Building energy prediction plays a vital role in develo** a model predictive controller for
consumers and optimizing energy distribution plan for utilities. Common approaches for …

Due to the massive data sets available for drug candidates, modern drug discovery has
advanced to the big data era. Central to this shift is the development of artificial intelligence …

This paper presents a novel cost aggregation network, called Volumetric Aggregation with
Transformers (VAT), for few-shot segmentation. The use of transformers can benefit …

We investigated the effect of different training scenarios on predicting the (retro) synthesis of
chemical compounds using text-like representation of chemical reactions (SMILES) and …

This paper describes a testing methodology for quantitatively assessing the risk that rare or
unique training-data sequences are unintentionally memorized by generative sequence …

Abstract The Internet of Things (IoT) and the relevant technologies have had a significant
impact on smart farming as a major sub-domain within the field of agriculture. Modern …

There is an increasing interest in creating high-resolution 3D subsurface geo-models using
multisource retrieved data, ie, borehole, geophysical techniques, geological maps, and rock …

Accurate, high spatial and temporal resolution water quality monitoring in inland waters is
vital for environmental management. However, water quality monitoring in inland waters by …

Artificial neural networks had their first heyday in molecular informatics and drug discovery
approximately two decades ago. Currently, we are witnessing renewed interest in adapting …

Machine learning is an important task for learning artificial neural networks, and we find in
the learning one of the common problems of learning the Artificial Neural Network (ANN) is …