Browsing 08 Information and Computing Sciences by Title

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Browsing 08 Information and Computing Sciences by Title

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  • Khosravi, Anas; Lu, Jie (Prokom Softwares, 2006)
  • Lowe, David; Mujkanovic, Amir; Miorandi, Daniel; Yamamoto, Lidia (Springer-Verlag Berlin Heidelberg, 2010)
    In previous work the authors have described an approach for building distributed selfa??healing systems a?? referred to as EmbryoWare a?? that, in analogy to Embryonics in hardware, is inspired by cellular development and ...
  • Tan, Chek Tien; Rosser, Daniel; Bakkes, Ing; Pisan, Yusuf (ACM, 2012)
    Current quantitative methods of measuring player experience in games are mostly intrusive to play and less suited to natural, non-laboratory play environments. This paper presents an initial study to validate the ...
  • Gay, Valerie; Leijdekkers, Peter; Barin, Edwards (IEEE Communications Society, 2010)
    A trial with a cardiac rehabilitation centre is in progress where we test a novel cardiac rehab application using a standard mobile phone and wireless sensors. The goal is to obtain insight how remote monitoring compares ...
  • Tran, Quynh-Nhu Numi; Low, Graham; Williams, Mary-Anne (Springer-Verlag Berlin Heidelberg, 2003)
    This paper proposes a comprehensive and multi-dimensional feature analysis framework for evaluating and comparing methodologies for developing multi-agent systems (MAS). Developed from a synthesis of various existing ...
  • Li, Zelin; Zhang, Jian; Wu, Qiang; Geers, Glen (IEEE Computer Society, 2010)
    Feature enhancement in an image is to reinforce some exacted features so that it can be used for object classification and detection. As the thermal image is lack of texture and colorful information, the techniques for ...
  • Bian, Wei; Li, Jing; Tao, Dacheng (Springer-Verlag Berlin, 2010)
    Mitchell et al. [9] demonstrated that support vector machines (SVM) are effective to classify the cognitive state of a human subject based on fRMI images observed over a single time interval. However, the direct use of ...
  • Otoom, Ahmed; Gunes, Hatice; Piccardi, Massimo (IEEE, 2008)
    We address the problem of abandoned object classification in video surveillance. Our aim is to determine (i) which feature extraction technique proves more useful for accurate object classification in a video surveillance ...
  • Cheng, Jun; Xie, Can; Bian, Wei; Tao, Dacheng (Elsevier, 2012)
    Hand gesture recognition has been intensively applied in various human?computer interaction (HCI) systems. Different hand gesture recognition methods were developed based on particular features, e.g., gesture trajectories ...
  • Sha, T; Song, Mingli; Bu, Jj; Chen, Chih-Yu; Tao, Dacheng (Elsevier Science Bv, 2011)
    3D facial expression recognition has great potential in human computer interaction and intelligent robot systems. In this paper, we propose a two-step approach which combines both the feature selection and the feature ...
  • Kamal, Abu; Zhu, Xingquan; Pandya, Abhijit; Hsu, Sam; Narayanan, Ramaswamy (World Scientific Publ Co Pte Ltd, 2010)
    Feature selection for supervised learning concerns the problem of selecting a number of important features (w.r.t. the class labels) for the purposes of training accurate prediction models. Traditional feature selection ...
  • Mao, Qi; Tsang, Ivor (IEEE, 2013)
    ?Feature selection with specific multivariate performance measures is the key to the success of many applications such as image retrieval and text classification. The existing feature selection methods are usually designed ...
  • Anaissi, Ali Hassan; Kennedy, Paul; Goyal, Madhu (IEEE Computer Society, 2011)
    Gene expression data is a very complex data set characterised by abundant numbers of features but with a low number of observations. However, only a small number of these features are relevant to an outcome of interest. ...
  • Liu, Huawei; Wu, Xindong; Zhang, Shichao (ACM, 2011)
    One of the challenges in data mining is the dimensionality of data, which is often very high and prevalent in many domains, such as text categorization and bio-informatics. The high-dimensionality of data may bring many ...
  • Kamal, Abu; Zhu, Xingquan; Pandya, Abhijit; Hsu, Sam (IEEE Computer Society, 2009)
    Feature selection concerns the problem of selecting a number of important features (w.r.t. the class labels) in order to build accurate prediction models. Traditional feature selection methods, however, fail to take the ...
  • Duong, Tarn; Cowling, Arianna; Koch, Inge; Wand, Matt (Elsevier, 2008)
    Multivariate kernel density estimation provides information about structure in data. Feature significance is a technique for deciding whether features?such as local extrema?are statistically significant. This paper proposes ...
  • Khushaba, Rami; Al-Ani, Ahmed; Al-Jumaily, Adel Ali (Springer, 2009)
    One of the fundamental motivations for feature selection is to overcome the curse of dimensionality. A novel feature selection algorithm is developed in this chapter based on a combination of Differential Evolution (DE) ...
  • Al-Ani, Ahmed; Alsukker, Akram; Khushaba, Rami (Elsevier, 2013)
    Differential evolution has started to attract a lot of attention as a powerful search method and has been successfully applied to a variety of applications including pattern recognition. One of the most important tasks in ...
  • Larssen, Astrid Twenebowa; Robertson, Toni; Edwards, Jenny (ACM, 2007)
  • Li, Ming; Hoang, Doan (Elsevier, 2005)
    Differentiated Service (DiffServ) architecture has been proposed as a scalable QoS architecture for Internet. DiffServ, however, could not control its loads under heavy traffic conditions, and it could not provide strong ...