Knowledge Computing and Its Applications: Knowledge Manipulation and Processing Techniques: Volume 1, Volume 1

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· Springer
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この電子書籍について

This book provides a major forum for the technical advancement of knowledge management and its applications across diversified domains. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, data mining, recommender systems, image processing, pattern recognition and predictions using machine learning techniques is the major strength of this book.

Effective knowledge management has become a key to the success of business organizations, and can offer a substantial competitive edge.

So as to be accessible to all scholars, this book combines the core ideas of knowledge management and its applications in numerous domains, illustrated in case studies. The techniques and concepts proposed here can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas.

著者について

Margret Anouncia S is a Professor at Vellore Institute of Technology (VIT) University in India. She received her bachelor’s degree in Computer Science and Engineering from Bharathidasan University (1993), Tiruchirappalli, India and a master’s in Software Engineering from Anna University (2000), Chennai, India. She was awarded a Doctorate in Computer Science and Engineering at VIT University (2008). Her areas of interest include digital image processing, software engineering and knowledge engineering.

Uffe Kock Wiil received M.Sc. (1990) and Ph.D. (1993) degrees from Aalborg University, Aalborg, Denmark. He has been a Professor of Software Engineering at the University of Southern Denmark, Denmark since 2004. His primary research interests are health informatics (clinical decision support systems; patient empowerment) and security informatics (analysis and visualization of crime-related data).

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