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.
Survey and critique of techniques for extracting rules from trained artificial neural networks
1995745 citationsRobert Andrews, Joachim Diederich et al.Knowledge-Based Systemsprofile →
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 Alan Tickle'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 Alan Tickle with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alan Tickle more than expected).
This network shows the impact of papers produced by Alan Tickle. 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 Alan Tickle. The network helps show where Alan Tickle may publish in the future.
Co-authorship network of co-authors of Alan Tickle
This figure shows the co-authorship network connecting the top 25 collaborators of Alan Tickle.
A scholar is included among the top collaborators of Alan Tickle 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 Alan Tickle. Alan Tickle is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tickle, Alan, et al.. (2005). Animation of Complex Data Communications Concepts May Not Always Yield improved Learning Outcomes. QUT ePrints (Queensland University of Technology). 151–154.4 indexed citations
6.
Stark, Karen, et al.. (2005). Using a network simulation tool to engage students in active learning enhances their understanding of complex data communications concepts. Australasian Computing Education Conference. 223–228.24 indexed citations
7.
Bruce, Christine, et al.. (2005). Diagnostic Learning: Using Web-Based Self Diagnostic Tools for Learning Abstract Concepts in Data Network Education. QUT ePrints (Queensland University of Technology).1 indexed citations
Andrews, Robert, Joachim Diederich, & Alan Tickle. (1995). Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems. 8(6). 373–389.745 indexed citations breakdown →
13.
Anderson, Alison, Dennis Longley, & Alan Tickle. (1993). The Risk Data Repository: A Novel Approach to Security Risk Modelling. 185–194.5 indexed citations
14.
Caelli, William, Dennis Longley, & Alan Tickle. (1992). A Methodology for Describing Information and Physical Security Architectures. 277–296.11 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.