To promote sustainable development, the smart city implies a global vision that merges
artificial intelligence, big data, decision making, information and communication technology …

Data mining (DM) is an instrument of pattern detection and retrieval of knowledge from a
large quantity of data. Many robust early detection services and other health-related …

Classifiers trained with class-imbalanced data are known to perform poorly on test data of
the" minor" classes, of which we have insufficient training data. In this paper, we investigate …

In the big data environment, hospital medical data are also becoming more complex and
diversified. The traditional method of manually processing data has not been able to meet …

Neural networks trained with class-imbalanced data are known to perform poorly on minor
classes of scarce training data. Several recent works attribute this to over-fitting to minor …

Ensemble technique and under-sampling technique are both effective tools used for
imbalanced dataset classification problems. In this paper, a novel ensemble method …

Learning a classifier from an imbalanced dataset is an important problem in data mining and
machine learning. Since there is more information from the majority classes than the …

Due to the high-dimensional characteristics of dataset, we propose a new method based on
the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed …

Deep learning has been applied successfully in sequence understanding and translation
problems, especially in univariate, unimodal contexts, where large number of supervision …

Background An imbalanced dataset is defined as a training dataset that has imbalanced
proportions of data in both interesting and uninteresting classes. Often in biomedical …