Decision forests, including Random Forests and Gradient Boosting Trees, have recently
demonstrated state-of-the-art performance in a variety of machine learning settings …

The capability to perform anomaly detection in a resource-constrained setting, such as an
edge device or a loaded server, is of increasing need due to emerging on-premises …

The escalating issue of land subsidence poses a critical threat to the economic prosperity of
Indonesia's North Coast in Central Java. This recurring phenomenon intensifies annual tidal …

Machine learning is becoming ubiquitous. From finance to medicine, machine learning
models are boosting decision-making processes and even outperforming humans in some …

Machine learning (ML) methods are widely proposed for security monitoring of Internet of
Things (IoT). However, these methods can be computationally expensive for resource …

Random forests use ensembles of decision trees to boost accuracy for machine learning
tasks. However, large ensembles slow down inference on platforms that process each tree …

Successful neuromodulation approaches to alter episodic memory require closed-loop
stimulation predicated on the effective classification of brain states. The practical …

Decision forest, including RandomForest, XGBoost, and LightGBM, is one of the most
popular machine learning techniques used in many industrial scenarios, such as credit card …

Tree ensembles, random forests and gradient boosted trees, are useful in resource-limited
machine learning deployments. However, traversing tree data structures is not cache …

Memory mapping enhances decision tree implementations by enabling constant-time
statistical inference, and is particularly effective when memory mapped tables fit in processor …