Título :	4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Dublin, Ireland, August 25–28, 2020, Proceedings
Tipo de documento:	documento electrónico
Autores:	Holzinger, Andreas, ; Kieseberg, Peter, ; Tjoa, A Min, ; Weippl, Edgar,
Mención de edición:	1 ed.
Editorial:	[s.l.] : Springer
Fecha de publicación:	2020
Número de páginas:	XI, 552 p. 171 ilustraciones, 112 ilustraciones en color.
ISBN/ISSN/DL:	978-3-030-57321-8
Nota general:	Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.
Palabras clave:	Inteligencia artificial  Procesamiento de imágenes  Visión por computador  Ingeniería de software  Ordenadores  Software de la aplicacion  Imágenes por computadora  visión  reconocimiento de patrones y gráficos  Entornos informáticos  Aplicaciones informáticas y de sistemas de información
Índice Dewey:	006.3 Inteligencia artificial
Resumen:	Este libro constituye las actas arbitradas de la 4.ª Conferencia internacional entre dominios IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9, CD-MAKE 2020, celebrada en Dublín, Irlanda, en agosto de 2020. Los 30 artículos completos revisados presentados fueron cuidadosamente revisados ​​y seleccionados entre 140 presentaciones. La integración entre dominios y la evaluación de diferentes campos proporciona una atmósfera para fomentar diferentes perspectivas y opiniones; Ofrecerá una plataforma para ideas novedosas y una nueva mirada a las metodologías para poner estas ideas en práctica en beneficio de la humanidad. Debido a la pandemia de Corona, CD-MAKE 2020 se llevó a cabo como un evento virtual.
Nota de contenido:	Explainable Artificial Intelligence: concepts, applications, research challenges and visions -- The Explanation Game: Explaining Machine Learning Models Using Shapley Values -- Back to the Feature: a Neural-Symbolic Perspective on Explainable AI -- Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification -- Explainable Reinforcement Learning: A Survey -- A Projected Stochastic Gradient algorithm for estimating Shapley Value applied in attribute importance -- Explaining predictive models with mixed features using Shapley values and conditional inference trees -- Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case -- eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters -- Data Understanding and Interpretation by the Cooperation of Data Analyst and Medical Expert -- A study on the fusion of pixels and patient metadata in CNN-based classification of skin lesion images -- The European legal framework for medical AI -- An Efficient Method for Mining Informative Association Rules in Knowledge Extraction -- Interpretation of SVM using Data Mining Technique to Extract Syllogistic Rules -- Non-Local Second-Order Attention Network For Single Image Super Resolution -- ML-ModelExplorer: An explorative model-agnostic approach to evaluate and compare multi-class classifiers -- Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints -- Scenario-based Requirements Elicitation for User-Centric Explainable AI A Case in Fraud Detection -- On-the-fly Black-Box Probably Approximately Correct Checking of Recurrent Neural Networks -- Active Learning for Auditory Hierarchy -- Improving short text classification through global augmentation methods -- Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM -- A Clustering Backed Deep Learning Approach for Document Layout Analysis -- Calibrating Human-AI Collaboration: Impactof Risk, Ambiguity and Transparency on Algorithmic Bias -- Applying AI in Practice: Key Challenges and Lessons Learned -- Function Space Pooling For Graph Convolutional Networks -- Analysis of optical brain signals using connectivity graph networks -- Property-Based Testing for Parameter Learning of Probabilistic Graphical Models -- An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge -- Inter-Space Machine Learning in Smart Environments.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i

4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Dublin, Ireland, August 25–28, 2020, Proceedings [documento electrónico] / Holzinger, Andreas, ; Kieseberg, Peter, ; Tjoa, A Min, ; Weippl, Edgar,  . -  1 ed.  . - [s.l.] : Springer, 2020 . - XI, 552 p. 171 ilustraciones, 112 ilustraciones en color.
ISBN : 978-3-030-57321-8
Libro disponible en la plataforma SpringerLink. Descarga y lectura en formatos PDF, HTML y ePub. Descarga completa o por capítulos.

Palabras clave:	Inteligencia artificial  Procesamiento de imágenes  Visión por computador  Ingeniería de software  Ordenadores  Software de la aplicacion  Imágenes por computadora  visión  reconocimiento de patrones y gráficos  Entornos informáticos  Aplicaciones informáticas y de sistemas de información
Índice Dewey:	006.3 Inteligencia artificial
Resumen:	Este libro constituye las actas arbitradas de la 4.ª Conferencia internacional entre dominios IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9, CD-MAKE 2020, celebrada en Dublín, Irlanda, en agosto de 2020. Los 30 artículos completos revisados presentados fueron cuidadosamente revisados ​​y seleccionados entre 140 presentaciones. La integración entre dominios y la evaluación de diferentes campos proporciona una atmósfera para fomentar diferentes perspectivas y opiniones; Ofrecerá una plataforma para ideas novedosas y una nueva mirada a las metodologías para poner estas ideas en práctica en beneficio de la humanidad. Debido a la pandemia de Corona, CD-MAKE 2020 se llevó a cabo como un evento virtual.
Nota de contenido:	Explainable Artificial Intelligence: concepts, applications, research challenges and visions -- The Explanation Game: Explaining Machine Learning Models Using Shapley Values -- Back to the Feature: a Neural-Symbolic Perspective on Explainable AI -- Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification -- Explainable Reinforcement Learning: A Survey -- A Projected Stochastic Gradient algorithm for estimating Shapley Value applied in attribute importance -- Explaining predictive models with mixed features using Shapley values and conditional inference trees -- Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case -- eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters -- Data Understanding and Interpretation by the Cooperation of Data Analyst and Medical Expert -- A study on the fusion of pixels and patient metadata in CNN-based classification of skin lesion images -- The European legal framework for medical AI -- An Efficient Method for Mining Informative Association Rules in Knowledge Extraction -- Interpretation of SVM using Data Mining Technique to Extract Syllogistic Rules -- Non-Local Second-Order Attention Network For Single Image Super Resolution -- ML-ModelExplorer: An explorative model-agnostic approach to evaluate and compare multi-class classifiers -- Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints -- Scenario-based Requirements Elicitation for User-Centric Explainable AI A Case in Fraud Detection -- On-the-fly Black-Box Probably Approximately Correct Checking of Recurrent Neural Networks -- Active Learning for Auditory Hierarchy -- Improving short text classification through global augmentation methods -- Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM -- A Clustering Backed Deep Learning Approach for Document Layout Analysis -- Calibrating Human-AI Collaboration: Impactof Risk, Ambiguity and Transparency on Algorithmic Bias -- Applying AI in Practice: Key Challenges and Lessons Learned -- Function Space Pooling For Graph Convolutional Networks -- Analysis of optical brain signals using connectivity graph networks -- Property-Based Testing for Parameter Learning of Probabilistic Graphical Models -- An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge -- Inter-Space Machine Learning in Smart Environments.
En línea:	https://link-springer-com.biblioproxy.umanizales.edu.co/referencework/10.1007/97 [...]
Link:	https://biblioteca.umanizales.edu.co/ils/opac_css/index.php?lvl=notice_display&i