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|Title: ||Edge Computing Tied in Artificial Neural Network Classifiers|
|Other Titles: ||Dirbtinių neuronų tinklų klasifikatoriais susieta kraštų kompiuterija|
|Authors: ||Skirelis, Julius|
|Issue Date: ||6-May-2021|
|Publisher: ||Vilniaus Gedimino technikos universitetas|
|Citation: ||Skirelis, J. 2021. Edge Computing Tied in Artificial Neural Network Classifiers: doctoral dissertation. Vilnius: Technika, 152 p.|
|Abstract: ||The dissertation deals with traditional and artiﬁcial neural networks based intelligent classiﬁers, simulations and natural experiments are performed taking into account their speciﬁcs and possible speciﬁc applications in: cell colony image parametrization and image stitching. Research is conducted to evaluate the poten tial of developed algorithms and methods application to address edge computing challenges and are therefore examined in different network topologies: centralized, decentralized and distributed. Edge computing and its beneﬁts are compared to the currently relevant but no longer meeting the needs technology – the cloud computing.
The object of the dissertation is the intelligent classiﬁer for edge computing tasks. The aim of the work is to propose and investigate original, artiﬁcial neural network classiﬁer based Internet of Things systems for the edge computing tasks. The dissertation is designated to contribute to the development of artiﬁcial neural network based edge computing solutions. Analytical review of artiﬁcial neural networks for edge computing is performed that underlies the work the relevance of the raise problem and explains the importance of research of the classiﬁer application in edge computing.
The dissertation consists of an introduction, four chapters and general conclusions. In the ﬁrst chapter essential knowledge and progress on artiﬁcial neural networks based classiﬁers application in edge computing technology is presented, the relationship between the edge computing and Internet of Things is deﬁned, traditional and intelligent classiﬁcation methods are discussed, self-organizing adaptive resonance theory based classiﬁers are analyzed and dissertation tasks are formulated. In the second chapter three simulation experiments are described with further analysis of their results revealing quantitative and qualitative Internet of Things characteristics, lastly, Internet of Things, edge computing and sensor network speciﬁcs are revealed. The third chapter describes the proposed cell colony images parameterization method, the experiments using constructed stand are performed comparing heuristic, support vectors, and adaptive resonance theory versions 1 and 2 classiﬁers by evaluation of classiﬁcation quality based on statistical methods. In the fourth chapter a proposed ﬁxed-structure artiﬁcial neural network classiﬁer based adaptive resolution selection stitching system is described, further the arranged constructed stand and obtained experiment results are discussed.
The main results of the thesis were published in 9 scientiﬁc publications: three of them were printed in peer-reviewed scientiﬁc journals, two of which are listed in Clarivate Analytics Web of Science and one of them is with impact factor, six articles – in conference proceedings. The research results were presented in 11 scientiﬁc conferences.|
|Description: ||Doctoral dissertation|
|Appears in Collections:||Technologijos mokslų daktaro disertacijos ir jų santraukos|
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