Breast cancer is the most frequently diagnosed cancer in USA; furthermore breast cancer is the second most frequent cause of death for women in the United States as well as in Asia. In USA 40,600 deaths from breast cancer in 2009, 400 were men.[1] Several well established tools are currently used to screen for breast cancer including clinical breast exams, mammograms, and ultrasound. Supervised training is a technique in which a set of representative input output pairs is presented to the network. Through an iterative algorithm, the interval network weights are adjusted to decrease the difference between the network prediction and the true result for the training cases. The test has been performed on the breast cancer dataset using three classification techniques: Bayes learner, Decision Tree and Neural Net. The experiment concludes that Neural Net performance is better than the Decision Tree classification and Naïve Bayes classification for early detection of breast cancer with better accuracy and precision.

Breast cancer is the most frequently diagnosed cancer in USA. Furthermore breast cancer is the second major cause of death for women in USA. Several well established tools are currently used for screening for breast cancer including clinical breast exam, mammograms and ultrasound. Mammography is one of the most effective in terms of accuracy and cost. However the low positive predicted value (PPV) of breast cancer biopsies resulting from mammograms leads to 70% unnecessary biopsies with benign outcomes. In order to reduce the large number of surgical biopsies of breast, several CAD based system has been proposed in the last decades. Using these systems the radiologist gets an aid on their decision to perform breast biopsies. The dataset used is based on BIRADS findings. Prior work achieves good result with decision tree and neural network. The paper use AutoMLP, BP (back propagation) neural network and support vector machine (SVM) approach to predict the outcomes of mammogram with better result. Using SVM the false biopsies should significantly reduced to only 13%.

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In distributed system common global clock and shared memory does not exist, so knowledge is shared by passing messages between several sites. Reliable broadcast eventually delivers messages to all participating sites. Total order broadcast ensures that all messages must be delivered to all sites in same order and it is a stronger notion of reliable broadcast [1]. Event-B is based on set theory and used event driven approach. For system-level analysis and modeling Event-B is a formal technique. In this technique system is gone through several stages for refinement [7,9]. To specify total order broadcasting, introduce privilege based algorithm and refine it at the refinement level that only owner of the token can broadcast the messages in privilege based algorithm and detect failures like messages having same sequence number, token is not present for broadcasting a messages, higher sequence number message is delivered before lower one.