Detection of anomalous behaviors in data centers is crucial to predictive maintenance and
data safety. With data centers, we mean any computer network that allows users to transmit …

Accurately estimating a battery's state of health (SOH) helps prevent battery-powered
applications from failing unexpectedly. With the superiority of reducing the data requirement …

Smartphones enable to collect large data streams about phone calls that, once combined
with Computational Intelligence techniques, bring great potential for improving the …

Incomplete data are frequently encountered and bring difficulties when it comes to further
processing. The concepts of granular computing (GrC) help deliver a higher level of …

The control of complex nonlinear systems (such as autonomous vehicles) usually requires
models which might be unavailable or inaccurate. In this paper, a novel data-driven Model …

This article proposes a data‐stream‐driven event‐triggered control strategy using evolving
fuzzy models learned by granulation of input–output samples of nonlinear systems with …

This paper proposes a novel Error Based Evolving Takagi–Sugeno Fuzzy Model (EBeTS)
and a new data-driven approach to fault prognostics based on that fuzzy model. The …

Power-quality disturbances lead to several drawbacks such as limitation of the production
capacity, increased line and equipment currents, and consequent ohmic losses; higher …

Log-based predictive maintenance of computing centers is a main concern regarding the
worldwide computing grid that supports the CERN (European Organization for Nu-clear …

Rule-based models have become a popular way to represent and analyze the main
knowledge residing in data because of the increasing complexity and uncertainty. For …