Knowledge Engineering for Modern Information Systems : Methods, Models and Tools /
ed. by Sandeep Kautish, Saurav Nanda, Prateek Agrawal, Vishu Madaan, Charu Gupta, Anand Sharma.
- 1 online resource (VI, 232 p.)
- Smart Computing Applications , 3 2700-6239 ; .
Frontmatter -- Contents -- Knowledge engineering for industrial expert systems -- Machine learning integrated blockchain model for Industry 4.0 smart applications -- Prototyping the expectancy disconfirmation theory model for quality service delivery in federal university libraries in Nigeria -- Design of chatbot using natural language processing -- Algorithm development based on an integrated approach for identifying cause and effect relationships between different factors -- Risk analysis and management in projects -- Assessing and managing risks in smart computing applications -- COVID-19 visualization and exploratory data analysis -- Business intelligence and decision support systems: business applications in the modern information system era -- Business intelligence implementation in different organizational setup evidence from reviewed literatures -- Conceptualization of a modern digital-driven health-care management information system (HMIS) -- Knowledge engine for a Hindi text-to-scene generation system -- Index
restricted access http://purl.org/coar/access_right/c_16ec
Knowledge Engineering (KE) is a fi eld within artifi cial intelligence that develops knowledgebased systems. KE is the process of imitating how a human expert in a specifi c domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent fi ndings and innovative research in the information system and KE domain. Highlighting the challenges and diffi culties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will fi nd this book a valuable source of ideas and guidance.
Mode of access: Internet via World Wide Web.
In English.
9783110713633
10.1515/9783110713633 doi
Algorithmen.
Big Data.
Künstliche Intelligenz.
Maschinelles Lernen.
Frontmatter -- Contents -- Knowledge engineering for industrial expert systems -- Machine learning integrated blockchain model for Industry 4.0 smart applications -- Prototyping the expectancy disconfirmation theory model for quality service delivery in federal university libraries in Nigeria -- Design of chatbot using natural language processing -- Algorithm development based on an integrated approach for identifying cause and effect relationships between different factors -- Risk analysis and management in projects -- Assessing and managing risks in smart computing applications -- COVID-19 visualization and exploratory data analysis -- Business intelligence and decision support systems: business applications in the modern information system era -- Business intelligence implementation in different organizational setup evidence from reviewed literatures -- Conceptualization of a modern digital-driven health-care management information system (HMIS) -- Knowledge engine for a Hindi text-to-scene generation system -- Index
restricted access http://purl.org/coar/access_right/c_16ec
Knowledge Engineering (KE) is a fi eld within artifi cial intelligence that develops knowledgebased systems. KE is the process of imitating how a human expert in a specifi c domain would act and take decisions. It contains large amounts of knowledge, like metadata and information about a data object that describes characteristics such as content, quality, and format, structure and processes. Such systems are computer programs that are the basis of how a decision is made or a conclusion is reached. It is having all the rules and reasoning mechanisms to provide solutions to real-world problems. This book presents an extensive collection of the recent fi ndings and innovative research in the information system and KE domain. Highlighting the challenges and diffi culties in implementing these approaches, this book is a critical reference source for academicians, professionals, engineers, technology designers, analysts, undergraduate and postgraduate students in computing science and related disciplines such as Information systems, Knowledge Engineering, Intelligent Systems, Artifi cial Intelligence, Cognitive Neuro - science, and Robotics. In addition, anyone who is interested or involved in sophisticated information systems and knowledge engineering developments will fi nd this book a valuable source of ideas and guidance.
Mode of access: Internet via World Wide Web.
In English.
9783110713633
10.1515/9783110713633 doi
Algorithmen.
Big Data.
Künstliche Intelligenz.
Maschinelles Lernen.