@inproceedings{TomicDrenjanacLazendicetal.,
  author    = {Tomic, Dana Kathrin and Drenjanac, Domagoj and Lazendic, Goran and H{\"o}rmann, Sandra and Handler, Franz and W{\"o}ber, Wilfried and Schulmeister, Klemens and Otte, Marcel and Auer, Wolfgang},
  title     = {Semantic Services for Adaptive Processes in Livestock Farming},
  series = {International Conference of Agricultural Engineering (AgEng 2014)},
  booktitle = {International Conference of Agricultural Engineering (AgEng 2014)},
  pages     = {8},
  subject      = {Agriculture},
  language  = {en}
}
@article{TomicDrenjanacLazendicetal.,
  author    = {Tomic, Dana Kathrin and Drenjanac, Domagoj and Lazendic, Goran and H{\"o}rmann, Sandra and Handler, Franz and W{\"o}ber, Wilfried and Aschauer, Christian and Auer, Wolfgang},
  title     = {Ontologies and semantic services for process optimization in agricultural production},
  series = {e\&i Elektrotechnik und Informationstechnik},
  journal   = {e\&i Elektrotechnik und Informationstechnik},
  subject      = {Semantics},
  language  = {en}
}
@article{KamravamaneshPflueglNischkaueretal.,
  author    = {Kamravamanesh, Donya and Pfl{\"u}gl, Stefan and Nischkauer, Winfried and Limbeck, Andreas and Lackner, Maximilian and Herwig, Christoph},
  title     = {Photosynthetic poly-β-hydroxybutyrate accumulation in unicellular cyanobacterium Synechocystis sp. PCC 6714},
  series = {AMB Express},
  volume    = {143},
  journal   = {AMB Express},
  number    = {7},
  subject      = {Bacteria},
  language  = {en}
}
@misc{KamravamaneshPflueglLackneretal.,
  author    = {Kamravamanesh, Donya and Pfl{\"u}gl, Stefan and Lackner, Maximilian and Herwig, Christoph},
  title     = {Photosynthetic poly-β-hydroxybutyrate accumulation in unicellular cyanobacterium Synechocystis sp. PCC 6714},
  subject      = {Bacteria},
  language  = {en}
}
@book{LacknerGrabowStadler,
  author    = {Lackner, Maximilian and Grabow, Wilhelm and Stadler, Philipp},
  title     = {Handbook of Online and Near-real-time Methods in Microbiology},
  publisher      = {Fachhochschule Technikum Wien},
  subject      = {Microbiology},
  language  = {en}
}
@misc{Lackner,
  author    = {Lackner, Maximilian},
  title     = {PBAT - A versatile bioplastics},
  subject      = {Bioplastics},
  language  = {en}
}
@book{OrsolitsLackner,
  author    = {Orsolits, Horst and Lackner, Maximilian},
  title     = {Virtual Reality und Augmented Reality in der Digitalen Produktion},
  publisher = {Gabler},
  isbn      = {978-3-658-29008-5},
  publisher      = {Fachhochschule Technikum Wien},
  subject      = {Virtual Reality},
  language  = {de}
}
@book{Lackner,
  author    = {Lackner, Maximilian},
  title     = {Chemical Engineering Vocabulary: Bilingual},
  publisher      = {Fachhochschule Technikum Wien},
  subject      = {Chemical Engineering},
  language  = {en}
}
@article{SpitzerSonnleitnerKempeLackner,
  author    = {Spitzer-Sonnleitner, Birgit and Kempe, Andre and Lackner, Maximilian},
  title     = {Influence of halide solutions on collagen networks - measurements of physical properties by atomic force microscopy (AFM)},
  series = {Influence of halide solutions on collagen networks},
  journal   = {Influence of halide solutions on collagen networks},
  subject      = {Collagen Networks},
  language  = {en}
}
@article{WoeberMehnenCurtoetal.,
  author    = {W{\"o}ber, Wilfried and Mehnen, Lars and Curto, Manuel and Dias Tibihika, Papius and Tesfaye, Genanaw and Meimberg, Harald},
  title     = {Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning},
  series = {Applied Sciences},
  volume    = {2022},
  journal   = {Applied Sciences},
  number    = {12(6), 3158},
  pages     = {26},
  abstract  = {Abstract: The biological investigation of a population's shape diversity using digital images is typi- cally reliant on geometrical morphometrics, which is an approach based on user-defined landmarks. In contrast to this traditional approach, the progress in deep learning has led to numerous applications ranging from specimen identification to object detection. Typically, these models tend to become black boxes, which limits the usage of recent deep learning models for biological applications. However, the progress in explainable artificial intelligence tries to overcome this limitation. This study compares the explanatory power of unsupervised machine learning models to traditional landmark-based approaches for population structure investigation. We apply convolutional autoencoders as well as Gaussian process latent variable models to two Nile tilapia datasets to investigate the latent structure using consensus clustering. The explanatory factors of the machine learning models were extracted and compared to generalized Procrustes analysis. Hypotheses based on the Bayes factor are formulated to test the unambiguity of population diversity unveiled by the machine learning models. The findings show that it is possible to obtain biologically meaningful results relying on unsupervised machine learning. Furthermore we show that the machine learning models unveil latent structures close to the true population clusters. We found that 80\% of the true population clusters relying on the convolutional autoencoder are significantly different to the remaining clusters. Similarly, 60\% of the true population clusters relying on the Gaussian process latent variable model are significantly different. We conclude that the machine learning models outperform generalized Procrustes analysis, where 16\% of the population cluster was found to be significantly different. However, the applied machine learning models still have limited biological explainability. We recommend further in-depth investigations to unveil the explanatory factors in the used model. Keywords: generalized procrustes analysis; machine learning; convolutional autoencoder; Gaussian process latent variable models},
  subject      = {generalized procrustes analysis},
  language  = {en}
}