Data classification plays important role in the field of data mining. The increasing rate of data diversity and size decrease the performance and efficiency of classifier. The decreasing performance of classifier compromised with unvoted data of classifier. Now the merging of two or more classifier for better prediction and voting of data are used, such techniques are called Ensemble classifier...

This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the startup transient current signal, and used the...

With rapid technological progress in the Internet of Things (IoT), it has become imperative to concentrate on its security aspect. This paper represents a model that accounts for detection botnets through use machine learning algorithms. The examined anomalies, commonly referred as botnets, cluster IoT devices attempting connect network. Essentially, this exhibited transport layer data (User Da...

This study focuses on how to predict diabetes using blood sample data and machine learning algorithms like the Voting Classifier over Random Forest technique. The proposed prediction models were trained evaluated a dataset that included seven variables: glucose level, diastolic pressure, thickness, insulin levels, BMI, age, skin. new classifier (VC) (RF) are used of 1495 records with 10 feature...

There are several disease kinds in global populations that may be related to human lifestyles, social, genetic, economic, and other factors the nature of country they live in. Most recent studies have focused on investigating prevalent diseases spread population order minimize mortality risks, choose best method for treatment, improve community healthcare. Kidney is one most widespread health p...