Wyniki wyszukiwania - BazTech

1

An algorithm for arbitrary-order cumulant tensor calculation in a sliding window of data streams

Domino Krzysztof, Gawron Piotr

International Journal of Applied Mathematics and Computer Science

|

2019

|

Vol. 29, no. 1

195--206

EN

High-order cumulant tensors carry information about statistics of non-normally distributed multivariate data. In this work we present a new efficient algorithm for calculation of cumulants of arbitrary orders in a sliding window for data streams. We show that this algorithm offers substantial speedups of cumulant updates compared with the current solutions. The proposed algorithm can be used for processing on-line high-frequency multivariate data and can find applications, e.g., in on-line signal filtering and classification of data streams. To present an application of this algorithm, we propose an estimator of non-Gaussianity of a data stream based on the norms of high order cumulant tensors. We show how to detect the transition from Gaussian distributed data to non-Gaussian ones in a data stream. In order to achieve high implementation efficiency of operations on super-symmetric tensors, such as cumulant tensors, we employ a block structure to store and calculate only one hyper-pyramid part of such tensors.

2

A hybrid cascade neuro-fuzzy network with pools of extended neo-fuzzy neurons and its deep learning

Bodyanskiy Yevgeniy V., Tyshchenko Oleksii K.

International Journal of Applied Mathematics and Computer Science

|

2019

|

Vol. 29, no. 3

477--488

EN

This research contribution instantiates a framework of a hybrid cascade neural network based on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron.

3

A framework for network intrusion detection using network programmability and data stream clustering machine learning algorithms

de Ribamar Lima Ribeiro Admilson, Moreno Ordonez Edward David, Alves Nascimento Anderson Clayton

Annals of Computer Science and Information Systems

|

2019

|

Vol. 20

57--63

EN

Several operational security mechanisms have been developed to mitigate malicious activity in the Internet. However, the most these mechanisms require a signature basis and present the inability to predict new malicious activity. Other anomaly-based mechanisms are inefficient due to the possibility of an attacker simulates legitimate traffic, which causes many false alarms. Thus, to overcome that problem, in this paper we present an anomaly-based framework that uses network programmability and machine learning algorithms over continuous data stream. Our approach overcomes the main challenges that occur when develop an anomaly-based system using machine learning techniques. We have done an experimental evaluation to demonstrate the feasibility of the proposed framework. In the experiments, we use a DDoS attack as network intrusion and we show that the technique attains an Accuracy of 98.98%, a Recall of 60%, a Precision of 60% and an FPR of 0.48% for 1% DDoS attack on the real normal traffic. This shows the effectiveness of our technique.

4

Regression function and noise variance tracking methods for data streams with concept drift

Jaworski M.

International Journal of Applied Mathematics and Computer Science

|

2018

|

Vol. 28, no. 3

559--567

EN

Two types of heuristic estimators based on Parzen kernels are presented. They are able to estimate the regression function in an incremental manner. The estimators apply two techniques commonly used in concept-drifting data streams, i.e., the forgetting factor and the sliding window. The methods are applicable for models in which both the function and the noise variance change over time. Although nonparametric methods based on Parzen kernels were previously successfully applied in the literature to online regression function estimation, the problem of estimating the variance of noise was generally neglected. It is sometimes of profound interest to know the variance of the signal considered, e.g., in economics, but it can also be used for determining confidence intervals in the estimation of the regression function, as well as while evaluating the goodness of fit and in controlling the amount of smoothing. The present paper addresses this issue. Specifically, variance estimators are proposed which are able to deal with concept drifting data by applying a sliding window and a forgetting factor, respectively. A number of conducted numerical experiments proved that the proposed methods perform satisfactorily well in estimating both the regression function and the variance of the noise.

5

Instance selection techniques in reduction of data streams derived from medical devices

Byczkowska-Lipińska L., Wosiak A.

Przegląd Elektrotechniczny

|

2017

|

R. 93, nr 12

115--118

EN

The research described in this paper concerns the reduction of streams of data derived from medical devices, i.e. ECG recordings. Experimental studies included three instance selection techniques: thresholding method, bounds checking and frequent data reduction . It was shown that application the instance selection techniques may reduce data stream by over 90% without losing anomalies or the measurements that are key values for the medical diagnosis.

PL

W ramach niniejszej pracy przeprowadzona została redukcja strumienia danych pozyskanych z urządzeń medycznych. Badania eksperymentalne obejmowały zastosowanie trzech technik selekcji przypadków: metody eliminacji progowej, weryfikacji zakresu oraz redukcji obiektów częstych. W pracy zostało wykazane, że zastosowanie selekcji przypadków pozwala na redukcję strumienia danych o ponad 90% bez utraty wartości kluczowych dla postawienia diagnozy medycznej.

6

Exploring complex and big data

Stefanowski J., Krawiec K., Wrembel R.

International Journal of Applied Mathematics and Computer Science

|

2017

|

Vol. 27, no. 4

669--679

EN

This paper shows how big data analysis opens a range of research and technological problems and calls for new approaches. We start with defining the essential properties of big data and discussing the main types of data involved. We then survey the dedicated solutions for storing and processing big data, including a data lake, virtual integration, and a polystore architecture. Difficulties in managing data quality and provenance are also highlighted. The characteristics of big data imply also specific requirements and challenges for data mining algorithms, which we address as well. The links with related areas, including data streams and deep learning, are discussed. The common theme that naturally emerges from this characterization is complexity. All in all, we consider it to be the truly defining feature of big data (posing particular research and technological challenges), which ultimately seems to be of greater importance than the sheer data volume.

7

Survival analysis on data streams: Analyzing temporal events in dynamically changing environments

Shaker A., Hüllermeier E.

International Journal of Applied Mathematics and Computer Science

|

2014

|

Vol. 24, no. 1

199--212

EN

In this paper, we introduce a method for survival analysis on data streams. Survival analysis (also known as event history analysis) is an established statistical method for the study of temporal “events” or, more specifically, questions regarding the temporal distribution of the occurrence of events and their dependence on covariates of the data sources. To make this method applicable in the setting of data streams, we propose an adaptive variant of a model that is closely related to the well-known Cox proportional hazard model. Adopting a sliding window approach, our method continuously updates its parameters based on the event data in the current time window. As a proof of concept, we present two case studies in which our method is used for different types of spatio-temporal data analysis, namely, the analysis of earthquake data and Twitter data. In an attempt to explain the frequency of events by the spatial location of the data source, both studies use the location as covariates of the sources.

8

Analiza obrazu termowizyjnego uzyskanego w wyniku strumieniowania

Onoszko K., Lis M.

Elektronika : konstrukcje, technologie, zastosowania

|

2013

|

Vol. 54, nr 10

63-66

PL

Artykuł opisuje problem zmian kolorystyki obrazów przesyłanych z użyciem strumieni multimedialnych. Analizie zostaje poddany obraz uzyskany z kamery termowizyjnej, na którym znajduje się pasek palety barw. Monitorując zmiany tego fragmentu, zostaje przeprowadzona próba oceny wpływu algorytmu kompresyjnego na jakość odwzorowania barw. W tekście zaproponowany również został sposób na niwelację negatywnego wpływu kompresji.

EN

Article describes the problem changes color images transmitted using multimedia streams. It analyzes the image from the infrared camera, where is a stripe color palette. Observing changes in this passage, is carried out an attempt to assess the impact of compression algorithms on fidelity color. The text has proposed a way to remove the negative effects of compression.