@misc{12873,
  abstract     = {{Reliable Banknote Authentication is critical for economic stability. Regarding everyday use, recent studies implemented successful techniques using banknote images taken by mobile phone cameras. One challenge in mobile banknote authentication is that it is impossible to collect images by all series/brands of mobile phones. In this study, classification models are implemented that are able to generalize to the samples from a wide number of mobile phone series even though they are trained with samples from a small group of series. Existing state-of-the-art banknote authentication approaches train a separate model per sub-image of a banknote, using the extracted features of that sub-image. A new approach that trains a single global model on the concatenated features of all the sub-images is presented. Furthermore, ensemble models that combine Linear Discriminant Analysis and Deep Neural Networks are employed in order to maximize the accuracy. Implemented techniques were able to reach up to F1-score of 0.99914 on a Euro banknote data set which contain images from 16 different mobile-phone series. The results also indicate that new global model approach can improve the accuracy of the existing banknote authentication techniques in case of model training with images from restricted/incomplete phone series and brands.}},
  author       = {{Sürmeli, Baris Gün and Gillich, Eugen and Dörksen, Helene}},
  booktitle    = {{Artificial Neural Networks and Machine Learning - ICANN 2023}},
  editor       = {{Iliadis,  Lazaros }},
  isbn         = {{978-3-031-44209-4}},
  issn         = {{1611-3349}},
  location     = {{Heraklion, GREECE}},
  pages        = {{332--343}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Generalisation Approach for Banknote Authentication by Mobile Devices Trained on Incomplete Samples}}},
  doi          = {{10.1007/978-3-031-44210-0_27}},
  volume       = {{14255}},
  year         = {{2023}},
}

@book{4336,
  abstract     = {{Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems.

The very successful synergistic combination of methodologies and approaches from Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions for the vision to support human intelligence with machine learning.

The papers selected for this volume focus on hot topics in smart health; they discuss open problems and future challenges in order to provide a research agenda to stimulate further research and progress.}},
  editor       = {{Holzinger, Andreas and Röcker, Carsten and Ziefle, Martina}},
  isbn         = {{978-3-319-16225-6}},
  issn         = {{1611-3349}},
  keywords     = {{HCI, ambient assisted living, big data, computational intelligence, context awareness, data centric medicine, decision support, interactive data mining, keyword detection, knoweldge bases, knoweldge discovery, machine learning, medical decision support, medical informatics, natural language processing, pervasive health, smart home, ubiquitous computing, visualization, wearable sensors}},
  pages        = {{275}},
  publisher    = {{Springer}},
  title        = {{{Smart Health: Open Problems and Future Challenges}}},
  doi          = {{10.1007/978-3-319-16226-3}},
  volume       = {{8700}},
  year         = {{2015}},
}

@inbook{5883,
  abstract     = {{The study of users’ emotional behavior as an important aspect of the user experience has been receiving increasing attention for the past few years. In this paper we discuss the multi-component character of emotions and its consideration in the area of human-technology interaction. Based on the approach proposed by Scherer [1], various aspects of emotions were investigated in an interactive context: subjective feelings, physiological activation, motor expression, cognitive appraisals, and behavioral tendencies. In an experiment emotion-related changes were detected for a number of emo tional components by using a variety of methods: rating scales for sub jec tive feelings, electromyography of facial muscles, heart rate, electrodermal activity, perfor mance data and questionnaires on cognitive appraisals. Results are discussed with respect to the correlation between the components and their associated methods. We suggest that a combination of methods provides a comprehensive basis for analyzing emotions as an aspect of the user experience.}},
  author       = {{Mahlke, Sascha and Minge, Michael}},
  booktitle    = {{Affect and Emotion in HCI}},
  editor       = {{Peter, Christian and Beale, Russell}},
  isbn         = {{978-3-540-85098-4}},
  issn         = {{1611-3349}},
  pages        = {{51--62}},
  publisher    = {{Springer}},
  title        = {{{Consideration of Multiple Components of Emotions in Human-Technology Interaction}}},
  doi          = {{10.1007/978-3-540-85099-1_5}},
  volume       = {{4868}},
  year         = {{2008}},
}