Now showing items 1-6 of 6

  • Emotion-corpus guided lexicons for sentiment analysis on Twitter. 

    Bandhakavi, Anil; Wiratunga, Nirmalie; Massie, Stewart; Padmanabhan, Deepak (Springer https://doi.org/10.1007/978-3-319-47175-4_5, 2016-11-05)
    BANDHAKAVI, A., WIRATUNGA, N. and MASSIE, S. 2016. Emotion-corpus guided lexicons for sentiment analysis on Twitter. In Bramer, M. and Petridis, M. (eds.) 2016. Research and development in intelligent systems XXXIII: incorporating applications and innovations in intelligent systems XXIV: proceedings of the 36th SGAI nternational conference on innovative techniques and applications of artificial intelligence (AI-2016), 13-15 December 2016, Cambridge, UK. Cham, Switzerland: Springer [online], pages 71-86. Available from: https://doi.org/10.10007/978-3-319-47175-4_5
    Research in Psychology have proposed frameworks that map emotion concepts with sentiment concepts. In this paper we study this mapping from a computational modelling perspective with a view to establish the role of an ...
  • A fine-grained random forests using class decomposition: an application to medical diagnosis. 

    Elyan, Eyad; Gaber, Mohamed Medhat (Springer http://dx.doi.org/10.1007/s00521-015-2064-z, 2015-09-22)
    ELYAN, E. and GABER, M.M. 2015. A fine-grained random forests using class decomposition: an application to medical diagnosis. Neural computing and applications [online], FirstOnline. Available from: http://dx.doi.org/10.1007/s00521-015-2064-z
    Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits ...
  • A framework for unsupervised change detection in activity recognition. 

    Bashir, Sulaimon A.; Petrovski, Andrei; Doolan, Daniel C. (Emerald https://doi.org/10.1108/IJPCC-03-2017-0027, 2017-06-05)
    BASHIR, S.A., PETROVSKI, A. and DOOLAN, D. 2017. A framework for unsupervised change detection in activity recognition. International journal of pervasive computing and communications [online], 13(2), pages 157-175. Available from: https://doi.org/10.1108/IJPCC-03-2017-0027
    Purpose - This purpose of this paper is to develop a change detection technique for activity recognition model. The approach aims to detect changes in the initial accuracy of the model after training and when the model is ...
  • Improving kNN for human activity recognition with privileged learning using translation models. 

    Wijekoon, Anjana; Wiratunga, Nirmalie; Sani, Sadiq; Massie, Stewart; Cooper, Kay (Springer, 2018-07-09)
    WIJEKOON, A., WIRATUNGA, N., SANI, S., MASSIE, S. and COOPER, K. [2018]. Improving kNN for human activity recognition with privileged learning using translation models. Lecture notes in computer science [online], Proceedings of the 26th international conference on case-based reasoning 2018 (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Cham: Springer [online], (accepted).
    Multiple sensor modalities provide more accurate Human Activity Recognition (HAR) compared to using a single modality, yet the latter is preferred by consumers as it is more convenient and less intrusive. This presents a ...
  • Inferential measurements for situation awareness: enhancing traffic surveillance by machine learning. 

    Rattadilok, Prapa; Petrovski, Andrei (IEEE http://dx.doi.org/10.1109/CIVEMSA.2013.6617402, 2013-07)
    RATTADILOK, P. and PETROVSKI, A., 2013. Inferential measurements for situation awareness: enhancing traffic surveillance by machine learning. In: Proceedings of the 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). 15-17 July 2013. Piscataway, NJ: IEEE. Pp. 93 – 98.
    The paper proposes a generic approach to building inferential measurement systems. The large amount of data needed to be acquired and processed by such systems necessitates the use of machine learning techniques. In this ...
  • Learning adaptation knowledge to improve case-based reasoning 

    Craw, Susan; Wiratunga, Nirmalie; Rowe, Ray (Elsevier http://dx.doi.org/10.1016/j.artint.2006.09.001, 2006-11)
    CRAW, S., WIRATUNGA, N. and ROWE, R., 2006. Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence, 170 (16-17), pp. 1175-1192.
    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, ...