Description: Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery by Boris Kovalerchuk, Kawa Nazemi, Nuno Datia, Ebad Banissi, Răzvan Andonie 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. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. FORMAT Paperback CONDITION Brand New Publisher Description 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 scholarspresented 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. Table of Contents 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 Sensors 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 MultiDimensional 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? Details ISBN3030931218 Author Rzvan Andonie Pages 674 Publisher Springer Nature Switzerland AG Series Studies in Computational Intelligence Year 2023 Edition 1st ISBN-13 9783030931216 Format Paperback Imprint Springer Nature Switzerland AG Place of Publication Cham Country of Publication Switzerland Edited by Rzvan Andonie Publication Date 2023-06-06 Edition Description 1st ed. 2022 Alternative 9783030931186 DEWEY 006.312 Series Number 1014 Audience Professional & Vocational Illustrations 288 Illustrations, color; 46 Illustrations, black and white; XV, 674 p. 334 illus., 288 illus. in color. ISBN-10 3030931218 UK Release Date 2023-06-06 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:159744011;
Price: 317.54 AUD
Location: Melbourne
End Time: 2024-12-23T06:23:00.000Z
Shipping Cost: 16.61 AUD
Product Images
Item Specifics
Restocking fee: No
Return shipping will be paid by: Buyer
Returns Accepted: Returns Accepted
Item must be returned within: 30 Days
Format: Paperback
ISBN-13: 9783030931216
Author: Boris Kovalerchuk, Kawa Nazemi, Nuno Datia, Ebad Banissi
Type: Does not apply
Book Title: Integrating Artificial Intelligence and Visualization for Visual
Language: Does not apply
