This overview gravitates on research achievements that have recently emerged from the
confluence between Big Data technologies and bio-inspired computation. A manifold of …

Many daily applications are generating massive amount of data in the form of stream at an
ever higher speed, such as medical data, clicking stream, internet record and banking …

The aim of the present study is to solve the singular second-order functional differential
model with the development of neuro-swarm intelligent computing solver ANN–PSO–SQP …

Mining non-stationary stream is a challenging task due to its unique property of infinite
length and dynamic characteristics let alone the issues of concept drift, concept evolution …

At present, concept drift in the nonstationary data stream is showing trends with different
speeds and different degrees of severity, which has brought great challenges to many fields …

The fractional neuro-evolution-based intelligent computing has substantial potential to solve
fractional order systems represented with Lane–Emden equation arising in astrophysics …

Developing effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …

Data clustering divides the datasets into different groups. Incremental Density-Based Spatial
Clustering of Applications with Noise (DBSCAN) is a famous density-based clustering …

Deep learning-based approaches have gained popularity for many applications in recent
years and have become the state-of-the-art method in machine learning applications …

The challenges raised by the massive data are being managed by the community through
the advancements of infrastructure and algorithms, and now the processing of fast data is …