Stroke is a serious disease that has significant impact on the quality of life and safety patients. Accurately predicting stroke risk great significance for preventing treating stroke. In past few years, machine learning methods have shown potential in risk. However, due to imbalance data challenges feature selection model selection, prediction still faces some difficulties.This article aims co...

Cardiac disease treatments are often being subjected to the acquisition and analysis of vast quantity digital cardiac data. These data can be utilized for various beneficial purposes. data’s utilization becomes more important when we dealing with critical diseases like a heart attack where patient life is at stake. Machine learning deep two famous techniques that helping in making raw useful. S...

Diabetes is an acute disease that happens when the pancreas cannot produce enough insulin. It can be fatal if undiagnosed and untreated. If diabetes revealed early enough, it possible, with adequate treatment, to live a healthy life. Recently, researchers have applied artificial intelligence techniques forecasting of diabetes. As result, new SMOTE-based deep LSTM system was developed detect ear...

As credit card becomes the most popular payment mode particularly in online sector, fraudu- lent activities using technologies are rapidly increasing as a result. The purpose of this work is to develop novel system for fraud detection based on sequential modeling data, attention mechanism Long Short Term Mem- ory(LSTM) deep Recurrent Neural Networks(RNN) and Synthetic Minority Oversampling Tech...

Abstract The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines challenges class-imbalanced streams concept drift. In this paper, we propose very fast continuous synthetic minority oversampling technique ( VFC - SMOTE ). It novel meta-strategy to be prepended any streaming machine clas...

The educational sector faced many types of research in predicting student performance based on supervised and unsupervised machine learning algorithms. Most students' data are imbalanced, where the final classes not equally represented. Besides size dataset, this problem affects model's prediction accuracy. In paper, Synthetic Minority Oversampling Technique (SMOTE) filter is applied to dataset...

Abstract Considering the complexities and challenges in classification of multiclass imbalanced fault conditions, this study explores systematic combination unsupervised supervised learning by hybridising clustering (CLUST) optimised multi-layer perceptron neural network with grey wolf algorithm (GWO-MLP). The hybrid technique was meticulously examined on a historical hydraulic system dataset f...

The increasing availability of big data has led to the development applications that make human life easier. In order process this correctly, it is necessary extract useful and valid information from large warehouses through a knowledge discovery in databases (KDD). Data mining an important part involves discovering developing models unknown patterns. quality used supervised machine learning al...