Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

Higher dimensional data such as video and 3D are the leading edge of multimedia retrieval
and computer vision research. In this survey, we give a comprehensive overview and key …

Deep learning has recently seen rapid development and received significant attention due
to its state-of-the-art performance on previously-thought hard problems. However, because …

Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various
challenging machine learning problems such as image recognition. Although these methods …

This work attempts to address two fundamental questions about the structure of the
convolutional neural networks (CNN):(1) why a nonlinear activation function is essential at …

This chapter focuses on describing how the human‐centered design (HCD) process can be
revisited and expanded in an artificial intelligence (AI) context, proposing a methodological …

Traditional authentication systems use alphanumeric or graphical passwords, or token-
based techniques that require “something you know and something you have”. The …

This paper introduces a framework for how to appropriately adopt and adjust machine
learning (ML) techniques used to construct electrocardiogram (ECG)-based biometric …

Some research topics belonging to classification problems in the nuclear industry, such as
fault diagnosis and accident identification, can be solved by feature extraction and …

Embeddings map** high-dimensional discrete input to lower-dimensional continuous
vector spaces have been widely adopted in machine learning applications as a way to …