Banknotes are currencies used by any nation to carry-out financial activities and are every countries asset which every nation wants it (bank-note) to be genuine. Lot of miscreants induces fake notes into the market which resemble exactly the original note. Hence, there is a need for an efficient authentication system which predicts accurately whether the given note is genuine or not. Exhaustive experiments have been conducted using different machine learning techniques and found that Decision tree and MLP techniques are effective for banknote authentication which efficiently classifies a given banknote data. The rules given by Decision Tree are also tested and found that they are accurate enough to be used for prediction.

Preventing Chronic Kidney Disease has become one of the most intriguing task to the healthcare society. The major objective of this paper is to deal mainly with different classification algorithms namely NaiveBayes, Multi Layer Perceptron and Support Vector Machine. The work analyzes the Chronic Kidney Disease dataset taken from the machine learning repository of UCI. Pre-processing techniques such as missing value replacement, unsupervised discretization and normalization are applied to the Chronic Kidney Disease dataset to improve accuracy. Accuracy and time are the taken as the experimental outcomes of the classification models. The final conclusion states that Support Vector Machine implements much superior than all the other classification methods.