In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …

Recently, a number of classification techniques have been introduced. However, processing
large dataset in a reasonable time has become a major challenge. This made classification …

Mobile technologies offer new opportunities for prospective, high resolution monitoring of
long-term health conditions. The opportunities seem of particular promise in psychiatry …

Handwritten character recognition is a computer-vision-system problem that is still critical
and challenging in many computer-vision tasks. With the increased interest in handwriting …

Recently, deep learning techniques demonstrated efficiency in building better performing
machine learning models which are required in the field of offline Arabic handwriting …

Nowadays, convolutional neural networks (CNNs) have achieved tremendous performance
in many machine learning areas. However, using a large number of parameters leads to the …

Convolutional neural networks (CNNs) are a promising tool for solving real-world problems.
However, successful CNNs often require a large number of parameters, which leads to a …

Abstract Deep Neural Networks (DNNs) have been vastly and successfully employed in
various artificial intelligence and machine learning applications (eg, image processing and …

The distillation technique helps transform cumbersome neural networks into compact
networks so that models can be deployed on alternative hardware devices. The main …

Offline handwriting recognition with deep neural networks is usually limited to words or lines
due to large computational costs. In this paper, a less computationally expensive full page …