http://localhost:8080/xmlui/handle/123456789/1734 Mon, 26 Jan 2026 19:54:27 GMT 2026-01-26T19:54:27Z http://localhost:8080/xmlui/handle/123456789/1795 Title: Combinatorial Double Auction based Meta-scheduler for Medical Image Analysis Application in Grid Environment Authors: Karthikeyan Periyasamia; Arul Xavier Viswanathan Mariammalb; Iwin Thanakumar Josephc; Velliangiri Sarveshwarand Abstract: Grid computing provides more computing power to solve the financial forecasting, weather forecasting, drug design and medical image processing application. Many meta-scheduling algorithms have been proposed to schedule jobs. Considering the architecture and characteristics of the grid environments, traditional meta-scheduler algorithms cannot be applied to the grid computing properly. In this paper, we have come up with a combinatorial double auction based meta-scheduler. The aim of this meta-scheduler is to maximize the number of the job accepted. We assess the proposed meta-scheduler performance by simulating the grid environment. The experimental result shows that the proposed meta-scheduler algorithm maximize the number of the job accepted than the traditional meta-scheduler algorithm. Tue, 01 Jan 2019 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1795 2019-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/1796 Title: Detection and Prevention of Types of Attacks Using Machine Learning Techniques in Cognitive Radio Networks Authors: Sudha Ya Abstract: A number of studies have been done on several types of data link and network layer attacks and defenses for CSS in CRNs, but there are still a number of challenges unsolved and open issues waiting for solutions. Specifically, from the perspective of attackers, when launching the attack, users have to take into account of the factors of attack gain, attack cost and attack risk, together. From the perspective of defenders, there are also three aspects deserving consideration: defense reliability, defense efficiency and defense universality. The attacks and defenses are mutually coupled from each other. Attackers need to adjust their strategies to keep their negative effects on final decisions and avoid defenders’ detection, while defenders have to learn and analyze attack behaviors and designs effective defense rules. Indeed, attack and defense ought to be considered together. the proposed methodology overcomes the problems of several data link and network layer attacks and it effects in CSS(Co-operative Spectrum Sensing) of CNRs using Machine Learning based Defense, Cross layers optimization techniques and Defence based Prevention mechanisms. Tue, 01 Jan 2019 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1796 2019-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/1793 Title: Realistic Deformation and Removal of Soft Tissues Modeling for the Simulation of Virtual Surgery Authors: K. Jayasudha; Mohan G. Kabadib Abstract: Most of the simulation methods for soft tissue modeling involve tetrahedral meshes which is quite complex and takes much computation time. Instead, this work attempts to make use of delaunay triangulated mesh that consists of unique mathematical properties suited for simulating soft tissues. Although, triangulated mesh is not so complex yet effective in producing elements of good quality. It even reduces computation time compared to the tetrahedral mesh by providing more geometric flexibility. In virtual surgery, it is essential to model the layers of soft tissues of human skin to perform a simulation of deformation and removal of cells. Based on this the multilayered model of skin prototype is developed in a pre-process and used for interactive modeling. This work presents a simple method for performing real-time collision detection in a virtual surgery environment. Also shows the efficient computation of collisions between the scalpel and delaunay triangulated mesh using a local collision detection function. The framework incorporates qualitative results obtained towards the simulation of surgical deformation and removal of soft tissues using appropriate algorithms. It also uses real-time texture mapping to enhance the visual realism. Tue, 01 Jan 2019 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1793 2019-01-01T00:00:00Z http://localhost:8080/xmlui/handle/123456789/1788 Title: Detection of Prostate Cancer Related Genes using Modified Ford-Fulkerson Algorithm in Protein-to-Protein Interaction Network Authors: Sanjeev Prakashrao Kaulguda; Vishwanath Hulipalledb; Somanagouda Patilc Abstract: Prostate cancer is a malignancy cancer that affects prostate epithelial cells. Presently, prostate cancer is the second leading cause of cancer-related death in men. In this research, a new computational system was proposed for determining the prostate cancer related genes with the shortest path methodology in a Protein to Protein Interaction (PPI) network. Here, a weighted PPI network was constructed on the basis of PPI data from Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Totally, eighteen prostate related genes were extracted from the STRING database by using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Then, the shortest path between eighteen genes was identified using modified Ford-Fulkerson algorithm. Generally, the conventional Ford-Fulkerson algorithm was very effective in detecting the shortest path between the prostate cancer-related genes, but the elapsed time was high when the PPI network has more number of genes. In order to reduce the elapsed time, the modified Ford Fulkerson algorithm was developed by eliminating the invalid path in gene connection. In the experimental section, the proposed shortest path approach reduced the elapsed time up to 0.025-0.002 seconds compared to the existing shortest path methodologies. Tue, 01 Jan 2019 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/1788 2019-01-01T00:00:00Z