@inproceedings{KaniusasPfuetznerMehnenetal.,
  author    = {Kaniusas, Eugenijus and Pf{\"u}tzner, Helmut and Mehnen, Lars and Kosel, J{\"u}rgen and Tellez-Blanco, Juan C.},
  title     = {Adaptive measurements of blood pressure changes using magnetic sensor and ECG},
  series = {Il-oji metine Konferencijy Pranesimu tezes},
  booktitle = {Il-oji metine Konferencijy Pranesimu tezes},
  pages     = {29 -- 29},
  subject      = {Blood Pressure},
  language  = {en}
}
@inproceedings{KaniusasPfuetznerMehnenetal.,
  author    = {Kaniusas, Eugenijus and Pf{\"u}tzner, Helmut and Mehnen, Lars and Kosel, J{\"u}rgen and Tellez-Blanco, Juan C.},
  title     = {Biomedical Applicability of Magnetoelastic Bilayer Sensors},
  series = {Proceedings of the 11th International Symposium on Applied Electromagnetics \& Mechanics},
  booktitle = {Proceedings of the 11th International Symposium on Applied Electromagnetics \& Mechanics},
  pages     = {236 -- 237},
  subject      = {Magnetism},
  language  = {en}
}
@inproceedings{MehnenKaniusas,
  author    = {Mehnen, Lars and Kaniusas, Eugenijus},
  title     = {The SSETI Knowledge Base System},
  series = {Proceedings of the AMSAT-UK 21st Annual Colloquium 2006},
  booktitle = {Proceedings of the AMSAT-UK 21st Annual Colloquium 2006},
  pages     = {61 -- 62},
  subject      = {Knowledge Base},
  language  = {en}
}
@inproceedings{MehnenKaniusasPfuetzner,
  author    = {Mehnen, Lars and Kaniusas, Eugenijus and Pf{\"u}tzner, Helmut},
  title     = {Magnetostrictive Skin Sensor for Apnea Detection},
  series = {Schlafmedizin im dritten Jahrtausend},
  booktitle = {Schlafmedizin im dritten Jahrtausend},
  pages     = {37 -- 38},
  subject      = {Magnetics},
  language  = {en}
}
@inproceedings{KrellMehnenLeissetal.,
  author    = {Krell, Christian and Mehnen, Lars and Leiss, Elisabeth and Pf{\"u}tzner, Helmut},
  title     = {Rotational Single Sheet Testing on Samples of Arbitrary Size and Shape},
  series = {Proceedings of 1 and 2-dimensional Measurement and Testing, Vienna (Austria)},
  booktitle = {Proceedings of 1 and 2-dimensional Measurement and Testing, Vienna (Austria)},
  pages     = {96 -- 103},
  subject      = {Testing},
  language  = {en}
}
@article{WoeberMehnenSykaceketal.,
  author    = {W{\"o}ber, Wilfried and Mehnen, Lars and Sykacek, Peter and Meimberg, Harald},
  title     = {Investigating Explanatory Factors of Machine Learning Models for Plant Classification},
  series = {Plants},
  volume    = {2021},
  journal   = {Plants},
  number    = {10(12):2674},
  pages     = {20},
  abstract  = {Recent progress in machine learning and deep learning has enabled the implementation of plant and crop detection using systematic inspection of the leaf shapes and other morphological characters for identification systems for precision farming. However, the models used for this approach tend to become black-box models, in the sense that it is difficult to trace characters that are the base for the classification. The interpretability is therefore limited and the explanatory factors may not be based on reasonable visible characters. We investigate the explanatory factors of recent machine learning and deep learning models for plant classification tasks. Based on a Daucus carota and a Beta vulgaris image data set, we implement plant classification models and compare those models by their predictive performance as well as explainability. For comparison we implemented a feed forward convolutional neuronal network as a default model. To evaluate the performance, we trained an unsupervised Bayesian Gaussian process latent variable model as well as a convolutional autoencoder for feature extraction and rely on a support vector machine for classification. The explanatory factors of all models were extracted and analyzed. The experiments show, that feed forward convolutional neuronal networks (98.24\% and 96.10\% mean accuracy) outperforms the Bayesian Gaussian process latent variable pipeline (92.08\% and 94.31\% mean accuracy) as well as the convolutional autoenceoder pipeline (92.38\% and 93.28\% mean accuracy) based approaches in terms of classification accuracy, even though not significant for Beta vulgaris images. Additionally, we found that the neuronal network used biological uninterpretable image regions for the plant classification task. In contrast to that, the unsupervised learning models rely on explainable visual characters. We conclude that supervised convolutional neuronal networks must be used carefully to ensure biological interpretability. We recommend unsupervised machine learning, careful feature investigation, and statistical feature analysis for biological applications. View Full-Text Keywords: deep learning; machine learning; plant leaf morphometrics; explainable AI},
  subject      = {deep learning},
  language  = {en}
}
@inproceedings{KaniusasPfuetznerMehnenetal.,
  author    = {Kaniusas, Eugenijus and Pf{\"u}tzner, Helmut and Mehnen, Lars and Tellez-Blanco, Juan C. and Kosel, J{\"u}rgen},
  title     = {Kraujo Spaudimo Kitimo Matavimas Magnetiniu Sensoriumo},
  series = {Il-oji metine MU Psychofiziologijos ir reabilitacijos instituto Konferencioja - Pranemisu tezes},
  booktitle = {Il-oji metine MU Psychofiziologijos ir reabilitacijos instituto Konferencioja - Pranemisu tezes},
  pages     = {29 -- 29},
  subject      = {Magnetics},
  language  = {mul}
}
@inproceedings{KrellMehnenKaniusasetal.,
  author    = {Krell, Christian and Mehnen, Lars and Kaniusas, Eugenijus and Leiss, Elisabeth and Pf{\"u}tzner, Helmut},
  title     = {Effects of stress on permeability, losses and magnetostriction},
  series = {Proceedings of 1 and 2-dimensional Measurement and Testing, Vienna (Austria)},
  booktitle = {Proceedings of 1 and 2-dimensional Measurement and Testing, Vienna (Austria)},
  pages     = {242 -- 247},
  subject      = {Material Stress},
  language  = {en}
}