Claudia Eckert mainly investigates Engineering design process, New product development, Artificial intelligence, Machine learning and Product. Her studies deal with areas such as Knowledge management, Design process, Process and Process management as well as Engineering design process. Her New product development research incorporates themes from Product design, Systems engineering, Industrial engineering and Engineering management.
Her biological study spans a wide range of topics, including State and Malware. Her research in Machine learning focuses on subjects like Pattern recognition, which are connected to Detector. Her Product research is multidisciplinary, relying on both Risk analysis and Function.
Her primary areas of study are Engineering design process, Process, Systems engineering, Design process and Product. Her Engineering design process study combines topics from a wide range of disciplines, such as Management science, Industrial engineering, Engineering management, Knowledge management and New product development. Claudia Eckert frequently studies issues relating to Process management and Knowledge management.
Her New product development research integrates issues from Product design and Manufacturing engineering. Her Design process study typically links adjacent topics like Process modeling. As part of her studies on Product, she frequently links adjacent subjects like Risk analysis.
Her main research concerns Engineering design process, Process, New product development, Design process and Artificial intelligence. Claudia Eckert combines subjects such as Context, Management science, Engineering management, Terminology and Product with her study of Engineering design process. Her studies in Product integrate themes in fields like Truck and Systems engineering.
Her work in Process tackles topics such as Function which are related to areas like Benchmarking, Reverse engineering and Software engineering. The concepts of her Design process study are interwoven with issues in Key, Engineering ethics, Risk analysis and Product engineering. Her Artificial intelligence study incorporates themes from Machine learning and Malware.
Claudia Eckert mostly deals with Engineering design process, Artificial intelligence, Malware, Machine learning and Process. Her research integrates issues of Management science, Terminology, Product, Design process and Component in her study of Engineering design process. Her Artificial intelligence research is multidisciplinary, incorporating elements of Diagram, GRASP and Functional decomposition.
The Malware study combines topics in areas such as Executable, Support vector machine, Data structure, Feature selection and Asset. Her Machine learning research incorporates elements of Functional analysis, Training set, Data mining and Product. Her Process research focuses on Resource and how it relates to Empirical research, Decision support system and Knowledge management.
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Predicting change propagation in complex design
P. John Clarkson;Caroline Simons;Claudia Eckert.
Journal of Mechanical Design (2001)
Change and customisation in complex engineering domains
Claudia Eckert;P. John Clarkson;Winfried Zanker.
Research in Engineering Design (2004)
Sources of inspiration: a language of design
Claudia Eckert;Martin Stacey.
Design Studies (2000)
Engineering change: an overview and perspective on the literature
T. A. W. Jarratt;T. A. W. Jarratt;C. M. Eckert;N. H. M. Caldwell;P. J. Clarkson.
Research in Engineering Design (2011)
Deep Learning for Classification of Malware System Call Sequences
Bojan Kolosnjaji;Apostolis Zarras;George D. Webster;Claudia Eckert.
australasian joint conference on artificial intelligence (2016)
Design process improvement : a review of current practice
PJ Clarkson;CM Eckert.
(2005)
Change Propagation Analysis in Complex Technical Systems
Monica Giffin;Olivier de Weck;Gergana Bounova;Rene Keller.
Journal of Mechanical Design (2007)
Distributed Attribute-Based Encryption
Sascha Müller;Stefan Katzenbeisser;Claudia Eckert.
international conference on information security and cryptology (2009)
Is negative selection appropriate for anomaly detection
Thomas Stibor;Philipp Mohr;Jonathan Timmis;Claudia Eckert.
genetic and evolutionary computation conference (2005)
Is Feature Selection Secure against Training Data Poisoning
Huang Xiao;Battista Biggio;Gavin Brown;Giorgio Fumera.
international conference on machine learning (2015)
University of Cambridge
University of Cagliari
University of Cagliari
University of Cambridge
The Ohio State University
University of Passau
Tel Aviv University
MIT
University of Cagliari
Georgia Institute of Technology
Profile was last updated on December 6th, 2021.
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The ranking d-index is inferred from publications deemed to belong to the considered discipline.
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