Intelligent Systems in Big Data, Semantic Web and Machine Learning
(Sprache: Englisch)
This book describes important methodologies, tools and techniques from the fields of artificial intelligence, basically those which are based on relevant conceptual and formal development. The coverage is wide, ranging from machine learning to the use...
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Klappentext zu „Intelligent Systems in Big Data, Semantic Web and Machine Learning “
This book describes important methodologies, tools and techniques from the fields of artificial intelligence, basically those which are based on relevant conceptual and formal development. The coverage is wide, ranging from machine learning to the use of data on the Semantic Web, with many new topics. The contributions are concerned with machine learning, big data, data processing in medicine, similarity processing in ontologies, semantic image analysis, as well as many applications including the use of machine leaning techniques for cloud security, artificial intelligence techniques for detecting COVID-19, the Internet of things, etc. The book is meant to be a very important and useful source of information for researchers and doctoral students in data analysis, Semantic Web, big data, machine learning, computer engineering and related disciplines, as well as for postgraduate students who want to integrate the doctoral cycle.
Inhaltsverzeichnis zu „Intelligent Systems in Big Data, Semantic Web and Machine Learning “
Experimental evaluation of proposed algorithm for identifying abnormal messages in SIP Network.- Smart tourism recommender system using semantic matching.- Data-Driven information filtering framework for dynamically hybrid job recommendation.- Semantic image analysis for automatic image annotation.- New method for data replication and reconstruction in distributed databases.- A distributed intrusion detection approach based on machine learning technique for a cloud security.- Cb2Onto: OWL ontology learning approach from couchbase.- Data profling over big data area - A Survey of big data profiling: state-of-the-Art, use cases and challenges.- Generalization of the fuzzy conformable differentiability with application to fuzzy fractional differential equations.
Bibliographische Angaben
- 2022, 1st ed. 2021, XIV, 315 Seiten, 120 farbige Abbildungen, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Herausgegeben: Noreddine Gherabi, Janusz Kacprzyk
- Verlag: Springer, Berlin
- ISBN-10: 3030725901
- ISBN-13: 9783030725907
Sprache:
Englisch
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