The paper proposes an approach for estimating the rectal temperature of dairy cows based on the non-invasive real-time monitoring of their respiration rates and the temperature-humidity index (THI) of the environment, combined with the analysis of infrared images. We use multimodal machine learning for the joint processing (fusion) of these different types of data. The implementation is performed using a new open source AutoML Python module named AutoGluon. After training and optimizing three different regression models (a neural network and two powerful boosting algorithms), it reduces the variance of the result using level 2 stacking. The evaluation metrics we work with are the mean absolute error, MAE, and the coefficient of determination, $R^2$. For a sample of 295 studied animals, a weighted ensemble provides quite decent results: $R^2=0.73$ and MAE $\approx 0.1$ °C. For another sample of 118 cows, we additionally use the pulse rate as a predictor and we achieve $R^2=0.65$, MAE $\approx 0.2$ °C. The maximal error is almost $1$ °C due to outliers, but the median absolute error in both cases is significantly lower: MedAE $<0.1$ °C, with the standard deviations respectively being $0.{118}^{°}$ and $0.{137}^{°}$. These encouraging results give us confidence that tabular and visual data fusion in ML models has great potential for the advancement of non-invasive real-time monitoring and early diagnostics methods.

Paper presents application of a recently proposed approach for multidimensional data clustering to data received from a microphone array antenna. The accumulated sound pressure at each point (a microphone in the array) is used to create “sound picture” of the observed by the microphone antenna area. Features for classification are extracted using overlapping receptive fields based on the model of direction selective cells in the middle temporal (MT) cortex. Next the clustering procedure using Echo state network and subtractive clustering algorithm is applied to separate receptive fields in proper number of classes. The obtained results are compared with the sonograms created by the original software of the producer of microphone array.