Abstract Machine learning has been the corner stone in analysing and extracting information
from data and often a problem of missing values is encountered. Missing values occur …

The amount of data produced and communicated over the Internet is significantly increasing,
thereby creating challenges for the organizations that would like to reap the benefits from …

There is no doubt that big data are now rapidly expanding in all science and engineering
domains. While the potential of these massive data is undoubtedly significant, fully making …

In this paper, we analyze the convergence of the alternating direction method of multipliers
(ADMM) for minimizing a nonconvex and possibly nonsmooth objective function, ϕ (x_0 …

This paper investigates big data analytics research and application in supply chain
management between 2010 and 2016 and provides insights to industries. In recent years …

The advent of connected devices and omnipresence of Internet have paved way for
intruders to attack networks, which leads to cyber-attack, financial loss, information theft in …

In data analytics, missing data is a factor that degrades performance. Incorrect imputation of
missing values could lead to a wrong prediction. In this era of big data, when a massive …

Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

To improve the efficiency of big data feature learning, the paper proposes a privacy
preserving deep computation model by offloading the expensive operations to the cloud …

One of the major challenges of solving Big Data optimization problems via traditional multi-
objective evolutionary algorithms (MOEAs) is their high computational costs. This issue has …