Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …

This study explores digital business transformation through the lens of four emerging
technology fields: artificial intelligence, blockchain, cloud and data analytics (ie, ABCD) …

The use of immersive technologies has changed the consumption environment in which
retailers provide services. We present findings from a study designed to investigate …

Infrared imaging-based machine vision (IRMV) is the technology used to automatically
inspect, detect, and analyse infrared images (or videos) obtained by recording the intensity …

This study investigates the factors that build resistance and attitude towards AI voice
assistants (AIVA). A theoretical model is proposed using the dual-factor framework by …

A recurring problem faced when training neural networks is that there is typically not enough
data to maximize the generalization capability of deep neural networks. There are many …

Artificial intelligence (AI) has become an integral part of every industry. With the emergence
of big data, the industries, and more especially textile and apparel (T&A) industry, are on the …

The performance of deep convolution neural networks will be further enhanced with the
expansion of the training data set. For the image classification tasks, it is necessary to …

In this article, we argue that journalism studies, and particularly research focused on
automated journalism, has much to learn from Human-Machine Communication (HMC), an …