Deep learning (DL) has solved a problem that a few years ago was thought to be intractable—
the automatic recognition of patterns in spatial and temporal data with an accuracy superior …

Clustering is a widely used unsupervised learning technique across data mining and
machine learning applications and finds frequent use in diverse fields ranging from …

This paper focuses on building a spatio-temporal AI inference engine for estimating hard
disk reliability. Most electronic systems such as hard disks routinely collect such reliability …

The Quantum Double Delta Swarm (QDDS) Algorithm is a networked, fully-connected novel
metaheuristic optimization algorithm inspired by the convergence mechanism to the center …

With the advent of pervasive cloud computing technologies, service reliability and
availability are becoming major concerns, especially as we start to integrate cyberphysical …

Clustering is a widely used unsupervised learning technique across data mining and
machine learning applications and finds frequent use in diverse fields ranging from …

With sensor fusion and data-driven approaches taking center stage in ubiquitous computing,
customized and application-specific optimization methods are increasingly important. The …

In this paper, we outline the derivation of a variant of the Quantum Double Delta Swarm
(QDDS) algorithm using a Chebyshev map driven chaotic component in the solution phase …