TY  - CHAP
A1  - Kaniusas, Eugenijus
A1  - Pfützner, Helmut
A1  - Mehnen, Lars
A1  - Kosel, Jürgen
A1  - Tellez-Blanco, Juan C.
T1  - Adaptive measurements of blood pressure changes using magnetic sensor and ECG
T2  - Il-oji metine Konferencijy Pranesimu tezes
KW  - Blood Pressure
KW  - Magnetics
KW  - Sensor
KW  - Biomedicine
Y1  - 2019
SP  - 29
EP  - 29
ER  - 
TY  - CHAP
A1  - Kaniusas, Eugenijus
A1  - Pfützner, Helmut
A1  - Mehnen, Lars
A1  - Kosel, Jürgen
A1  - Tellez-Blanco, Juan C.
T1  - Biomedical Applicability of Magnetoelastic Bilayer Sensors
T2  - Proceedings of the 11th International Symposium on Applied Electromagnetics & Mechanics
KW  - Magnetism
KW  - Biomedicine
KW  - Sensor
Y1  - 2019
SP  - 236
EP  - 237
ER  - 
TY  - CHAP
A1  - Mehnen, Lars
A1  - Kaniusas, Eugenijus
T1  - The SSETI Knowledge Base System
T2  - Proceedings of the AMSAT-UK 21st Annual Colloquium 2006
KW  - Knowledge Base
Y1  - 2018
SP  - 61
EP  - 62
ER  - 
TY  - CHAP
A1  - Mehnen, Lars
A1  - Kaniusas, Eugenijus
A1  - Pfützner, Helmut
T1  - Magnetostrictive Skin Sensor for Apnea Detection
T2  - Schlafmedizin im dritten Jahrtausend
KW  - Magnetics
KW  - Sensor
Y1  - 2019
SP  - 37
EP  - 38
ER  - 
TY  - CHAP
A1  - Krell, Christian
A1  - Mehnen, Lars
A1  - Leiss, Elisabeth
A1  - Pfützner, Helmut
T1  - Rotational Single Sheet Testing on Samples of Arbitrary Size and Shape
T2  - Proceedings of 1 and 2-dimensional Measurement and Testing, Vienna (Austria)
KW  - Testing
Y1  - 2019
SP  - 96
EP  - 103
ER  - 
TY  - JOUR
A1  - Wöber, Wilfried
A1  - Mehnen, Lars
A1  - Sykacek, Peter
A1  - Meimberg, Harald
T1  - Investigating Explanatory Factors of Machine Learning Models for Plant Classification
JF  - Plants
N2  - 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
KW  - deep learning
KW  - machine learning
KW  - plant leaf morphometrics
KW  - explainable AI
Y1  - 
VL  - 2021
IS  - 10(12):2674
ER  - 
TY  - CHAP
A1  - Kaniusas, Eugenijus
A1  - Pfützner, Helmut
A1  - Mehnen, Lars
A1  - Tellez-Blanco, Juan C.
A1  - Kosel, Jürgen
T1  - Kraujo Spaudimo Kitimo Matavimas Magnetiniu Sensoriumo
T2  - Il-oji metine MU Psychofiziologijos ir reabilitacijos instituto Konferencioja - Pranemisu tezes
KW  - Magnetics
KW  - Sensor
Y1  - 2019
SP  - 29
EP  - 29
ER  - 
TY  - CHAP
A1  - Krell, Christian
A1  - Mehnen, Lars
A1  - Kaniusas, Eugenijus
A1  - Leiss, Elisabeth
A1  - Pfützner, Helmut
T1  - Effects of stress on permeability, losses and magnetostriction
T2  - Proceedings of 1 and 2-dimensional Measurement and Testing, Vienna (Austria)
KW  - Material Stress
KW  - Magnetism
Y1  - 2019
SP  - 242
EP  - 247
ER  -