Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration

Application of ensemble learning techniques to model the atmospheric concentration of SO2

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

Ensemble machine learning methods for acoustic modeling of speech

A Hybrid Machine Learning Method for Intrusion Detection

Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implemen...

Measuring Dissolved Oxygen Concentration in Aquaculture

(DO) is considered the most important water quality variable in fish culture. In the broadest sense, however, dissolved oxygen concentration is no more important than other environmental variables because any factor that is outside the range tolerated by fish can cause stress or death. What makes dissolved oxygen concentration so important in intensive fish culture is the speed with which it ca...

Hybrid and Ensemble Methods in Machine Learning J.UCS Special Issue

Hybrid and ensemble methods in machine learning have attracted a great attention of the scientific community over the last years [Zhou, 12]. Multiple, ensemble learning models have been theoretically and empirically shown to provide significantly better performance than single weak learners, especially while dealing with high dimensional, complex regression and classification problems [Brazdil,...