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Applied Machine Learning and Data Analytics - 5th International Conference, AMLDA 2022, Reynosa, Tamaulipas, Mexico, December 22–23, 2022, Revised Selected Papers (1st ed. 2023)
M.A. Jabbar, Fernando Ortiz-RodrÃguez, Sanju Tiwari, Patrick Siarry
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R2,309
Discovery Miles 23 090
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the 5th
International Conference on Applied Machine Learning and Data
Analytics, AMLDAÂ 2022, held in Reynosa, Tamaulipas, Mexico,
during December 22–23, 2022. The 16 full papers and 4 short
papers included in this book were carefully reviewed and selected
from 89 submissions. They were organized in topical sections as
follows: Machine learning, Healthcare and medical
imaging informatics; biometrics; forensics; precision agriculture;
risk management; robotics and satellite imaging.
Since the development of the semantic web, knowledge graphs (KGs)
have been used by search engines, knowledge-engines and
question-answering services as well as social networks. A knowledge
graph, also known as a semantic network, represents and illustrates
a network of real-world entities such as objects, events,
situations, or concepts and the relationships between them. This
information is usually stored in a graph database and visualized as
a graph structure, prompting the term "knowledge graph". Knowledge
graphs structure the information of entities, their properties and
the relation between them. Personal knowledge graphs (PKG) encode
the same information at an individual level and therefore vary
widely. PKGs require the processing of each person's individual
information and is constructed in an automated fashion. Once a PKG
is constructed, it will be integrated in broader purpose KGs. A PKG
is a representation of all relevant common-sense knowledge and
personal data for a user and can support the development of
innovative applications such as a digitalized personalized coach.
It empowers stakeholders to make more effective decisions. This
book explores in a structured manner the global advanced research
around PKGs to support the development of innovative digitalized
personalized applications such as personal banking, personalized
book-keeping, daily health-related activities monitoring and goal
management tracking. The authors present methodologies, tools and
applications including innovative topics tailored for PKGs such as
named entity recognition and linking, construction approaches,
modelling of personalization and context-awareness, evaluation
approaches, relation extraction techniques, query answering in user
specific knowledge graphs, knowledge representation and reasoning
(KRR), visualization tools, integration tools and techniques, and
fact summarization. The book provides systematic coverage of this
complex topic for researchers, scientists and engineers in both
industry and academia working in data science, ICTs, knowledge
engineering, semantic web, reasoning, information retrieval, and
machine and deep learning with a focus on knowledge graphs.
Advanced students with an interest in the field will also find this
to be a useful resource.
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