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.
Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm
2014482 citationsEl‐Sayed A. El‐Dahshan, Kenneth Revett et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Kenneth Revett
Since
Specialization
Citations
This map shows the geographic impact of Kenneth Revett'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 Kenneth Revett with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kenneth Revett more than expected).
This network shows the impact of papers produced by Kenneth Revett. 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 Kenneth Revett. The network helps show where Kenneth Revett may publish in the future.
Co-authorship network of co-authors of Kenneth Revett
This figure shows the co-authorship network connecting the top 25 collaborators of Kenneth Revett.
A scholar is included among the top collaborators of Kenneth Revett 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 Kenneth Revett. Kenneth Revett 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.
Magalhães, Sérgio Tenreiro de, et al.. (2017). Internet of Things for the Hotel Industry: A Review. Repositório Institucional da Universidade Católica Portuguesa (Universidade Católica Portuguesa).3 indexed citations
Salem, Abdel Halim, et al.. (2012). A survey of EEG based user authentication schemes.32 indexed citations
4.
Revett, Kenneth, et al.. (2012). On the use of SPECT imaging datasets for automated classification of ventricular heart disease.5 indexed citations
5.
Tantawi, Manal, Kenneth Revett, Mohamed F. Tolba, & A.M. Salem. (2012). On the use of the electrocardiogram for biometrie authentication.5 indexed citations
Revett, Kenneth, et al.. (2011). On the applicability of heart rate for affective gaming. Annual Conference on Computers. 267–272.1 indexed citations
9.
Salama, Mostafa A., Nashwa El-Bendary, Aboul Ella Hassanien, Kenneth Revett, & Aly A. Fahmy. (2011). Interval-based attribute evaluation algorithm. Federated Conference on Computer Science and Information Systems. 153–156.3 indexed citations
10.
Revett, Kenneth, et al.. (2011). Affective gaming: a GSR based approach. Annual Conference on Computers. 262–266.2 indexed citations
Revett, Kenneth, Florin Gorunescu, Abdel-Badeeh M. Salem, & El‐Sayed A. El‐Dahshan. (2009). Evaluation of the Feature Space of an Erythematosquamous Dataset Using Rough Sets. Annals of the University of Craiova Mathematics and Computer Science Series. 36(2). 123–130.9 indexed citations
Revett, Kenneth, et al.. (2005). An automated anomaly EEG detection algorithm using discrete wavelet transforms. WestminsterResearch (University of Westminster). 2679–2685.
Berlin, E., S. J. Bhathena, P.G. Kliman, & Kenneth Revett. (1989). Effect of saturation of dietary lipids on insulin receptors and membrane fluidity in rabbit erythrocytes. Nutrition reports international. 39(2). 367–381.12 indexed citations
20.
Reiser, Sheldon, et al.. (1988). Increased insulin receptors in carbohydrate-sensitive subjects: a mechanism for hyperlipaemia in these subjects?. PubMed. 42(6). 465–72.3 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.