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Ricardo Buettner

Researcher at FOM University of Applied Sciences for Economics and Management

Publications -  84
Citations -  1835

Ricardo Buettner is an academic researcher from FOM University of Applied Sciences for Economics and Management. The author has contributed to research in topics: Computer science & Convolutional neural network. The author has an hindex of 27, co-authored 63 publications receiving 1597 citations. Previous affiliations of Ricardo Buettner include University of Hohenheim.

Papers
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Journal ArticleDOI

Predicting user behavior in electronic markets based on personality-mining in large online social networks

TL;DR: A personality-based product recommender (PBPR) framework to analyze social media data in order to predict a user’s personality and to subsequently derive its personality- based product preferences is proposed.
Journal ArticleDOI

A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

TL;DR: In this article, a combination of thermographic off-axis imaging as data source and deep learning-based neural network architectures was used to detect printing defects such as delamination and splatter with an accuracy of 96.80%.
Book ChapterDOI

Cognitive Workload of Humans Using Artificial Intelligence Systems: Towards Objective Measurement Applying Eye-Tracking Technology

TL;DR: Applying eye-tracking technology, it is shown how the user’s cognitive workload can be measure more objectively by capturing eye movements and pupillary responses.
Proceedings ArticleDOI

Getting a Job via Career-Oriented Social Networking Sites: The Weakness of Ties

TL;DR: A substantial negative relationship between the number of contacts and job search success is found, which supports the experience of practitioners but contradicts scholarly findings.
Proceedings ArticleDOI

A Systematic Literature Review of Crowdsourcing Research from a Human Resource Management Perspective

TL;DR: From a human resource management perspective, the crowd sourcing literature included in top peer-reviewed journals and conferences is reviewed, and empirical and design-oriented research needs are identified.