16,368 research outputs found
Optimising rule-based classification in temporal data
This study optimises manually derived rule-based expert system classification of objects according to changes in their properties over time. One of the key challenges that this study tries to address is how to classify objects that exhibit changes in their behaviour over time, for example how to classify companies’ share price stability over a period of time or how to classify students’ preferences for subjects while they are progressing through school. A specific case the paper considers is the strategy of players in public goods games (as common in economics) across multiple consecutive games. Initial classification starts from expert definitions specifying class allocation for players based on aggregated attributes of the temporal data. Based on these initial classifications, the optimisation process tries to find an improved classifier which produces the best possible compact classes of objects (players) for every time point in the temporal data. The compactness of the classes is measured by a cost function based on internal cluster indices like the Dunn Index, distance measures like Euclidean distance or statistically derived measures like standard deviation. The paper discusses the approach in the context of incorporating changing player strategies in the aforementioned public good games, where common classification approaches so far do not consider such changes in behaviour resulting from learning or in-game experience. By using the proposed process for classifying temporal data and the actual players’ contribution during the games, we aim to produce a more refined classification which in turn may inform the interpretation of public goods game data
Program transformations using temporal logic side conditions
This paper describes an approach to program optimisation based on transformations, where temporal logic is used to specify side conditions, and strategies are created which expand the repertoire of transformations and provide a suitable level of abstraction. We demonstrate the power of this approach by developing a set of optimisations using our transformation language and showing how the transformations can be converted into a form which makes it easier to apply them, while maintaining trust in the resulting optimising steps. The approach is illustrated through a transformational case study where we apply several optimisations to a small program
Training and Scaling Preference Functions for Disambiguation
We present an automatic method for weighting the contributions of preference
functions used in disambiguation. Initial scaling factors are derived as the
solution to a least-squares minimization problem, and improvements are then
made by hill-climbing. The method is applied to disambiguating sentences in the
ATIS (Air Travel Information System) corpus, and the performance of the
resulting scaling factors is compared with hand-tuned factors. We then focus on
one class of preference function, those based on semantic lexical collocations.
Experimental results are presented showing that such functions vary
considerably in selecting correct analyses. In particular we define a function
that performs significantly better than ones based on mutual information and
likelihood ratios of lexical associations.Comment: To appear in Computational Linguistics (probably volume 20, December
94). LaTeX, 21 page
Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes
This research is a survey to determine the career chosen of form four student
in commerce streams. The important aspect of the career chosen has been divided
into three, first is information about career, type of career and factor that most
influence students in choosing a career. The study was conducted at Sekolah
Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was
chosen by using non-random sampling purpose method as respondent. All
information was gather by using questionnaire. Data collected has been analyzed in
form of frequency, percentage and mean. Results are performed in table and graph.
The finding show that information about career have been improved in students
career chosen and mass media is the main factor influencing students in choosing
their career
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
Intelligent systems in manufacturing: current developments and future prospects
Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS
- …