Susan Craw

3.7k citations
69 papers · 1.9k indexed · h-index 21

Impact in

Papers in

Susan Craw

66 papers receiving 1.7k citations

Peers

Susan Craw
Comparison fields: 5 of 140
  • Computer Vision and Pattern Recognition 534
  • Cognitive Neuroscience 478
  • Artificial Intelligence 544
  • Experimental and Cognitive Psychology 173
  • Signal Processing 124
Replace David Powers with:
David Powers Australia
Hongan Wang China
Amanda Sharkey United Kingdom
Aaron Quigley United Kingdom
Gustavo Ramírez-González Colombia
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Imad H. Elhajj Lebanon
Lyn Bartram Canada
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Citations per field
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Citations per year

Countries citing papers authored by Susan Craw

Since Specialization
Citations

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

Fields of papers citing papers by Susan Craw

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Susan Craw, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Susan Craw Line = papers co-authored together Susan Craw links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1
Monitoring health in smart homes using simple sensors.
20184
2
Music recommenders: user evaluation without real users?
20153
3 20155
4 201420
5 20128
6 20115
7
Informed case base maintenance: a complexity profiling approach
20078
8 200666
9
Complexity-guided case discovery for case based reasoning
200510
10 2005340
11
Using case-base data to learn adaptation knowledge for design
200122
12
Experiences with a Generic Refinement Toolkit (Short Paper)
20002
13
Representing problem-solving for knowledge refinement
19993
14 1999157
15
Genetic algorithms for feature selection and weighting
199917
16
Applying Genetic Algorithms to Land Use Planning.
199914
17 19971
18
Automating the refinement of knowledge based systems
199217
19
Judging knowledge-base quality
19914
20 199121

About Susan Craw

Susan Craw is a scholar working on Software, Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems and Signal Processing, having authored 69 papers that have together received 1.9k indexed citations. Recurring topics across this work include AI-based Problem Solving and Planning (21 papers), Face recognition and analysis (9 papers), Semantic Web and Ontologies (7 papers), Software Engineering Research (6 papers), Face Recognition and Perception (6 papers), Machine Learning and Data Classification (6 papers), Face and Expression Recognition (5 papers) and Context-Aware Activity Recognition Systems (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (534 citations), Cognitive Neuroscience (478 citations), Artificial Intelligence (544 citations), Experimental and Cognitive Psychology (173 citations) and Signal Processing (124 citations). Susan Craw has collaborated with scholars based in United Kingdom, New Zealand and United States. Frequent co-authors include Nicholas Costen, D. M. Parker, Keith Matthews, A. R. Sibbald, J.R. Lishman, Stewart Massie, Nirmalie Wiratunga, R.C. Rowe, Agnar Aamodt and Ramón López de Mántaras. Their work appears in journals such as The Knowledge Engineering Review, Knowledge-Based Systems, Artificial Intelligence, International Journal of Human-Computer Studies and Image and Vision Computing.

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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