Scalability and Robustness of Feed Yard Mortality Prediction Modeling to Improve Profitability

Cattle feed yards routinely track and collect data for individual calves throughout the feeding period. Using such operational data from nine U.S. feed yards for the years 2016-2019, we evaluated the scalability and economic viability of using machine learning classifier predicted mortality as a culling decision aid. The expected change in net return per head when using the classifier predictions as a culling aid as compared to the status quo culling protocol for calves having been pulled at least once for bovine respiratory disease was simulated. This simulated change in net return ranged from - $1.61 to$19.46/head. Average change in net return and standard deviation for the nine feed yards in this study was $6.31/head and$7.75/head, respectively

ContrasGAN : unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning

Human Activity Recognition (HAR) makes it possible to drive applications directly from embedded and wearable sensors. Machine learning, and especially deep learning, has made significant progress in learning sensor features from raw sensing signals with high recognition accuracy. However, most techniques need to be trained on a large labelled dataset, which is often difficult to acquire. In this paper, we present ContrasGAN, an unsupervised domain adaptation technique that addresses this labelling challenge by transferring an activity model from one labelled domain to other unlabelled domains. ContrasGAN uses bi-directional generative adversarial networks for heterogeneous feature transfer and contrastive learning to capture distinctive features between classes. We evaluate ContrasGAN on three commonly-used HAR datasets under conditions of cross-body, cross-user, and cross-sensor transfer learning. Experimental results show a superior performance of ContrasGAN on all these tasks over a number of state-of-the-art techniques, with relatively low computational cost.PostprintPeer reviewe

Modeling Skewed Class Distributions by Reshaping the Concept Space

We introduce an approach to learning from imbalanced class distributions that does not change the underlying data distribution. The ICC algorithm decomposes majority classes into smaller sub-classes that create a more balanced class distribution. In this paper, we explain how ICC can not only addressthe class imbalance problem but may also increase the expressive power of the hypothesis space. We validate ICC and analyze alternative decomposition methods on well-known machine learning datasets as well as new problems in pervasive computing. Our results indicate that ICC performs as well or better than existing approaches to handling class imbalance