Information compression in the context model

The Context Model provides a formal framework for the representation, interpretation, and analysis of vague and uncertain data. The clear semantics of the underlying concepts make it feasible to compare well-known approaches to the modeling of imperfect knowledge like that given in Bayes Theory, Shafer's Evidence Theory, the Transferable Belief Model, and Possibility Theory. In this paper we present the basic ideas of the Context Model and show its applicability as an alternative foundation of Possibility Theory and the epistemic view of fuzzy sets

A rule induction approach to forecasting critical alarms in a telecommunication network

This paper proposes a white box method of predicting critical alarms so they can be mitigated and understood by engineers. Forecasting these alarms will avoid outages and maintain the agreed service level which is beneficial to both the provider of telecommunication services and the consumers. The paper evaluates several item set mining approaches on a set of alarms of the British Telecom (BT) national telecommunication network and proposes a novel transformation of the data to enable the discovery of patterns undetectable by current item set mining approaches. The result is a method for rule induction that predicts alarms with high precision using a wide range of features

A method of rule induction for predicting and describing future alarms in a telecommunication network

In order to gain insights into events and issues that may cause alarms in parts of IP networks, intelligent methods that capture and express causal relationships are needed. Methods that are predictive and descriptive are rare and those that do predict are often limited to using a single feature from a vast data set. This paper follows the progression of a Rule Induction Algorithm that produces rules with strong causal links that are both descriptive and predict events ahead of time. The algorithm is based on an information theoretic approach to extract rules comprising of a conjunction of network events that are significant prior to network alarms. An empirical evaluation of the algorithm is provided

Linagliptin Improves Insulin Sensitivity and Hepatic Steatosis in Diet-Induced Obesity

Linagliptin (tradjenta™) is a selective dipeptidyl peptidase-4 (DPP-4) inhibitor. DPP-4 inhibition attenuates insulin resistance and improves peripheral glucose utilization in humans. However, the effects of chronic DPP-4 inhibition on insulin sensitivity are not known. The effects of long-term treatment (3–4 weeks) with 3 mg/kg/day or 30 mg/kg/day linagliptin on insulin sensitivity and liver fat content were determined in diet-induced obese C57BL/6 mice. Chow-fed animals served as controls. DPP-4 activity was significantly inhibited (67–89%) by linagliptin (P<0.001). Following an oral glucose tolerance test, blood glucose concentrations (measured as area under the curve) were significantly suppressed after treatment with 3 mg/kg/day (–16.5% to –20.3%; P<0.01) or 30 mg/kg/day (–14.5% to –26.4%; P<0.05) linagliptin (both P<0.01). Liver fat content was significantly reduced by linagliptin in a dose-dependent manner (both doses P<0.001). Diet-induced obese mice treated for 4 weeks with 3 mg/kg/day or 30 mg/kg/day linagliptin had significantly improved glycated hemoglobin compared with vehicle (both P<0.001). Significant dose-dependent improvements in glucose disposal rates were observed during the steady state of the euglycemic–hyperinsulinemic clamp: 27.3 mg/kg/minute and 32.2 mg/kg/minute in the 3 mg/kg/day and 30 mg/kg/day linagliptin groups, respectively; compared with 20.9 mg/kg/minute with vehicle (P<0.001). Hepatic glucose production was significantly suppressed during the clamp: 4.7 mg/kg/minute and 2.1 mg/kg/minute in the 3 mg/kg/day and 30 mg/kg/day linagliptin groups, respectively; compared with 12.5 mg/kg/minute with vehicle (P<0.001). In addition, 30 mg/kg/day linagliptin treatment resulted in a significantly reduced number of macrophages infiltrating adipose tissue (P<0.05). Linagliptin treatment also decreased liver expression of PTP1B, SOCS3, SREBP1c, SCD-1 and FAS (P<0.05). Other tissues like muscle, heart and kidney were not significantly affected by the insulin sensitizing effect of linagliptin. Long-term linagliptin treatment reduced liver fat content in animals with diet-induced hepatic steatosis and insulin resistance, and may account for improved insulin sensitivity

Beyond Neuro-Fuzzy: Perspectives And Directions

The interest in neuro--fuzzy systems has grown tremendously over the last few years. First approaches concentrated mainly on neuro--fuzzy controllers, whereas newer approaches can also be found in the domain of data analysis. After successful applications in Japan neuro--fuzzy concepts also find their way into the European industries, though mainly simple models, like FAMs, still prevail. This paper shortly reviews some modern neuro--fuzzy concepts. After this a generic neuro--fuzzy model is presented, that serves a foundation for specific derived neuro--fuzzy applications, this is shown with a model for neuro--fuzzy data analysis, which we see as an important perspective for the neuro--fuzzy domain. The paper concludes with some thoughts on further research directions that go beyond simple neuro--fuzzy control applications. 1 Introduction Neuro--fuzzy systems enjoyed an ever growing popularity over the recent years. The first approaches were mainly neuro--fuzzy controllers, but newer ..

A Fuzzy Perceptron as a Generic Model for Neuro-Fuzzy Approaches

This paper presents a fuzzy perceptron as a generic model of multilayer fuzzy neural networks, or neural fuzzy systems, respectively. This model is suggested to ease the comparision of different neuro--fuzzy approaches that are known from the literature. A fuzzy perceptron is not a fuzzification of a common neural network architecture, and it is not our intention to enhance neural learning algorithms by fuzzy methods. The idea of the fuzzy perceptron is to provide an architecture that can be initialized with prior knowledge, and that can be trained using neural learning methods. The training is carried out in such a way that the learning result is interpretable in the form of linguistic fuzzy if--then rules. Next to the advantage of having a generic model to compare neuro--fuzzy models, the fuzzy perceptron can be specialized e.g. for data analysis and control tasks. 1 Introduction Combinations of neural networks and fuzzy systems have become very popular during the last two years [Be..