Animal behavior, as one of the most crucial indicators of animal health and welfare, provides
rich insights into animal physical and mental states. Automated animal activity recognition …

Conservation of biodiversity is critical for the coexistence of humans and the sustenance of
other living organisms within the ecosystem. Identification and prioritization of specific …

We develop an end-to-end deep-neural-network-based algorithm for classifying animal
behavior using accelerometry data on the embedded system of an artificial intelligence of …

Bio-telemetry from small tags attached to animals is one of the principal methods for
studying the ecology and behaviour of wildlife. The field has constantly evolved over the last …

During disease or toxin challenges, the behavioral activities of grazing animals alter in
response to adverse situations, potentially providing an indicator of their welfare status …

The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-
based applications that efficiently process and classify the data acquired by the resource …

Transfer learning has proven to be a valuable technique for deploying machine learning
models on edge devices and embedded systems. By leveraging pre-trained models and fine …

Intelligent edge vision tasks encounter the critical challenge of ensuring power and latency
efficiency due to the typically heavy computational load they impose on edge platforms. This …

The growth of Artificial Intelligence (AI) and the Internet of Things (IoT) sensors has given
rise to a synergistic paradigm known as AIoT, wherein AI functions as the decision-maker …

Nowadays, microcontrollers are very common in a wide range of applications. Integrating
Machine Learning with Embedded Systems is essential and should be provided at a low …