In this paper, we develop a heart disease prediction model that can assist medical professionals in predicting heart disease status based on the clinical data of patients. Firstly, we select 14 important clinical features, i.e., age, sex, chest pain type, trestbps, cholesterol, fasting blood sugar, resting ecg, max heart rate, exercise induced angina, old peak, slope, number of vessels colored,...

Numerous data mining techniques have been developed to extract information and identify patterns and predict trends from large data sets. In this study, two classification techniques, the J48 implementation of the C4.5 algorithm and a Naive Bayes classifier are applied to predict lung cancer survivability from an extensive data set with fifteen years of patient records. The purpose of the proje...

and Objective Nearly at every patient visit medical documents are produced and stored in a medical record, often in unstructured form as free text. Growing amount of stored documents increases the need for effective and timely retrieval of information. We developed a multi-label classification system to categorize German language free text medical documents (e.g. discharge letters, clinical fin...

the Paper presents the comparison of different classification techniques for the task of classifying Number plate image data set. The comparison was conducted using WEKA (Waikato Environment for Knowledge Analysis) open source which mainly consists the collection of machine learning algorithms for data mining purpose. The main purpose of this paper is to investigate efficiency of different clas...

Spams are becoming a serious threat for the users of online social networks especially for the ones like of twitter. twitter’s structural features make it more volatile to spam attacks. In this paper, we propose a spam detection approach for twitter based on sentimental features. We perform our experiments on a data collection of 29K tweets with 1K tweets for 29 trending topics of 2012 on twitt...

System administrators have to analyze a number of system parameters to identify performance bottlenecks in a system. The major contribution of this paper is a utility – EvoPerf – which has the ability to autonomously monitor different system-wide parameters, requiring no user intervention, to accurately identify performance based anomalies (or bottlenecks). EvoPerf uses Windows Perfmon utility ...

Large computational value has always been a restraint in processing huge network intrusion data. This problem can be extenuated through feature selection to abbreviate the size of the network data involved. In this paper, we first deal existing feature selection methods that are computationally executable for processing vast network intrusion datasets. In this paper, we study and analysis of fo...