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Τεκμήριο An application of supervised and unsupervised learning approaches to telecommunications fraud detection(2008-10) Hilas, Constantinos S.; Mastorocostas, Paris A.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 technique’s outcome is evaluated with appropriate measures.Τεκμήριο A block-diagonal recurrent fuzzy neural network for system identification(2009-10) Mastorocostas, Paris A.; Hilas, Constantinos S.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.Τεκμήριο A block-diagonal recurrent fuzzy neural network for system identification(2009-10-01) Mastorocostas, Paris A.; Hilas, Constantinos S.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.Τεκμήριο Clustering of telecommunications user profiles for fraud detection and security enhancement in large corporate networks: a case study(2015-07-01) Hilas, Constantinos S.; Mastorocostas, Paris A.; Rekanos, Ioannis T.A user’s transactions with modern networks and services produce a vast amount of user related data. The byproduct of every phone call a person makes or every web page one visits is translated into a log record with usage data. By studying these log records, the user’s behavior is revealed and one may come up with clues about user preferences, identify security issues, or discover fraudulent use of the network or the service one provides. Thus, the modeling of network users’ behavior may serve as an invaluable tool for the IT manager. In this paper, many of these issues are discussed and emphasis is given on the construction of appropriate user behavior representation in telecommunications. As an example, the application of two clustering techniques is presented, with the task to identify appropriate user behavior representations (profiles) inside a large organization’s telecommunications network, in order to spot fraudulent usage. Through this study a researcher and/or the organization’s network manager may gain more insight into the problems of user profiling and fraud detection.Τεκμήριο A dynamic fuzzy neural filter for separation of discontinuous adventitious sounds from vesicular sounds(2007-01-01) Mastorocostas, Paris A.; Theocharis, John B.This paper presents a recurrent filter that performs real-time separation of discontinuous adventitious sounds from vesicular sounds. The filter uses two Dynamic Fuzzy Neural Networks, operating in parallel, to perform the task of separation of the lung sounds, obtained from patients with pulmonary pathology. Extensive experimental results, including fine/coarse crackles and squawks, are given, and a performance comparison with a series of other models is conducted, underlining the separation capabilities of the proposed filter and its improved performance with respect to its competing rivals.Τεκμήριο A genetic programming approach to telecommunications fraud detection and classification(2014-03) Hilas, Constantinos S.; Kazarlis, Spyridon A.; Rekanos, Ioannis T.; Mastorocostas, Paris A.Telecommunications fraud has drawn the researchers’ attention due to the huge economic burden on companies and to the interesting aspect of users’ behavior modeling. In the present paper, an application of genetic programming to fraud detection is presented. Genetic programming is used for case classification in order to distinguish between normal and fraudulent activities in a telecommunications network. Implications to appropriate user behavior modeling are, also, discussed. Real world cases of defrauded user accounts are modeled by means of selected usage features and comparisons with other approaches are made.Τεκμήριο A Locally Recurrent Globally Feed-Forward Fuzzy Neural Network for Processing Lung Sounds(2007) Mastorocostas, Paris A.; Varsamis, Dimitris N.; Mastorocostas, Costas A.; Hilas, Costas S.This paper presents a locally recurrent globally feedforward fuzzy neural network, with internal feedback, that performs the task of separation of lung sounds, obtained from patients with pulmonary pathology. The filter is a novel generalized Takagi-Sugeno-Kang fuzzy model, where the consequent parts of the fuzzy rules are Block-Diagonal Recurrent Neural Networks. Extensive experimental results, regarding the lung sound category of squawks, are given, and a performance comparison with a series of other fuzzy and neural filters is conducted, underlining the separation capabilities of the proposed filter.Τεκμήριο A pipelined recurrent fuzzy model for real-time analysis of lung sounds(2008-12) Mastorocostas, Paris A.; Stavrakoudis, Dimitris; Theoharis, JohnThis paper presents a recurrent fuzzy-neural filter that performs the task of separation of lung sounds, obtained from patients with pulmonary pathology. The filter is a pipelined Takagi–Sugeno–Kang recurrent fuzzy network, consisting of a number of modules interconnected in a cascaded form. The participating modules are implemented through recurrent fuzzy neural networks with internal dynamics. The structure of the modules is evolved sequentially from input–output data. Extensive experimental results, regarding the lung sound category of crackles, are given, and a performance comparison with a series of other fuzzy and neural filters is conducted, underlining the separation capabilities of the proposed filter.Τεκμήριο A Recurrent Neural Filter for Adaptive Noise Cancellation(2006) Mastorocostas, Paris A.; Varsamis, Dimitris N.; Mastorocostas, Constantinos A.; Hilas, Constantinos S.This paper presents a dynamic neural filter for adaptive noise cancellation. The cancellation task is transformed to a system-identification problem, which is tackled by use of the Block-Diagonal Recurrent Neural Network. The filter is applied to a benchmark noise cancellation problem, where a comparative analysis with a series of other dynamic models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.Τεκμήριο ReNFFor: a recurrent neurofuzzy forecaster for telecommunications data(2012-01-21) Mastorocostas, Paris A.; Hilas, Constantinos S.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.Τεκμήριο SCOLS-FuM: A Hybrid Fuzzy Modeling Method for Telecommunications Time-Series Forecasting(2014) Mastorocostas, Paris A.; Hilas, Constantinos S.An application of fuzzy modeling to the problem of telecommunications time-series prediction is proposed in this paper. The model building process is a two-stage sequential algorithm, based on Subtractive Clustering (SC) and the Orthogonal Least Squares (OLS) techniques. Particularly, the SC is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, an orthogonal estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. A comparative analysis with well-established forecasting models is conducted on real world telecommunications data, where the characteristics of the proposed forecaster are highlighted.Τεκμήριο Telecommunications data forecasting based on a dynamic neuro-fuzzy network(2011-05) Mastorocostas, Paris A.; Hilas, Constantinos S.In this work a dynamic neuro-fuzzy network (DyNF-Net) is proposed, which is applied on the outgoing telephone traffic of a large organization. It is a modified Takagi-Sugeno-Kang fuzzy neural network, where the consequent parts of the fuzzy rules are neural networks with internal recurrence, thus introducing dynamics to the overall system. Real world telecommunications data are used in order to compare the DyNF-Net to well-established forecasting models. The comparison highlights the particular characteristics of the proposed neuro-fuzzy network.