@misc{811,
  author       = {{Böhl, Freda}},
  keywords     = {{E-Learning, eLearning}},
  pages        = {{60}},
  publisher    = {{Hochschule Ostwestfalen-Lippe}},
  title        = {{{eLearning in der Hochschullehre: Entwicklung eines Leitfadens für den Studiengang Medienproduktion}}},
  year         = {{2017}},
}

@inbook{4298,
  abstract     = {{In this paper, we present the current state-of-the-art of decision making (DM) and machine learning (ML) and bridge the two research domains to create an integrated approach of complex problem solving based on human and computational agents. We present a novel classification of ML, emphasizing the human-in-the-loop in interactive ML (iML) and more specific on collaborative interactive ML (ciML), which we understand as a deep integrated version of iML, where humans and algorithms work hand in hand to solve complex problems. Both humans and computers have specific strengths and weaknesses and integrating humans into machine learning processes might be a very efficient way for tackling problems. This approach bears immense research potential for various domains, e.g., in health informatics or in industrial applications. We outline open questions and name future challenges that have to be addressed by the research community to enable the use of collaborative interactive machine learning for problem solving in a large scale.}},
  author       = {{Robert, Sebastian and Büttner, Sebastian and Röcker, Carsten and Holzinger, Andreas}},
  booktitle    = {{Machine Learning for Health Informatics : State-of-the-Art and Future Challenges }},
  editor       = {{Holzinger, Andreas}},
  isbn         = {{978-3-319-50477-3 }},
  keywords     = {{Decision making, Reasoning, Interactive machine learning, Collaborative interactive machine learning}},
  pages        = {{357--376}},
  publisher    = {{Springer}},
  title        = {{{Reasoning Under Uncertainty: Towards Collaborative Interactive Machine Learning}}},
  doi          = {{10.1007/978-3-319-50478-0_18}},
  volume       = {{9605}},
  year         = {{2016}},
}

@inproceedings{2167,
  abstract     = {{Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry and politics: By applying new IT and new computer science solutions, production systems will become more adaptable, more resource ef- ficient and more user friendly. The analysis and diagnosis of such systems is a major part of this trend: Plants should detect automatically wear, faults and suboptimal configurations. This paper reflects the current state-of- the-art in diagnosis against the requirements of CPPSs, identifies three main gaps and gives application scenarios to outline first ideas for potential solutions to close these gaps.
}},
  author       = {{Niggemann, Oliver and Lohweg, Volker}},
  booktitle    = {{Twenty-Ninth Conference on Artificial Intelligence (AAAI-15)}},
  keywords     = {{Cyber-Physical Systems, Machine Learning, Diagnosis, Anomaly Detection}},
  title        = {{{On the Diagnosis of Cyber-Physical Production Systems - State-of-the-Art and Research Agenda}}},
  year         = {{2015}},
}

@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}},
}

@misc{9856,
  abstract     = {{According to the Bologna Accord in 2006 the study courses for architecture, urban planning and landscape planning at Kassel university were reformed to a bachelor and master education programme. New courses – so called “modules” were found. One of them “Wahrnehmung und Analyse von Räumen” – “landscape perception and analysis” – is an interdisciplinary course teaching and comparing three different perspectives – those of ecology, social science and landscape planning – on landscape. To manage a high number of students the e-learning platform “Moodle” is used. Also giving an introduction into GIS is a major part of the course. This article – after “landscape perception and analysis” started four years ago – gives an overview of the recent and future development of the course from a teachers perspective.}},
  author       = {{Leiner, Claas and Stemmer, Boris}},
  booktitle    = {{gis.Science}},
  issn         = {{2698-4571}},
  keywords     = {{Universitarian teaching, GIS, e-learning, bologna process}},
  number       = {{4}},
  pages        = {{105–110}},
  publisher    = {{Wichmann}},
  title        = {{{Teaching Landscape Planning - Landscape Perception and Analysis}}},
  year         = {{2011}},
}

@inproceedings{2087,
  abstract     = {{It is likely in real-world applications that only little data isavailable for training a knowledge-based system. We present a method forautomatically training the knowledge-representing membership functionsof a Fuzzy-Pattern-Classification system that works also when only littledata is available and the universal set is described insufficiently. Actually,this paper presents how the Modified-Fuzzy-Pattern-Classifier’s member-ship functions are trained using probability distribution functions.}},
  author       = {{Mönks, Uwe and Lohweg, Volker and Petker, Denis}},
  booktitle    = {{IPMU 2010 - International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems}},
  keywords     = {{Fuzzy Logic, Probability Theory, Fuzzy-Pattern-Classification, Machine Learning, Artificial Intelligence, Pattern Recognition}},
  publisher    = {{28 Jun 2010 - 02 July 2010, Dortmund, Germany}},
  title        = {{{Fuzzy-Pattern-Classifier Training with Small Data Sets}}},
  year         = {{2010}},
}