Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally …

The decline of the number of newly discovered mineral deposits and increase in demand for
different minerals in recent years has led exploration geologists to look for more efficient and …

New technologies have enabled the investigation of biology and human health at an
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …

Ensemble methods use multiple models to get better performance. Ensemble methods have
been used in multiple research fields such as computational intelligence, statistics and …

With the emerging technologies and all associated devices, it is predicted that massive
amount of data will be created in the next few years–in fact, as much as 90% of current data …

The problem of network intrusion detection poses innumerable challenges to the research
community, industry, and commercial sectors. Moreover, the persistent attacks occurring on …

In recent years, the variety and volume of data acquired by modern analytical instruments in
order to conduct a better authentication of food has dramatically increased. Several pattern …

The use of enzyme-mediated reactions has transcended ancient food production to the
laboratory synthesis of complex molecules. This evolution has been accelerated by …

In this paper, we have compared the classification results of two models ie Random Forest
and the J48 for classifying twenty versatile datasets. We took 20 data sets available from UCI …