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.
Data imbalance in classification: Experimental evaluation
2019531 citationsFadi Thabtah, Suhel Hammoud et al.Information Sciencesprofile →
Phishing detection based Associative Classification data mining
2014244 citationsNeda Abdelhamid, Aladdin Ayesh et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Fadi Thabtah'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 Fadi Thabtah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fadi Thabtah more than expected).
This network shows the impact of papers produced by Fadi Thabtah. 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 Fadi Thabtah. The network helps show where Fadi Thabtah may publish in the future.
Co-authorship network of co-authors of Fadi Thabtah
This figure shows the co-authorship network connecting the top 25 collaborators of Fadi Thabtah.
A scholar is included among the top collaborators of Fadi Thabtah 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 Fadi Thabtah. Fadi Thabtah is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Thabtah, Fadi, et al.. (2019). Data imbalance in classification: Experimental evaluation. Information Sciences. 513. 429–441.531 indexed citations breakdown →
Thabtah, Fadi, et al.. (2018). An Improved Associative Classification Algorithm based on Incremental Rules. Journal of the Association for Information Systems.1 indexed citations
Lu, Joan, et al.. (2014). Class Strength Prediction Method for Associative Classification. University of Huddersfield Repository (University of Huddersfield). 5–10.5 indexed citations
12.
Mohammad, Rami Mustafa A., Fadi Thabtah, & T.L. McCluskey. (2012). An assessment of features related to phishing websites using an automated technique. Huddersfield Research Portal (University of Huddersfield). 492–497.123 indexed citations
13.
Abdelhamid, Neda, Aladdin Ayesh, & Fadi Thabtah. (2012). An experimental study of three different rule ranking formulas in associative classification. DMU Open Research Archive (De Montfort University). 795–800.6 indexed citations
14.
Alhawari, Samer, et al.. (2010). Improving Performance of Customer Knowledge Expansion with Knowledge Management Process. 2010.2 indexed citations
15.
Abdel-Jaber, Hussein, et al.. (2008). Fuzzy logic controller of Random Early Detection based on average queue length and packet loss rate. International Symposium on Performance Evaluation of Computer and Telecommunication Systems. 428–432.15 indexed citations
16.
Abdel-Jaber, Hussein, et al.. (2007). Modelling BLUE Active Queue Management using Discrete-time Queue.. World Congress on Engineering. 568–573.10 indexed citations
17.
Hadi, Wael, Fadi Thabtah, & Hussein Abdel-Jaber. (2007). A Comparative Study using Vector Space Model with K-Nearest Neighbor on Text Categorization Data. World Congress on Engineering. 2165(1). 296–300.7 indexed citations
18.
Thabtah, Fadi. (2006). Pruning techniques in associative classification: Survey and comparison. Journal of Digital Information Management. 4(3). 197–202.13 indexed citations
19.
Thabtah, Fadi, Peter Cowling, & Yi Peng. (2005). Real performance of categorization-based association rule techniques. University of Huddersfield Repository (University of Huddersfield).1 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.