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Kai Heinrich
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2020 – today
- 2024
- [c30]Kai Heinrich, Armin Keshavarzi:
Are Our Predictions Healthy? A Comparative Meta-Analysis of Machine Learning Studies in Predictive Healthcare. ECIS 2024 - 2023
- [j8]Lukas-Valentin Herm, Kai Heinrich
, Jonas Wanner, Christian Janiesch
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability. Int. J. Inf. Manag. 69: 102538 (2023) - 2022
- [j7]Jonas Wanner
The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study. Electron. Mark. 32(4): 2079-2102 (2022) - [c29]Johannes Graf, Gino Lancho, Patrick Zschech, Kai Heinrich:
Where was COVID-19 first discovered? Designing a question-answering system for pandemic situations. ECIS 2022 - [c28]Kai Heinrich, Minh Anh Vu, Anastasiia Vysochyna:
Algorithms as a Manager: A Critical Literature Review of Algorithm Management. ICIS 2022 - [i4]Johannes Graf, Gino Lancho, Patrick Zschech
Where Was COVID-19 First Discovered? Designing a Question-Answering System for Pandemic Situations. CoRR abs/2204.08787 (2022) - [i3]Lukas-Valentin Herm, Kai Heinrich
Stop ordering machine learning algorithms by their explainability! A user-centered investigation of performance and explainability. CoRR abs/2206.10610 (2022) - 2021
- [j6]Kai Heinrich
Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decis. Support Syst. 143: 113494 (2021) - [j5]Christian Janiesch
Machine learning and deep learning. Electron. Mark. 31(3): 685-695 (2021) - [c27]Jonas Wanner, Laurell Popp, Kevin Fuchs, Kai Heinrich, Lukas-Valentin Herm, Christian Janiesch:
Adoption Barriers of AI: a Context-Specific Acceptance Model for Industrial Maintenance. ECIS 2021 - [c26]Patrick Zschech, Jannis Walk, Kai Heinrich, Michael Vössing, Niklas Kühl:
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems. ECIS 2021 - [c25]Jonas Wanner
Stop Ordering Machine Learning Algorithms by Their Explainability! An Empirical Investigation of the Tradeoff Between Performance and Explainability. I3E 2021: 245-258 - [i2]Christian Janiesch, Patrick Zschech, Kai Heinrich:
Machine learning and deep learning. CoRR abs/2104.05314 (2021) - [i1]Patrick Zschech, Jannis Walk, Kai Heinrich, Michael Vössing, Niklas Kühl:
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems. CoRR abs/2104.11600 (2021) - 2020
- [j4]Patrick Zschech
Intelligent User Assistance for Automated Data Mining Method Selection. Bus. Inf. Syst. Eng. 62(3): 227-247 (2020) - [c24]Kai Heinrich, Johannes Graf, Ji Chen, Jakob Laurisch, Patrick Zschech:
Fool me Once, shame on You, Fool me Twice, shame on me: a Taxonomy of Attack and de-Fense Patterns for AI Security. ECIS 2020 - [c23]Marie-Christin Papen, Janine Göttling, Kai Heinrich, Lisa Kraus, Christian Leyh, Florian Siems:
Vindictive Word-of-Mouth on Social Media Platforms - an Empirical Investigation of Drivers and their Measurement. ECIS 2020 - [c22]Jonas Wanner, Lukas-Valentin Herm, Kai Heinrich, Christian Janiesch, Patrick Zschech:
White, Grey, Black: Effects of XAI Augmentation on the Confidence in AI-based Decision Support Systems. ICIS 2020 - [c21]Jonas Wanner, Kai Heinrich, Christian Janiesch, Patrick Zschech:
How Much AI Do You Require? Decision Factors for Adopting AI Technology. ICIS 2020 - [c20]Kai Heinrich, Adrian Fischer, Michael Seifert:
Die Rolle der mehrschichtigen Netzwerkanalyse im Bereich Social Media Analysis am Beispiel der Identifizierung von Persönlichkeitsmerkmalen. Wirtschaftsinformatik (Zentrale Tracks) 2020: 277-292 - [c19]Kai Heinrich, Patrick Zschech
Ein Vergleich aktueller Deep-Learning-Architekturen zur Prognose von Prozessverhalten. Wirtschaftsinformatik (Zentrale Tracks) 2020: 876-892
2010 – 2019
- 2019
- [j3]Patrick Zschech
Prognostic Model Development with Missing Labels - A Condition-Based Maintenance Approach Using Machine Learning. Bus. Inf. Syst. Eng. 61(3): 327-343 (2019) - [j2]Kai Heinrich, Patrick Zschech
Objekterkennung im Weinanbau - Eine Fallstudie zur Unterstützung von Winzertätigkeiten mithilfe von Deep Learning. HMD Prax. Wirtsch. 56(5): 964-985 (2019) - [c18]Kai Heinrich, Patrick Zschech, Tarek Skouti, Jakob Griebenow, Sebastian Riechert:
Demystifying the Black Box: A Classification Scheme for Interpretation and Visualization of Deep Intelligent Systems. AMCIS 2019 - [c17]Patrick Zschech, Kai Heinrich, Richard Horn, Daniel Höschele:
Towards a Text-based Recommender System for Data Mining Method Selection. AMCIS 2019 - [c16]Kai Heinrich, Andreas Roth, Patrick Zschech:
Everything counts: a Taxonomy of Deep Learning Approaches for Object Counting. ECIS 2019 - [c15]Michael Könning, Kai Heinrich, Christian Leyh, Markus Westner:
A Quantitative Analysis of Culture-induced differences in Pivotal IT Outsourcing Contract Features. ECIS 2019 - [c14]Jonathan Philipps, Kai Heinrich:
Influencing Factors of Clinical Patient Recruitment Systems Design. HICSS 2019: 1-10 - [c13]Patrick Zschech, Jonas Bernien, Kai Heinrich:
Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA's Turbofan Degradation. ICIS 2019 - [c12]Kai Heinrich, Andreas Roth, Lukas Breithaupt, Björn Möller, Johannes Maresch:
Yield Prognosis for the Agrarian Management of Vineyards using Deep Learning for Object Counting. Wirtschaftsinformatik 2019: 407-421 - 2018
- [c11]Kai Heinrich, Vera Fleißner:
Deep Intelligent Systems for Time Series Prediction: Champion or Lame Duck? - Evidence from Crude Oil Price Prediction. AMCIS 2018 - [c10]Michael Könning, Kai Heinrich, Patrick Zschech, Christian Leyh:
Analyzing Influences on Pivotal ITO Contract Features: A Quantitative Multi-Study Design with Evidence from Western Europe. AMCIS 2018 - [c9]Vera Fleißner, Kai Heinrich, Michael Seifert:
Towards an Open Book Architecture for Deep Learning Networks: Data Properties and Architectures - Evidence from Time Series Analytics. LWDA 2018: 132-138 - 2017
- [c8]Patrick Zschech, Kai Heinrich, Marcus Pfitzner, Andreas Hilbert:
Are You up for the Challenge? Towards the Development of a Big Data Capability Assessment Model. ECIS 2017 - 2016
- [b1]Kai Heinrich:
The missing link: a general prediction model based on textural and network data features. Dresden University of Technology, Germany, 2016 - [c7]Kai Heinrich:
The Missing Link - Predictive Models based on Textual and Dynamic Network Data. AMCIS 2016 - [c6]Tobias Weiß, Madlen Diesing, Marco Krause, Kai Heinrich
Effective Visualizations of Energy Consumption in a Feedback System - A Conjoint Measurement Study. BIS 2016: 55-66 - 2015
- [c5]Kai Heinrich:
Integration von Topic Models und Netzwerkanalyse bei der Bestimmung des Kundenwertes. GeNeMe 2015: 277-284 - 2012
- [j1]Stefan Sommer, Andreas Schieber, Kai Heinrich
What is the Conversation About?: A Topic-Model-Based Approach for Analyzing Customer Sentiments in Twitter. Int. J. Intell. Inf. Technol. 8(1): 10-25 (2012) - [c4]Kai Heinrich, Andreas Hilbert, Martin Kersten:
Methoden für Trendanalysen im Web zur Unter-stützungdes Customer Relationship Management. MKWI 2012: 1145-1156 - 2011
- [c3]Andreas Schieber, Stefan Sommer, Andreas Hilbert, Kai Heinrich:
Analyzing customer sentiments in microblogs - A topic-model-based approach for Twitter datasets. AMCIS 2011 - [c2]Andreas Schieber, Stefan Sommer, Kai Heinrich, Andreas Hilbert:
Worüber reden die Kunden? - Ein modelbasierter Ansatz für die Analyse von Kundenmeinungen in Microblogs. GeNeMe 2011: 25-34 - [c1]Andreas Schieber, Kai Heinrich, Andreas Hilbert:
Analyse von Konsumentenmeinungen in Microblogs: Topic-based Opinion Mining. WSBI 2011: 50-57
Coauthor Index
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last updated on 2024-11-20 21:02 CET by the dblp team
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