Improper working of blood vessels within heart causes heart disease .Hospitals are using medical application software for their day to day operation for billing and generation of simple satictcs.Multispecilaity hospitals are using expert system but they have some limitations. Heart dieses predication is difficult task because we need lot of patient historical data, medical history and it also depend on knowledge and experience of doctors. In this paper decision support systems made by two data mining techniques decision tree and naive bayes. Performance analysis is performed on both the methods.

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Reordering motor vehicle spare parts for the purposes of stock replenishment is an important function of the parts manager in the typical motor dealership. Meaningful reordering requires a reliable forecast of the future demand for items. Production planning and control in remanufacturing are more complex than those in traditional manufacturing. Developing a reliable forecasting process is the first step for optimization of the overall planning process. In remanufacturing, forecasting the timing of demands is one of the critical issues. The current article presents the result of examining the effectiveness of demand forecasting by time series analysis in auto parts remanufacturing. A variety of alternative forecasting techniques were evaluated for this purpose with the aim of selecting one optimal technique to be implemented in an automatic reordering module of a real time computerized inventory management system.

Air contamination is the difficult issue that one must consider and is brought about by injurious gases present in the climate. For example, Carbon Dioxide, Carbon Monoxide, Sulfur Dioxide and so on. The dimensions of quality of the air shifts starting with one spot then onto the next. As indicated by WHO (World Health Organization) air contamination is the fifth significant reason for heart related diseases, hypertension, poor sustenance and tobacco smoking. Observing the measure of destructive gases over a specific zone can lessen the odds of jeopardize to individuals and caution to take prudent steps and do fundamental solutions for direct the emanation of harmful climatic gases. The present paper manages the observing of the polluted gases utilizing gas sensor which is connected with Node MCU. The levels of polluted gases sent through web to cloud stages utilizing MQTT conventions. The information is then sent to ThingSpeak cloud which can be additionally dissected from anyplace in world. Further analysis of the gas levels and estimations of future status of the air quality must be done and in order to carry out the same, high end strategies are adopted like Machine learning (ML) computation called Support Vector Regression (SVR) using Radial Basis Function (RBF), which is good in class regression analysis in the field of data prediction and significantly utilized for information anticipating. The implementation of proposed ML algorithm enhances the chances of forecasting close gas levels to actual values. And mean square error analysis gives the performance measure of the proposed model.