With air pollution having become a global concern, scientists are committed to working on its
amelioration. In the field of air pollution prediction, there have been good results in …

The establishment of fuzzy relations and the fuzzification of time series are the top priorities
of the model for predicting fuzzy time series. A lot of literature studied these two aspects to …

Time series forecasting is a powerful tool in planning and decision making, from traditional
statistical models to soft computing and artificial intelligence approaches several methods …

Intuitionistic fuzzy sets are extended form of type 1 fuzzy sets. The modeling methods use
intuitionistic fuzzy sets have second-order uncertainty approximation so these methods may …

The present article proposes a probabilistic weighted high-order fuzzy time series (FTS)
forecasting model employing Bayesian network (BN) to address complex relationships and …

Association rule mining is an important data mining technique used for discovering
relationships among all data items. Membership functions have a significant impact on the …

Decision rules have been successfully used in various classification applications because of
their interpretability and efficiency. In many real-world scenarios, especially in industrial …

Emotion processing has been a very intense domain of investigation in data analysis and
NLP during the previous few years. Currently, the algorithms of the deep neural networks …

In forecasting real time environmental factors, large data is needed to analyse the pattern
behind the data values. Air pollution is a major threat towards developing countries and it is …

In the present study, we propose a novel high-order weighted fuzzy time series (FTS)
forecasting method using k-mean clustering, weighted fuzzy logical relations and …