Recent advances in the domain of software defect prediction (SDP) include the integration of
multiple classification techniques to create an ensemble or hybrid approach. This technique …

The knowledge obtained from data can be useful for the improvement of education systems,
giving rise to a research space called Educational Data Mining (EDM). EDM covers the …

Bagging is an old and powerful method in ensemble learning which creates an ensemble of
classifiers over bootstraps through learning and then generates diverse classifiers. There …

Abstract Machine Learning (ML) is an evolving area of research with lot many opportunities
to explore.“It is the defining technology of this decade, though its impact on healthcare has …

Computational classification of cancers is an important research problem. Gene expression
data has 1000s of features, very few samples, and a class imbalance problem. In this paper …

Deep ensemble models have been demonstrated to show promising generalization
capability. A deep ensemble model includes several deep neural networks as base …

In the healthcare industry, develo** an efficient diagnostic system to classify liver cancer
cells is a very perplexing and arduous task. Recently, several studies demonstrate that deep …

Abstract Classification algorithms as Support Vector Machine (SVM) and Neural Network
(NN) have provided considerably good results in the diagnosis of Critical Care diseases …

The vibration signals of on-load tap-changer (OLTC) contain a rich of operating status
information and will effectively diagnose the mechanical fault of OLTC. For the purpose of …

Image classification is a fundamental task in computer vision. Due to the high dimensionality
of the image data and high variations across images such as rotation, scale, illumination …