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Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25-28, 2020, Proceedings

1st ed. 2020., 2020

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Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

PloS one, 2019-07, Vol.14 (7), p.e0218942-e0218942

2019

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Temporal data analytics

Journal of biomedical informatics, 2016-08, Vol.62, p.276-277

2016

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Medical temporal-knowledge discovery via temporal abstraction

AMIA ... Annual Symposium proceedings, 2009-11, Vol.2009, p.452-456

2009

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Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25–28, 2020, Proceedings

2021

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Temporal patterns selection for All-Cause Mortality prediction in T2D with ANNs

Journal of biomedical informatics, 2022-10, Vol.134, p.104198-104198, Article 104198

2022

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Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

Information security technical report, 2009-02, Vol.14 (1), p.16-29

2009

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Continuously Predicting the Completion of a Time Intervals Related Pattern

Advances in Knowledge Discovery and Data Mining, p.239-251

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Mining Temporal Data

Machine Learning for Data Science Handbook, 2023, p.469-490

2023

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Keeping pace with the creation of new malicious PDF files using an active-learning based detection framework

Security informatics (Berlin), 2016-02, Vol.5 (1), p.1, Article 1

2016

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Correction to: HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population

Artificial Intelligence in Medicine, 2021, p.C1-C1

2021

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Detecting unknown malicious code by applying classification techniques on OpCode patterns

Security informatics (Berlin), 2012-02, Vol.1 (1), p.1-1, Article 1

2012

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Self-supervised Segment Contrastive Learning for Medical Document Representation

Artificial Intelligence in Medicine, p.312-321

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Enhancing Hypotension Prediction in Real-Time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms

Artificial Intelligence in Medicine, p.46-51

Volltextzugriff (PDF)

Classification of Univariate Time Series via Temporal Abstraction and Deep Learning

2022 IEEE International Conference on Big Data (Big Data), 2022, p.1260-1265

2022

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Experiments with hierarchical concept-based search

Studies in health technology and informatics, 2007, Vol.129 (Pt 1), p.422

2007

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Continuous Verification Using Keystroke Dynamics

2010 International Conference on Computational Intelligence and Security, 2010, p.411-415

2010

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Artificial Intelligence in Medicine: 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part I

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Artificial Intelligence in Medicine: 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II

Volltextzugriff (PDF)

Clustering di-graphs for continuously verifying users according to their typing patterns

2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, 2010, p.000445-000449

2010

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Identity theft, computers and behavioral biometrics

2009 IEEE International Conference on Intelligence and Security Informatics, 2009, p.155-160

2009

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Malicious Code Detection and Acquisition Using Active Learning

2007 IEEE Intelligence and Security Informatics, 2007, p.371-371

2007

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Active learning to improve the detection of unknown computer worms activity

2008 11th International Conference on Information Fusion, 2008, p.1-8

2008

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Host Based Intrusion Detection using Machine Learning

2007 IEEE Intelligence and Security Informatics, 2007, p.107-114

2007

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A comparative evaluation of full-text, concept-based, and context-sensitive search

Yearbook of medical informatics, 2008, p.82-82

2008

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Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25-28, 2020, Proceedings

Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory

Temporal data analytics

Medical temporal-knowledge discovery via temporal abstraction

Artificial Intelligence in Medicine: 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Minneapolis, MN, USA, August 25–28, 2020, Proceedings

Temporal patterns selection for All-Cause Mortality prediction in T2D with ANNs

Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

Continuously Predicting the Completion of a Time Intervals Related Pattern

Mining Temporal Data

Keeping pace with the creation of new malicious PDF files using an active-learning based detection framework

Correction to: HYPE: Predicting Blood Pressure from Photoplethysmograms in a Hypertensive Population

Detecting unknown malicious code by applying classification techniques on OpCode patterns

Self-supervised Segment Contrastive Learning for Medical Document Representation

Enhancing Hypotension Prediction in Real-Time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms

Classification of Univariate Time Series via Temporal Abstraction and Deep Learning

Experiments with hierarchical concept-based search

Continuous Verification Using Keystroke Dynamics

Artificial Intelligence in Medicine: 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part I

Artificial Intelligence in Medicine: 22nd International Conference, AIME 2024, Salt Lake City, UT, USA, July 9–12, 2024, Proceedings, Part II

Clustering di-graphs for continuously verifying users according to their typing patterns

Identity theft, computers and behavioral biometrics

Malicious Code Detection and Acquisition Using Active Learning

Active learning to improve the detection of unknown computer worms activity

Host Based Intrusion Detection using Machine Learning

A comparative evaluation of full-text, concept-based, and context-sensitive search

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