Manufacturing organizations need to use different kinds of techniques and tools in order to
fulfill their foundation goals. In this aspect, using machine learning (ML) and data mining …

Clustering, as an unsupervised learning, is aimed at discovering the natural grou**s of a
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …

Cluster ensembles have been shown to be better than any standard clustering algorithm at
improving accuracy and robustness across different data collections. This meta-learning …

In this study, ensemble learning was applied to develop a classification model capable of
accurately estimating slope stability. Two prominent ensemble techniques—parallel learning …

Modelling and simulation of municipal solid waste (MSW) gasification process is a complex
and computationally expensive task due to the porous structure of MSW and the nonlinear …

One of the most important tasks in pattern, machine learning, and data mining is
classification problem. Introducing a general classifier is a challenge for pattern recognition …

In the contemporary data-driven era, the fields of machine learning, deep learning, big data,
statistics, and data science are essential for forecasting outcomes and getting insights from …

This comprehensive review delves into the intersection of ensemble machine learning
models and interpretability techniques for biomass and waste gasification, a field crucial for …

The issue of weathering steel (WS) material and structural component inspection has been
widely discussed in the current scientific research. However, there are few comprehensive …

For obtaining the more robust, novel, stable, and consistent clustering result, clustering
ensemble has been emerged. There are two approaches in clustering ensemble …