A popular unsupervised learning method, known as clustering, is extensively used in data
mining, machine learning and pattern recognition. The procedure involves grouping of …

As a newly emerging technology, deep learning (DL) is a very promising field in big data
applications. Remote sensing often involves huge data volumes obtained daily by numerous …

Retail companies recognize the need to analyze and predict their sales and customer
behavior against their products and product categories. Our study aims to help retail …

Recently, increasingly large amounts of data are generated from a variety of sources.
Existing data processing technologies are not suitable to cope with the huge amounts of …

Abstract Empowered by Internet of Things (IoT) and cloud computing platforms, the concept
of smart cities is making a transition from conceptual models to development and …

The growth of data generation capabilities, facilitated by advancements in communication
and computation technologies, as well as the rise of the Internet of Things (IoT), results in …

Some scientific studies involve huge amounts of bioinformatics data that cannot be analyzed
on personal computers usually employed by researchers for day-to-day activities but rather …

In this decade, the use of learning management systems (LMS) does not cease to increase,
becoming one of the most popular approaches adopted and widely used in the learning …

Assessing trust in online social networks (OSNs) is critical for many applications such as
online marketing and network security. It is a challenging problem, however, due to the …

Spark is a more efficient distributed big data processing framework following Hadoop. It
provides users with more than 180 adjustable configuration parameters, and how to choose …