.. (2017). A Vision for Health Informatics: Introducing the SKED Framework. 1 indexed citations
6.
Assefi, Mehdi, et al.. (2017). Big data machine learning using apache spark MLlib. 3492–3498. 64 indexed citations
7.
Tafti, Ahmad P., Mehdi Assefi, Eric LaRose, et al.. (2017). bigNN: An open-source big data toolkit focused on biomedical sentence classification. 3888–3896. 13 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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