Privacy Preserving Support Vector Machine Classification in WSN: Second Edition - Couverture souple

Azim, Muhammad Anwarul ; Kim, Dong Seong ; Park, Jong Sou

9786209193507: Privacy Preserving Support Vector Machine Classification in WSN: Second Edition

Couverture souple

ISBN 10 : 6209193501 ISBN 13 : 9786209193507

Editeur : LAP LAMBERT Academic Publishing, 2025

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2025
Langue: anglais
ISBN 10: 6209193501
ISBN 13: 9786209193507
Reliure: Broch�
Nombre de pages: 60
Coordonn�es du fabricant: non disponible
Personne responsable: KreGa Onlinehandel GmbH
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Privacy Preserving Support Vector Machine Classification in WSN

Muhammad Anwarul Azim

Edit� par LAP LAMBERT Academic Publishing Nov 2025, 2025

ISBN 10 : 6209193501 ISBN 13 : 9786209193507

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 60 pp. Englisch. N� de r�f. du vendeur 9786209193507

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Privacy Preserving Support Vector Machine Classification in WSN

Muhammad Anwarul Azim

Edit� par LAP LAMBERT Academic Publishing Nov 2025, 2025

ISBN 10 : 6209193501 ISBN 13 : 9786209193507

Neuf Taschenbuch

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -The increasing prominence of Wireless Sensor Network (WSN) is stimulating greater interest in developing many application areas. WSNs promise viable solutions aiming at many monitoring problems despite energy, communication, computation & storage constraints. The security issues, data privacy, confidentiality and integrity become vital when the sensors are deployed in a hostile environment. Support Vector Machines (SVM) classification is one of the most widely used classifications having advantage of accuracy and sparse representation that SVMs provide for decision boundaries. It is important to achieve energy efficient data mining in WSN while preserving privacy of data. In this thesis we introduce SVM classification for WSN consisting energy efficiency advantage by distributed incremental learning for the training and construction of global SVM classification model without disclosing the data to others. We show security analysis and energy estimation for preserving privacy and energy efficiency in WSN using SVM.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 60 pp. Englisch. N� de r�f. du vendeur 9786209193507

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