Constantinos S. Hilas

An application of supervised and unsupervised learning approaches to telecommunications fraud detection

This paper investigates the usefulness of applying different learning approaches to a problem of telecommunications fraud detection. Five different user models are compared by means of both supervised and unsupervised learning techniques, namely the multilayer perceptron and the hierarchical agglomerative clustering. One aim of the study is to identify the user model that best identifies fraud cases. The second task is to explore different views of the same problem and see what can be learned form the application of each different technique. All data come from real defrauded user accounts in a telecommunications network. The models are compared in terms of their performances. Each techniques outcome is evaluated with appropriate measures.

An inverse scattering approach based on the differential E-formulation

An inverse scattering technique based on the differential E-formulation in the frequency domain is proposed. The inversion is achieved by minimizing a cost functional, taking into account the discrepancy between measured and estimated field values, while the Helmholtz wave equation is set as constraint. The Fre/spl acute/chet derivatives of the cost functional with respect to the scatterer properties are derived analytically by means of the calculus of variations. Edge elements are used for the numerical treatment of the problem.

Brief paper: A computational intelligence-based forecasting system for telecommunications time series

In this work a computational intelligence-based approach is proposed for forecasting outgoing telephone calls in a University Campus. A modified Takagi-Sugeno-Kang fuzzy neural system is presented, where the consequent parts of the fuzzy rules are neural networks with an internal recurrence, thus introducing the dynamics to the overall system. The proposed model, entitled Locally Recurrent Neurofuzzy Forecasting System (LR-NFFS), is compared to well-established forecasting models, where its particular characteristics are highlighted.

A comparative study of common and self-adaptive differential evolution strategies on numerical benchmark problems

Abstract Differential Evolution (DE) is a population-based stochastic global optimization technique that requires the adjustment of a very few parameters in order to produce results. However, the control parameters involved in DE are highly dependent on the optimization problem; in practice, their fine-tuning is not always an easy task. The self-adaptive differential evolution (SADE) variants are those that do not require the pre-specified choice of control parameters. On the contrary, control parameters are selfadapted by using the previous learning experience. In this paper, we discuss and evaluate popular common and self-adaptive differential evolution (DE) algorithms. In particular, we present an empirical comparison between two self-adaptive DE variants and common DE methods. In order to assure a fair comparison, we test the methods by using a number of well-known unimodal and multimodal, separable and non-separable, benchmark optimization problems for different dimensions and population size. The results show that SADE variants outperform, or at least produce similar results, to common differential evolution algorithms in terms of solution accuracy and convergence speed. The advantage of using the self-adaptive methods is that the user does not need to adjust control parameters. Therefore, the total computational effort is significantly reduced.

A comparative study of Particle Swarm Optimization and Differential Evolution on Radar Absorbing Materials design for EMC applications

Radar Absorbing Materials (RAM) design for a desired frequency and angle range is presented. We evaluate the performance of Particle Swarm Optimization (PSO) and Differential Evolution (DE) regarding their applicability to absorber design. The results show that the DE algorithm outerperforms PSO variants.

A block-diagonal recurrent fuzzy neural network for system identification

A recurrent fuzzy neural network with internal feedback is suggested in this paper. The network is entitled dynamic block-diagonal fuzzy neural network (DBD-FNN), and constitutes a generalized Takagi-Sugeno-Kang fuzzy system, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks. The proposed model is applied to a benchmark identification problem, where a dynamic system is to be identified. Additionally, an application of the proposed model to the problem of the analysis of lung sounds is presented. Particularly, a filter based on the DBD-FNN is developed, trained with the RENNCOM method. Extensive experimental and simulation results are given and performance comparisons with a series of other models are conducted, highlighting the modeling characteristics of DBD-FNN as an identification tool and the effectiveness of the proposed separation filter.

Forecasting of telecommunications time-series via an Orthogonal Least Squares-based fuzzy model

An application of fuzzy modeling to the problem of telecommunications data prediction is proposed in this paper. The model building process is a two-stage sequential algorithm, based on the Orthogonal Least Squares (OLS) technique. Particularly, the OLS is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, a second orthogonal estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. Input selection is automatically performed, given a large input candidate set. Real world telecommunications data are used in order to highlight the characteristics of the proposed forecaster and to provide a comparative analysis with well-established forecasting models.

ReNFFor: a recurrent neurofuzzy forecaster for telecommunications data

In this work, a dynamic neurofuzzy system for forecasting outgoing telephone calls in a University Campus is proposed. The system comprises modified Takagi–Sugeno–Kang fuzzy rules, where the rules’ consequent parts are small neural networks with unit internal recurrence. The characteristics of the proposed forecaster, which is entitled recurrent neurofuzzy forecaster, are depicted via a comparative analysis with a series of well-known forecasting models.

Change Point Detection in Time Series Using Higher-Order Statistics: A Heuristic Approach

Changes in the level of a time series are usually attributed to an intervention that affects its temporal evolution. The resulting time series are referred to as interrupted time series and may be used to identify the events that caused the intervention and to quantify their impact. In the present paper, a heuristic method for level change detection in time series is presented. The method uses higher-order statistics, namely, the skewness and the kurtosis, and can identify both the existence of a change in the level of the time series and the time instance when it has happened. The technique is straightforwardly applicable to the detection of outliers in time series and promises to have several applications. The method is tested with both simulated and real-world data and is compared to other popular change detection techniques.

X-EDCA: A cross-layer MAC-centric mechanism for efficient multimedia transmission in congested IEEE 802.11e infrastructure networks

IEEE 802.11e is the indisputable standard for supporting multimedia traffic in modern Wireless Local Area Networks. However, it has been proven incapable of handling efficiently multimedia flows in congested networks. The main reason for this suboptimal behavior roots from the static nature of resource allocation specified in IEEE 802.11e. Dynamic tuning of the MAC parameters has demonstrated a beneficial performance in terms of application efficiency. Nevertheless, it is of great importance that this adaptation takes place by considering the multimedia traffic characteristics. In this paper, a cross-layer MAC-centric mechanism is introduced, under the name X-EDCA. The mechanism is designed to cope with high load situations in IEEE 802.11e wireless infrastructure networks by selectively prioritizing and protecting sensitive multimedia frames. The proposed scheme is evaluated and its effectiveness is proven by means of simulation.