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Full Description
This book contains selected papers from the KES-IDT-2021 conference, being held as a virtual conference in June 14-16, 2021.
The KES-IDT is an interdisciplinary conference with opportunities for the presentation of new research results and discussion about them under the common title "Intelligent Decision Technologies". The conference has been creating for years a platform for knowledge transfer and the generation of new ideas in the field of intelligent decision making.
The range of topics discussed during the conference covered methods of classification, prediction, data analysis, big data, decision support, knowledge engineering, modeling, social networks and many more in areas such as finance, economy, management and transportation. The discussed topics covered also decision making for problems regarding the electric vehicle industry.
The book contains also several sections devoted to specific topics, such as
Advances in intelligent data processing and its applications
Multi-criteria decision analysis methods
Knowledge engineering in large-scale systems
High-dimensional data analysis
Spatial data analysis and sparse estimation
Innovative technologies and applications in computer intelligence
Intelligent diagnosis and monitoring of systems
Decision making theory for economics.
Contents
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.
