The use of deep learning models for forecasting the resource consumption patterns of SQL
queries have recently been a popular area of study. While these models have demonstrated …

The optimization of convolutional neural networks (CNN) generally refers to the
improvement of the inference process, making it as fast and precise as possible. While …

The digital industrial revolution calls for smart manufacturing plants, ie plants that include
sensors and vision systems accompanied with artificial intelligence and advanced data …

Abstract Convolutional Neural Networks (CNNs) are leveraged for a wide range of satellite
imagery information extraction tasks. However, for tasks which seek to estimate aggregated …

Current convolutional neural networks (CNNs) are not designed for large scientific images
with rich multi-scale features, such as in satellite and microscopy domain. A new phase of …

Bone age is a reliable metric for determining the level of biological maturity of children and
adolescents. Its assessment is a crucial part of the diagnosis of a variety of pediatric …

Image classification with deep neural networks is typically restricted to images of small
dimensionality such as 224 x 244 in Resnet models [24]. This limitation excludes the 4000 x …

Query cost estimation is a classical task for database management. Recently, researchers
have applied AI-driven methods to implement query cost estimation for achieving high …

Convolutional Neural Network (CNN) has been widely used in medical image processing.
Histopathology is one of modality or images for a pathologist to analyze the status of cancer …

State-of-the-art object detectors need to be trained with a wide variety of data in order to
perform well in real-world problems. Training-data-diversity is very important to achieve …