All Outputs (13)

Fall prediction using behavioural modelling from sensor data in smart homes. (2019)
Journal Article
FORBES, G., MASSIE, S. and CRAW, S. 2020. Fall prediction using behavioural modelling from sensor data in smart homes. Artificial intelligence review [online], 53(2), pages 1071-1091. Available from: https://doi.org/10.1007/s10462-019-09687-7

The number of methods for identifying potential fall risk is growing as the rate of elderly fallers continues to rise in the UK. Assessments for identifying risk of falling are usually performed in hospitals and other laboratory environments, however... Read More about Fall prediction using behavioural modelling from sensor data in smart homes..

Improving e-learning recommendation by using background knowledge. (2018)
Journal Article
MBIPOM, B., CRAW, S. and MASSIE, S. 2021. Improving e-learning recommendation by using background knowledge. Expert systems [online], 38(7): artificial intelligence/EDMA 2017, article e12265. Available from: https://doi.org/10.1111/exsy.12265

There is currently a large amount of e-Learning resources available to learners on the Web. However, learners often have difficulty finding and retrieving relevant materials to support their learning goals because they lack the domain knowledge to cr... Read More about Improving e-learning recommendation by using background knowledge..

Case-base maintenance with multi-objective evolutionary algorithms. (2015)
Journal Article
LUPIANI, E., MASSIE, S., CRAW, S., JUAREZ, J.M. and PALMA, J. 2016. Case-base maintenance with multi-objective evolutionary algorithms. Journal of intelligent information systems [online], 46(2), pages 259-284. Available from: https://doi.org/10.1007/s10844-015-0378-z

Case-Base Reasoning is a problem-solving methodology that uses old solved problems, called cases, to solve new problems. The case-base is the knowledge source where the cases are stored, and the amount of stored cases is critical to the problem-solvi... Read More about Case-base maintenance with multi-objective evolutionary algorithms..

Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. (2014)
Journal Article
HORSBURGH, B., CRAW, S. and MASSIE, S. 2015. Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. Artificial intelligence [online], 219, pages 25-39. Available from: https://doi.org/10.1016/j.artint.2014.11.004

Online recommender systems are an important tool that people use to find new music. To generate recommendations, many systems rely on tag representations of music. Such systems however suffer from tag sparsity, whereby tracks lack a strong tag repres... Read More about Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems..

Learning adaptation knowledge to improve case-based reasoning. (2006)
Journal Article
CRAW, S., WIRATUNGA, N. and ROWE, R. 2006. Learning adaptation knowledge to improve case-based reasoning. Artificial intelligence, 170(16-17), pages 1175-1192. Available from: https://doi.org/10.1016/j.artint.2006.09.001

Case-Based Reasoning systems retrieve and reuse solutions for previously solved problems that have been encountered and remembered as cases. In some domains, particularly where the problem solving is a classification task, the retrieved solution can... Read More about Learning adaptation knowledge to improve case-based reasoning..

Retrieval, reuse, revision and retention in case-based reasoning. (2005)
Journal Article
DE MANTARAS, R.L., MCSHERRY, D., BRIDGE, D., LEAKE, D., SMYTH, B., CRAW, S., FALTINGS, B., MAHER, M.L., COX, M.T., FORBUS, K., KEANE, M., AAMODT, A. and WATSON, I. 2005. Retrieval, reuse, revision and retention in case-based reasoning. Knowledge engineering review [online], 20(3), pages 215-240. Available from: https://doi.org/10.1017/S0269888906000646

Case-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were so... Read More about Retrieval, reuse, revision and retention in case-based reasoning..

Design, innovation and case-based reasoning. (2005)
Journal Article
GOEL, A. and CRAW, S. 2005. Design, innovation and case-based reasoning. Knowledge engineering review [online], 20(3), pages 271-276. Available from:https://doi.org/10.1017/S0269888906000609

The design task is especially appropriate for applying, integrating, exploring and pushing the boundaries of case-based reasoning. In this paper, we briefly review the challenges that design poses for case-based reasoning and survey research on case-... Read More about Design, innovation and case-based reasoning..

Case-based reasoning for matching SMARTHOUSE technology to people's needs. (2004)
Journal Article
WIRATUNGA, N., CRAW, TAYLOR, B. and DAVIS, G. 2004. Case-based reasoning for matching SMARTHOUSE technology to people's needs. Knowledge-based systems [online], 17 (2-4), pages 139-146. Available from: https://doi.org/10.1016/j.knosys.2004.03.009

SMARTHOUSE technology offers devices that help the elderly and people with disabilities to live independently in their homes. This paper presents our experiences from a pilot project applying case-based reasoning techniques to match the needs of the... Read More about Case-based reasoning for matching SMARTHOUSE technology to people's needs..

Maintaining retrieval knowledge in a case-based reasoning system. (2001)
Journal Article
CRAW, S., JARMULAK, J. and ROWE, R. 2001. Maintaining retrieval knowledge in a case-based reasoning system. Computational intelligence [online], 17(2), pages 346-363. Available from: https://doi.org/10.1111/0824-7935.00149

The knowledge stored in a case base is central to the problem solving of a case-based reasoning (CBR) system. Therefore, case-base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and simi... Read More about Maintaining retrieval knowledge in a case-based reasoning system..

Knowledge modelling for a generic refinement framework. (1999)
Journal Article
BOSWELL, R. and CRAW, S. 1999. Knowledge modelling for a generic refinement framework. Knowledge-based systems [online], 12(5-6), pages 317-325. Available from: https://doi.org/10.1016/S0950-7051(99)00018-0

Refinement tools assist with debugging the knowledge-based system (KBS), thus easing the well-known knowledge acquisition bottleneck, and the more recently recognised maintenance overhead. The existing refinement tools were developed for specific rul... Read More about Knowledge modelling for a generic refinement framework..

Refinement complements verification and validation. (1996)
Journal Article
CRAW, S. 1996. Refinement complements verification and validation. International journal of human computer studies [online], 44(2), pages 245-256. Available from: https://doi.org/10.1006/ijhc.1996.0012

Knowledge based systems are being applied in ever increasing numbers. The development of knowledge acquisition tools has eased the Knowledge Acquisition Bottleneck. More recently there has been a demand for mechanisms to assure the quality of knowled... Read More about Refinement complements verification and validation..

Refinement in response to validation. (1995)
Journal Article
CRAW, S. and SLEEMAN, D. 1995. Refinement in response to validation. Expert systems with applications, 8(3), pages 343-349. Available from: https://doi.org/10.1016/0957-4174(94)E0025-P

Knowledge-based systems (KBSs) are being applied in ever increasing numbers. In parallel with the development of knowledge acquisition tools is the demand for mechanisms to assure their quality, particularly in safety critical applications. Quality a... Read More about Refinement in response to validation..

Consultant-2: pre- and post processing of machine learning applications. (1995)
Journal Article
SLEEMAN, D., RISSAKIS, M., CRAW, S., GRANER, N. and SHARMA, S., 1995. Consultant-2: pre- and post processing of machine learning applications. International journal of human computer studies [online], 43(1), pages 43-63. Available from: https://doi.org/10.1006/ijhc.1995.1035

The knowledge acquisition bottleneck in the development of large knowledge-based applications has not yet been resolved. One approach which has been advocated is the systematic use of Machine Learning (ML) techniques. However, ML technology poses dif... Read More about Consultant-2: pre- and post processing of machine learning applications..