The advancements in neural networks and the on-demand need for accurate and near real-
time Speech Emotion Recognition (SER) in human–computer interactions make it …

Autonomous vehicles were experiencing rapid development in the past few years. However,
achieving full autonomy is not a trivial task, due to the nature of the complex and dynamic …

Advances in biological and medical technologies have been providing us explosive
volumes of biological and physiological data, such as medical images …

It is increasingly recognized that artificial intelligence has been touted as a new mobile.
Because of the high volume of data that being generated by devices, sensors and social …

Early detection is critical for effective management of Alzheimer's disease (AD) and
screening for mild cognitive impairment (MCI) is common practice. Among several deep …

Abstract Cutting edge Deep Learning (DL) techniques have been widely applied to areas
like image processing and speech recognition so far. Likewise, some DL work has been …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

This paper introduces WILDCAT, a deep learning method which jointly aims at aligning
image regions for gaining spatial invariance and learning strongly localized features. Our …

Deep networks have achieved excellent performance in learning representation from visual
data. However, the supervised deep models like convolutional neural network require large …

Current high-quality object detection approaches use the scheme of salience-based object
proposal methods followed by post-classification using deep convolutional features. This …