Machine Learning Algorithm to Identify the Fault Data Identification Using Multi-Class Support Vector Machine

An experiment was conducted to the raw web log files, in a controlled lab environment, by using KDD technique and M-SVM algorithm. Based on the experiment conducted, the M-SVM algorithm generates 98.68% for true positive rate and 1.32% for false positive rate which indicates the significant efficiency of the new web log file classification and data transformation technique used in this research work. MSVM model identified fault data identification in more accurate with less time when compared to existing SVM model

HYPERPROPERTIES-BASED OPTICAL FLOW-BASED AUTONOMOUS DRIVING SYSTEM

This study presents an autonomous driving system based on the principles of trace vectors derived from hyperproperty of a modified optical flowalgorithm. This technique allows keeping track of the past motion vectors by tracking the constraint sets to overcome the non-linear attributes ofthe deformable feature points and motion vectors. The results presented in this work exhibits stable tracking and multi-step prediction in a limitednumber of steps with less training vectors

Bounds on Energy and Laplacian Energy of Graphs

Let G be simple graph with n vertices and m edges. The energy E(G) of G, denotedby E(G), is dened to be the sum of the absolute values of the eigenvalues of G. Inthis paper, we present two new upper bounds for energy of a graph, one in terms ofm,n and another in terms of largest absolute eigenvalue and the smallest absoluteeigenvalue. The paper also contains upper bounds for Laplacian energy of graph

Minimum Covering Seidel Energy of a Graph

In this paper we have computed minimum covering Seidel energies ofa star graph, complete graph, crown graph, complete bipartite graph and cocktailparty graphs. Upper and lower bounds for minimum covering Seidel energies of agraphs are also established.DOI : http://dx.doi.org/10.22342/jims.22.1.234.71-8