Big data deals with data of very large data size, heterogeneous data types and from different
sources. The data is very complex in nature and having growing data. Dealing with big data …

In this study, we propose dynamic model update methods for the adaptive classification
model of text streams in a distributed learning environment. In particular, we present two …

Abstract The concept of Data Stream has emerged as a result of the evolution of
technologies in different domains such as banking, e-commerce, social media, and many …

Goods and Service Tax (GST) was proposed by the government of India as aone tax one
nation scheme'replacing all cascading tax into a single tax called as GST. The GST is India's …

Sensors in the real-world applications are the major sources of big data streams with varying
underlying data distribution. Continuously generated time varying data streams are …

In online learning, the concept drifts refers to the situations where the objective variable
conforming to the input data changes over time. This change in distribution of data over time …

Online learning is done in order to work on dynamic environments in which the concept
tends to change with time and the accuracy of classifiers decreases. The current and …

Sentiment Analysis is a way to determine the emotions behind social media discussions.
Analyzing social data plays a vital role in knowing people‟ s behavior about an entity or …

Introduction: The present research was conducted at Birla Institute of Technology, off
Campus in Noida, India, in 2017. Methods: To assess the efficiency of the proposed …

Abstract Concept drift is the scenario in online learning in which value of target variable
changes with respect to time. The learning algorithms should be adaptive in nature in order …