Part II Model for Adaptive Semantics Visualization
Part III Proof of the Conceptual Model.
This book introduces a novel approach for intelligent visualizations that adapts the different visual variables and data processing to human's behavior and given tasks. Thereby a number of new algorithms and methods are introduced to satisfy the human need of information and knowledge and enable a usable and attractive way of information acquisition. Each method and algorithm is illustrated in a replicable way to enable the reproduction of the entire "SemaVis" system or parts of it. The introduced evaluation is scientifically well-designed and performed with more than enough participants to validate the benefits of the methods. Beside the introduced new approaches and algorithms, readers may find a sophisticated literature review in Information Visualization and Visual Analytics, Semantics and information extraction, and intelligent and adaptive systems. This book is based on an awarded and distinguished doctoral thesis in computer science
Visual Analytics for Strategic Decision Making in Technology Management
Deep Learning Image Recognition for Non-images
Self-service Data Classification Using Interactive Visualization and Interpretable Machine Learning
Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinates
Convolutional Neural Networks Analysis using Concentric-Rings Interactive Visualization
“Negative” Results – When the Measured Quantity Is Outside the Sensor’s Range – Can Help Data Processing
Visualizing and Explaining Language Models
Transparent Clustering with Cyclic Probabilistic Causal Models
Visualization and Self-Organizing Maps for the Characterization of Bank Clients
Augmented Classical Self-Organizing Map for Visualization of Discrete Data with Density Scaling
Gragnostics: Evaluating Fast, Interpretable Structural Graph Features for Classification and Visual Analytics
VisIRML Visualization with an Interactive Information Retrieval and Machine Learning Classifier
Visual Analytics of Hierarchical and Network Timeseries Models
ML approach to predict air quality using sensor and road traffic data
Context-Aware Diagnosis in Smart Manufacturing: TAOISM, an Industry 4.0-Ready Visual Analytics Model
Visual discovery of malware patterns in Android apps
Integrating Visual Exploration and Direct Editing of Multivariate Graphs
Real-Time Visual Analytics for Air Quality
Using Hybrid Scatterplots for Visualizing Multi‐Dimensional Data
Extending a genetic-based visualization: going beyond the radial layout?
Dual Y Axes Charts Defended: Case studies, domain analysis and a method
Hierarchical Visualization for Exploration of Large and Small Hierarchies
Geometric Analysis Leads to Adversarial Teaching of Cybersecurity
Applications and Evaluations of Drawing Scatterplots as Polygons and Outlier Points
Supply Chain and Decision Making: What is Next for Visualization?
This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses, " where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.