23 research outputs found
PSO based Neural Networks vs. Traditional Statistical Models for Seasonal Time Series Forecasting
Seasonality is a distinctive characteristic which is often observed in many
practical time series. Artificial Neural Networks (ANNs) are a class of
promising models for efficiently recognizing and forecasting seasonal patterns.
In this paper, the Particle Swarm Optimization (PSO) approach is used to
enhance the forecasting strengths of feedforward ANN (FANN) as well as Elman
ANN (EANN) models for seasonal data. Three widely popular versions of the basic
PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are considered here.
The empirical analysis is conducted on three real-world seasonal time series.
Results clearly show that each version of the PSO algorithm achieves notably
better forecasting accuracies than the standard Backpropagation (BP) training
method for both FANN and EANN models. The neural network forecasting results
are also compared with those from the three traditional statistical models,
viz. Seasonal Autoregressive Integrated Moving Average (SARIMA), Holt-Winters
(HW) and Support Vector Machine (SVM). The comparison demonstrates that both
PSO and BP based neural networks outperform SARIMA, HW and SVM models for all
three time series datasets. The forecasting performances of ANNs are further
improved through combining the outputs from the three PSO based models.Comment: 4 figures, 4 tables, 31 references, conference proceeding
Data mining for detecting Bitcoin Ponzi schemes
Soon after its introduction in 2009, Bitcoin has been adopted by
cyber-criminals, which rely on its pseudonymity to implement virtually
untraceable scams. One of the typical scams that operate on Bitcoin are the
so-called Ponzi schemes. These are fraudulent investments which repay users
with the funds invested by new users that join the scheme, and implode when it
is no longer possible to find new investments. Despite being illegal in many
countries, Ponzi schemes are now proliferating on Bitcoin, and they keep
alluring new victims, who are plundered of millions of dollars. We apply data
mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our
starting point is a dataset of features of real-world Ponzi schemes, that we
construct by analysing, on the Bitcoin blockchain, the transactions used to
perform the scams. We use this dataset to experiment with various machine
learning algorithms, and we assess their effectiveness through standard
validation protocols and performance metrics. The best of the classifiers we
have experimented can identify most of the Ponzi schemes in the dataset, with a
low number of false positives
Supporting personalised content management in smart health information portals
Information portals are seen as an appropriate platform for personalised healthcare and wellbeing information provision. Efficient content management is a core capability of a successful smart health information portal (SHIP) and domain expertise is a vital input to content management when it comes to matching user profiles with the appropriate resources. The rate of generation of new health-related content far exceeds the numbers that can be manually examined by domain experts for relevance to a specific topic and audience. In this paper we investigate automated content discovery as a plausible solution to this shortcoming that capitalises on the existing database of expert-endorsed content as an implicit store of knowledge to guide such a solution. We propose a novel content discovery technique based on a text analytics approach that utilises an existing content repository to acquire new and relevant content. We also highlight the contribution of this technique towards realisation of smart content management for SHIPs.<br /
A comprehensive theoretical framework for the optimization of neural networks classification performance with respect to weighted metrics
In many contexts, customized and weighted classification scores are designed
in order to evaluate the goodness of the predictions carried out by neural
networks. However, there exists a discrepancy between the maximization of such
scores and the minimization of the loss function in the training phase. In this
paper, we provide a complete theoretical setting that formalizes weighted
classification metrics and then allows the construction of losses that drive
the model to optimize these metrics of interest. After a detailed theoretical
analysis, we show that our framework includes as particular instances
well-established approaches such as classical cost-sensitive learning, weighted
cross entropy loss functions and value-weighted skill scores
An intelligent content discovery technique for health portal content management
Background: Continuous content management of health information portals is a feature vital for its sustainability and widespread acceptance. Knowledge and experience of a domain expert is essential for content management in the health domain. The rate of generation of online health resources is exponential and thereby manual examination for relevance to a specific topic and audience is a formidable challenge for domain experts. Intelligent content discovery for effective content management is a less researched topic. An existing expert-endorsed content repository can provide the necessary leverage to automatically identify relevant resources and evaluate qualitative metrics.Objective: This paper reports on the design research towards an intelligent technique for automated content discovery and ranking for health information portals. The proposed technique aims to improve efficiency of the current mostly manual process of portal content management by utilising an existing expert-endorsed content repository as a supporting base and a benchmark to evaluate the suitability of new content.Methods: A model for content management was established based on a field study of potential users. The proposed technique is integral to this content management model and executes in several phases (ie, query construction, content search, text analytics and fuzzy multi-criteria ranking). The construction of multi-dimensional search queries with input from Wordnet, the use of multi-word and single-word terms as representative semantics for text analytics and the use of fuzzy multi-criteria ranking for subjective evaluation of quality metrics are original contributions reported in this paper.Results: The feasibility of the proposed technique was examined with experiments conducted on an actual health information portal, the BCKOnline portal. Both intermediary and final results generated by the technique are presented in the paper and these help to establish benefits of the technique and its contribution towards effective content management.Conclusions: The prevalence of large numbers of online health resources is a key obstacle for domain experts involved in content management of health information portals and websites. The proposed technique has proven successful at search and identification of resources and the measurement of their relevance. It can be used to support the domain expert in content management and thereby ensure the health portal is up-to-date and current
Visual attention using spiking neural maps
International audienceVisual attention is a mechanism that biological systems have developed to reduce the large amount of visual information in order to efficiently perform tasks such as learning, recognition, tracking, etc. In this paper we describe a simple spiking neural network model that is able to detect, focus on and track a stimulus even in the presence of noise or distracters. Instead of using a regular rate-coding neuron model based on the continuum neural field theory (CNFT), we propose to use a time-based code by means of a network composed of leaky integrate-and-fire (LIF) neurons. The proposal is experimentally compared against the usual CNFT-based model