In recent years, the way that machine learning is used has undergone a paradigm shift
driven by distributed and collaborative learning. Several approaches have emerged to …

The dramatic growth of the Web has motivated researchers to extract knowledge from
enormous repositories and to exploit the knowledge in myriad applications. In this study, we …

As the usage of social media has increased, the size of shared data has instantly surged
and this has been an important source of research for environmental issues as it has been …

Product reviews are of great commercial value for online shopping market. The identification
of customer opinions from product reviews is helpful to improve the marketing decisions of …

Depression is one of the most common mental disorders, with sleep disturbances as typical
symptoms. With the popularity of wearable devices increasing in recent years, more and …

The classification of high-dimensional data is a challenge in machine learning. Traditional
classifier ensemble methods improve the diversity of classifiers through either …

Social media has rapidly expanded over a period of time and generated a huge repository of
content. Sentiment analysis of this data has a vast scope in decision support and attracted …

The coordinated integration of heterogeneous TinyML-enabled elements in highly
distributed Internet of Things (IoT) environments paves the way for the development of truly …

Twitter is used by millions of people across the world, so the data collected from Twitter can
be highly valuable for research and helpful in decision support. Here in this paper 'Twitter …

Ensemble classifiers are widely used for the enhancement of accuracy of twitter sentiment
classification. In the present research, a hybrid model based on stack based ensemble …