On utilizing weak estimators to achieve the online classification of data streams

Author's accepted version (post-print).Available from 03/09/2021.acceptedVersio

Intelligent Learning Automata-based Strategies Applied to Personalized Service Provisioning in Pervasive Environments

Doktorgradsavhandling i informasjons- og kommunikasjonsteknologi, Universitetet i Agder, Grimstad, 201

Optimising Firewall Performance in Dynamic Networks

More and more devices connect to the internet, this means that a lot sensitive information will be stored in various networks. In order to secure this information and manage the large amount of inevitable network traffic that these devices create, an optimised firewall is needed. In order to meet this demand, the thesis proposes two algorithms for solving the problem. The first algorithm will minimise the rule matching time by using a simple condition for performing swapping that both preserves the firewall consistency, the firewall integrity and ensures a greedy reduction of the matching time. The solution is novel in itself and can be considered as a generalisation of the algorithm proposed by Fulp in the paper 'Optimization of network firewall policies using ordered sets and directed acyclical graphs'. The second algorithm will read the network traffic and provide network statistics to the first algorithm. The solution is a novel modification of the algorithm by Oommen and Rueda in the paper 'Stochastic learning-based weak estimation of multinomial random variables and its applications to pattern recognition in non-stationary environments'. It will be shown that both algorithms, through experiments, are able to satisfy the problem of optimising a firewall

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Stochastic automata-based estimators for adaptively compressing files with nonstationary distributions

This correspondence shows that learning automata techniques, which have been useful in developing weak estimators, can be applied to data compression applications in which the data distributions are nonstationary. The adaptive coding scheme utilizes stochastic learning- based weak estimation techniques to adaptively update the probabilities of the source symbols, and this is done without resorting to either maximum likelihood, Bayesian, or sliding-window methods. The authors have incorporated the estimator in the adaptive Fano coding scheme and in an adaptive entropy-based scheme that "resembles" the well-known arithmetic coding. The empirical results obtained for both of these adaptive methods are obtained on real-life files that possess a fair degree of nonstationarity. From these results, it can be seen that the proposed schemes compress nearly 10% more than their respective adaptive methods that use maximum-likelihood estimator-based estimates

State Of the Art Report in the fields of numerical analysis and scientific computing. Final version as of 16/02/2020 deliverable D4.1 of the HORIZON 2020 project EURAD.: European Joint Programme on Radioactive Waste Management

Document information Project Acronym EURAD Project Title European Joint Programme on Radioactive Waste Management Project Type European Joint Programme (EJP) EC grant agreement No. 847593 Project starting / end date 1 st June 2019-30 May 2024 Work Package No. 4 Work Package Title Development and Improvement Of NUmerical methods and Tools for modelling coupled processes Work Package Acronym DONUT Deliverable No. 4.

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition