Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Predicting protein structure is both fascinating and formidable, playing a crucial role in
structure-based drug discovery and unraveling diseases with elusive origins. The Critical …

Automatic segmentation of salient objects in real-world images has gained increasing
interests owing to its popularity in diverse real-world applications, such as autonomous …

Medical audio classification for lung abnormality diagnosis is a challenging problem owing
to comparatively unstructured audio signals present in the respiratory sound clips. To tackle …

The recent breakthrough of deep learning based generative models has led to the escalated
generation of photo-realistic synthetic videos with significant visual quality. Automated …

In this research, we propose a variant of the Bare-Bones Particle Swarm Optimization
(BBPSO) algorithm for hyper-parameter selection and deep architecture generation for …

Still image human action recognition (HAR) is a challenging problem owing to limited
sources of information and large intra-class and small inter-class variations which requires …

Semantic segmentation is vital for many emerging surveillance applications, but current
models cannot be relied upon to meet the required tolerance, particularly in complex tasks …

Abstract Neural Architecture Search (NAS) methods are widely employed to address the
time-consuming and costly challenges associated with manual operation and design of …

Human action recognition can be applied in a multitude of fully diversified domains such as
active large-scale surveillance, threat detection, personal safety in hazardous environments …