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Intelligent Decision Technologies: Proceedings of the 12th KES International Conference on Intelligent Decision Technologies (KES-IDT 2020) de Ireneusz Czarnowski

Intelligent Decision Technologies: Proceedings of the 12th KES International Conference on Intelligent Decision Technologies (KES-IDT 2020): Smart Innovation, Systems and Technologies, cartea 193

Editat de Ireneusz Czarnowski, Robert J. Howlett, Lakhmi C. Jain
en Limba Engleză Hardback – 12 iun 2020
This book gathers selected papers from the KES-IDT-2020 Conference, held as a Virtual Conference on June 17–19, 2020.  The aim of the annual conference was to present and discuss the latest research results, and to generate new ideas in the field of intelligent decision-making. However, the range of topics discussed during the conference was definitely broader and covered methods in e.g. classification, prediction, data analysis, big data, data science, decision support, knowledge engineering, and modeling in such diverse areas as finance, cybersecurity, economics, health, management and transportation. The Problems in Industry 4.0 and IoT are also addressed. 

The book contains several sections devoted to specific topics, such as 

Intelligent Data Processing and its Applications

High-Dimensional Data Analysis and its Applications

Multi-Criteria Decision Analysis – Theory and Applications

Large-Scale Systems for Intelligent Decision-Making and Knowledge Engineering

Decision Technologies and Related Topics in Big Data Analysis of Social and Financial Issues

Decision-Making Theory for Economics
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Specificații

ISBN-13: 9789811559242
ISBN-10: 9811559244
Ilustrații: XVII, 546 p. 215 illus., 153 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.96 kg
Ediția:1st ed. 2020
Editura: Springer Nature Singapore
Colecția Springer
Seria Smart Innovation, Systems and Technologies

Locul publicării:Singapore, Singapore

Notă biografică

Ireneusz Czarnowski is a Professor at the Gdynia Maritime University. He holds B.Sc. and M.Sc. degrees in Electronics and Communication Systems from the same university. He received his doctorate in Computer Science from the Faculty of Computer Science and Management, Poznan University of Technology, in 2004. In 2012, he completed his postdoctoral degree at Wroclaw University of Science and Technology. His research interests include artificial intelligence, machine learning, evolutionary computations, multi-agent systems, data mining and data science. He is an Associate Editor of the Journal of Knowledge-Based and Intelligent Engineering Systems, published by IOS Press, and a reviewer for several scientific journals.

Dr. Robert Howlett is the Executive Chair of KES International, a non-profit organization that facilitates the dissemination of research results in areas including intelligent systems, sustainability, and knowledge transfer. A Visiting Professor at Bournemouth University, UK, his technical expertise is in the use of intelligent systems to solve industrial problems. He has been successful in applying artificial intelligence, machine learning, and related technologies to sustainability and renewable energy systems; condition monitoring, diagnostic tools, and systems; and automotive electronics and engine management systems. His current research focuses on the use of smart microgrids to achieve reduced energy costs and lower carbon emissions in areas such as housing and protected horticulture.  

Dr. Lakhmi C. Jain, Ph.D., M.E., Fellow (Engineers Australia), is affiliated with the University of Technology Sydney, Australia, and Liverpool Hope University, UK. Professor Jain serves with KES International, which provides the professional community with opportunities for publication, knowledge exchange, cooperation, and teambuilding. Involving over 5,000 researchers drawn from universities and companies worldwide, KES facilitates international cooperation and generates synergy in teaching and research. KES regularly provides networking opportunities for the professional community through one of the largest conferences of its kind.

Textul de pe ultima copertă

This book gathers selected papers from the KES-IDT-2020 Conference, held as a Virtual Conference on June 17–19, 2020.  The aim of the annual conference was to present and discuss the latest research results, and to generate new ideas in the field of intelligent decision-making. However, the range of topics discussed during the conference was definitely broader and covered methods in e.g. classification, prediction, data analysis, big data, data science, decision support, knowledge engineering, and modeling in such diverse areas as finance, cybersecurity, economics, health, management and transportation. The Problems in Industry 4.0 and IoT are also addressed. 

The book contains several sections devoted to specific topics, such as 

Intelligent Data Processing and its Applications

High-Dimensional Data Analysis and its Applications

Multi-Criteria Decision Analysis – Theory and Applications

Large-Scale Systems for Intelligent Decision-Making and Knowledge Engineering

Decision Technologies and Related Topics in Big Data Analysis of Social and Financial Issues

Decision-Making Theory for Economics

Caracteristici

Includes recent research on intelligent decision technologies Presents the latest findings in data science Addresses such diverse areas as finance, linguistics, management, and transportation

Cuprins

ArgVote: Which Party Argues Like Me? Exploring an Argument-Based Voting Advice Application.- ArgVote: Which Party Argues Like Me? Exploring an Argument-Based Voting Advice Application.- Impact of the Time Window Length on the Ship Trajectory Reconstruction Based on AIS Data Clustering.- Improved Genetic Algorithm for Electric Vehicle Charging Station Placement.- Solving a Many-objective Crop Rotation Problem with Evolutionary Algorithms.- The Utility of Neural Model in Predicting Tax Avoidance Behavior.- Triple-Station System of Detecting Small Airborne Objects in Dense Urban Environment.- Using Families of Extremal Quasi-Orthogonal Matrices in Communication Systems.- Variable Selection for Correlated High-dimensional Data with Infrequent Categorical Variables: Based on Sparse Sample Regression and Anomaly Detection Technology.- Verification of the Compromise Effect’s Suitability Based on Product Features of Automobiles.