LEARNING HYPERPLANES THAT CAPTURES THE GEOMETRIC STRUCTURE OF CLASS REGIONS

Most of the decision tree algorithms rely on impurity measures to evaluate the goodness of hyperplanes at each node while learning a decision tree in a top-down fashion. These impurity measures are not differentiable with relation to the hyperplane parameters. Therefore the algorithms for decision tree learning using impurity measures need to use some search techniques for finding the best hyperplane at every node. These impurity measures don’t properly capture the geometric structures of the data. In this paper a Two-Class algorithm for learning oblique decision trees is proposed. Aggravated by this, the algorithm uses a strategy, to evaluate the hyperplanes in such a way that the (linear) geometric structure in the data is taken into consideration. At each node of the decision tree, algorithm finds the clustering hyperplanes for both the classes. The clustering hyperplanes are obtained by solving the generalized Eigen-value problem. Then the data is splitted based on angle bisector and recursively learn the left and right sub-trees of the node. Since, in general, there will be two angle bisectors; one is selected which is better based on an impurity measure gini index. Thus the algorithm combines the ideas of linear tendencies in data and purity of nodes to find better decision trees. This idea leads to small decision trees and better performance

M2M Communications for E-Health and Smart Grid: An Industry and Standard Perspective

An overview of several standardization activities for machine-to-machine (M2M) communications is presented, analyzing some of the enabling technologies and applications of M2M in industry sectors such as Smart Grid and e-Health. This summary and overview of the ongoing work in M2M from the industrial and standardization perspective complements the prevalent academic perspective of such publications to date in this field

Ayurvedic management of Janusandhigata Vata - A Case Report

Osteoarthritis of knee is one of the major musculoskeletal abnormality found now a days. Osteoarthritis (OA) is the most common joint disorder all over the world. Symptomatic knee OA occurs in 10% men and 13% in women aged 60 years or older. The number of people affected with symptomatic OA is likely to increase due to the aging of the population and the obesity epidemic. In Ayurveda, osteoarthritis is correlated with Sandhigata Vata. Large number of studies have been conducted for the Ayurvedic treatment of Sandhigata Vata. So with help of this paper, efforts were taken for the successful Ayurvedic management of Jaanu Sandhigata Vata

To assess the importance of Nidana Parivarjan in the treatment of Urdhvag Amlapitta with Guduchi Satva

Amlapitta is one of the leading clinical conditions in today’s speedy lifestyle. Present study focuses on this burning issue and its causes mentioned in Ayurved texts and actual causes observed in day to day life. Importance and benefits of Nidanparivarjan over only symptomatic treatment was assessed during this study. Amlapitta cases were diagnosed according to Ayurvedic texts and classified into two groups. One group was administered with only treatment and the other group was advised Nidanparivarjan along with the treatment. At the end of the study, it was found that the group with Nidanparivarjan was more benefited as compared to only treatment group. This clearly states that Ayurvedic method of finding the particular Hetu of the disease and practice of avoiding those Hetu; i.e. Nidanparivarjan leads to better results and complete eradication and prevention of the disease; thus serving the main principle of Ayurved science - Prevention is better than Cure