The use of precision livestock has increased due to the need to improve the efficiency and
productivity required by the high food demand. Monitoring cattle behavior is a fundamental …

Abstract In recent years, Deep Learning (DL), such as the algorithms of Convolutional
Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial …

Augmented Reality/Virtual Reality (AR/VR) are the next step in the evolution of ubiquitous
computing after personal computers to mobile devices. Applications of AR/VR continue to …

A new simple decision-tree (DT) algorithm was developed using the data from a neck-
mounted accelerometer for real-time classification of feeding and ruminating behaviours of …

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to
optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity …

Livestock feeding behaviour is an influential research area in animal husbandry and
agriculture. In recent years, there has been a growing interest in automated systems for …

Monitoring livestock behaviour can be a useful way to improve farm animal management
and to detect individual health events. The use of automated systems that predict several …

We study the classification of animal behavior from accelerometry data using various
recurrent neural network (RNN) models. RNNs have extensively been employed to classify …

Time series classification and class imbalance problem are two common issues in a
multitude of real-life scenarios. This paper simultaneously explores both issues with deep …

In this paper, a sequential deep neural network in conjunction with a joint time-frequency
domain data representation is explored for the problem of cattle behaviour classification …