Bayesian Analysis for Component Manufacturing Processes

In manufacturing processes various machines are used to produce the same product. Based on the age, make, etc., of the machines the output may not always follow the same distribution. An attempt is made to introduce Bayesian techniques for a two machine problem. Two cases are presented in this article

Drying colloidal systems: laboratory models for a wide range of applications

The drying of complex fluids provides a powerful insight into phenomena that take place on time and length scales not normally accessible. An important feature of complex fluids, colloidal dispersions and polymer solutions is their high sensitivity to weak external actions. Thus, the drying of complex fluids involves a large number of physical and chemical processes. The scope of this review is the capacity to tune such systems to reproduce and explore specific properties in a physics laboratory. A wide variety of systems are presented, ranging from functional coatings, food science, cosmetology, medical diagnostics and forensics to geophysics and art

A Novel Lns Semi Supervised Learning Algorithm For Detecting Breast Cancer”

ABSTRACT Semi supervised learning is a relatively new area in machine learning which represents the blend of supervised and unsupervised learning. It has the potential of reducing the need of expensive labeled data whenever only a small set of labeled examples are available. In this paper semi supervised learning algorithm combining Logical data analysis based on complete binary tree with Naïve Bayes and SVM with learning based on both labeled and unlabeled data is proposed for detecting breast cancer. Few labeled data are used as supportive set to build the diagnostic model which is used for classifying the unlabeled data. Wisconsin breast cancer dataset from the UCI machine learning depository is used for the experiment. This algorithm yielded an accuracy of 98.7% for unlabeled samples