The Healthcare industry is generally "information rich", but unfortunately not all the data are mined which is required for discovering hidden patterns & effective decision making. Advanced data mining techniques are used to discover knowledge in database and for medical research, particularly in Heart disease prediction. This paper has analysed prediction systems for Heart disease using more number of input attributes. The system uses medical terms such as sex, blood pressure, cholesterol like 13 attributes to predict the likelihood of patient getting a Heart disease. Until now, 13 attributes are used for prediction. This research paper added two more attributes i.e. obesity and smoking. The data mining classification techniques, namely Decision Trees, Naive Bayes, and Neural Networks are analyzed on Heart disease database. The performance of these techniques is compared, based on accuracy. As per our results accuracy of Neural Networks, Decision Trees, and Naive Bayes are 100%, 99.62%, and 90.74% respectively. Our analysis shows that out of these three classification models Neural Networks predicts Heart disease with highest accuracy.
The approach stated in this paper mainly focuses on minimizing the length of the transaction table of the stock market, based on some common features among the attributes which indirectly minimize the complexity involved in processing; we call this approach as Fragment Based Mining. This deals mainly with reducing the time and space complexity involved in processing the data. Experimentally we try to show our approach is promising one. We conclude that this approach can potentially be used for predictions and recommendations stock trading platforms.
The Bluetooth is widely used to link cell phones to their accessories, and its security has not been considered a major problem. This research paper describes the critical problems and the risks that are identified in all Bluetooth-enabled kits that are tested. Also this paper will explain what Bluetooth is, how it works, and some of the vulnerabilities and risks associated with it.
A CPU is the very important part of the computer system; hence it must be utilized efficiently. When the demand for computing power increases, then scheduling problem becomes very important. The problem of task scheduling and load balancing are most important and challenging area of research in computer engineering. Task scheduling can be defined as allocating processes to processor so that total execution time will be minimized, utilization of processors will be optimized. Load balancing is the process of improving the performance of system through a redistribution of load among processor. In this paper, the performance analysis of various task scheduling algorithms based on different qualitative parameters is presented. The analysis indicates that task scheduling algorithms have some advantages as well as disadvantages. The main purpose of this paper is to help in design of new scheduling algorithms in future by studying existing task scheduling algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.