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001447330 001__ 1447330
001447330 003__ OCoLC
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001447330 020__ $$a9783030931193$$q(electronic bk.)
001447330 020__ $$a3030931196$$q(electronic bk.)
001447330 020__ $$z9783030931186
001447330 0247_ $$a10.1007/978-3-030-93119-3$$2doi
001447330 035__ $$aSP(OCoLC)1327598293
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001447330 049__ $$aISEA
001447330 050_4 $$aQ335
001447330 08204 $$a006.3$$223/eng/20220608
001447330 24500 $$aIntegrating artificial intelligence and visualization for visual knowledge discovery /$$cBoris Kovalerchuk, Kawa Nazemi, Rǎzvan Andonie, Nuno Datia, Ebad Banissi, editors.
001447330 264_1 $$aCham :$$bSpringer,$$c[2022]
001447330 264_4 $$c©2022
001447330 300__ $$a1 online resource (xv, 674 pages) :$$billustrations (chiefly color).
001447330 336__ $$atext$$btxt$$2rdacontent
001447330 337__ $$acomputer$$bc$$2rdamedia
001447330 338__ $$aonline resource$$bcr$$2rdacarrier
001447330 4901_ $$aStudies in computational intelligence,$$x1860-9503 ;$$vvolume 1014
001447330 500__ $$aIncludes author index.
001447330 5050_ $$aVisual 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?
001447330 506__ $$aAccess limited to authorized users.
001447330 520__ $$aThis 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.
001447330 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 8, 2022).
001447330 650_0 $$aArtificial intelligence.
001447330 650_0 $$aInformation visualization.
001447330 650_0 $$aVisual analytics.
001447330 655_0 $$aElectronic books.
001447330 7001_ $$aKovalerchuk, Boris,$$eeditor.
001447330 7001_ $$aNazemi, Kawa,$$eeditor.
001447330 7001_ $$aAndonie, Rǎzvan,$$eeditor.
001447330 7001_ $$aDatia, Nuno,$$eeditor.
001447330 7001_ $$aBanissi, E.$$q(Ebad),$$eeditor.
001447330 830_0 $$aStudies in computational intelligence ;$$vv. 1014.$$x1860-9503
001447330 852__ $$bebk
001447330 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-93119-3$$zOnline Access$$91397441.1
001447330 909CO $$ooai:library.usi.edu:1447330$$pGLOBAL_SET
001447330 980__ $$aBIB
001447330 980__ $$aEBOOK
001447330 982__ $$aEbook
001447330 983__ $$aOnline
001447330 994__ $$a92$$bISE