No abstract

No abstract

Diabetes Mellitus is one of the growing fatal diseases all over the world. It leads to complications that include heart disease, stroke, and nerve disease, kidney damage. So, Medical Professionals want a reliable prediction system to diagnose Diabetes. To predict the diabetes at earlier stage, different machine learning techniques are useful for examining the data from different sources and valuable knowledge is synopsized. So, mining the diabetes data in an efficient way is a crucial concern. In this project, a medical dataset has been accomplished to predict the diabetes. The R-Studio software was employed as a statistical computing tool for diagnosing diabetes. The PIMA Indian database was acquired from UCI repository will be used for analysis. The dataset was studied and analyzed to build an effective model that predicts and diagnoses the diabetes disease earlier.

The present paper studies a cold standby repairable system consisting of two identical components namely component 1, component 2 and onerepairman is studied. Assume that each component after repair is not 'as good as new' and also the successive working times form a decreasing a-series process, the successive repair time's form an increasing geometric process and both the processes are exposing to exponential failure law. Under these assumptions we study an optimal replacement policy N in which we replace the system when the number of failures of component 1 reaches N. It can be determined that an optimal repair replacement policy N* such that the long run average cost per unit time is minimized. It can also be derived an explicit expression of the long-run average cost and the corresponding optimal replacement policy N* can be determined analytically. Numerical results are provided to support the theoretical results

This paper studies a shock model for a repairable system with two-type failures by assuming that two kinds of shocks in a sequence of random shocks will make the system failed, one based on the inter-arrival time between two consecutive shocks less than a given positive value and the other based on the shock magnitude of single shock more than a given positive value . Further it is assumed that the system after repair is not ‘as good as new’, but the consecutive repair times of the system form a stochastic increasing α-seires process. Under these assumptions, we determine an explicit expression for the average cost rate and an optimal placement policy N* based on the number of failure of the system is determined such that the long-run average cost per unit time is minimized. The explicit expression of long-run average cost per unit time is derived, and the corresponding optimal replacement policy can be determined analytically or numerically. Finally, a numerical example is given.