The Experts below are selected from a list of 257274 Experts worldwide ranked by ideXlab platform

M. Suganthi - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Enhanced Gray Scale Adaptive Method for Prediction of Breast Cancer
    Journal of Medical Systems, 2018
    Co-Authors: C. Selvi, M. Suganthi
    Abstract:

    Breast cancer is the important problem across the globe in which, most of the women are suffering without knowing the causes and effects of the cancer cells. Mammographic is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent techniques are suggested for the detection of Microcalcification, Clusters, Masses, Spiculate lesions, Asymmetry and Architectural distortions in the mammograms. But the prediction of the cancer levels needs more research light. For the determination of the higher level of accuracy and prediction, the proposed algorithm called Enhanced Gray Scale Adaptive Method (EGAM) which works on the principle of combination of K-GLCM and Extreme Fuzzy Learning Machines (EFLM). The proposed algorithm has achieved 99% accuracy and less computation time in terms of classification, detection and prediction when compared with the existing intelligent algorithms.

  • A Novel Enhanced Gray Scale Adaptive Method for Prediction of Breast Cancer
    Journal of Medical Systems, 2018
    Co-Authors: C. Selvi, M. Suganthi
    Abstract:

    Breast cancer is the important problem across the globe in which, most of the women are suffering without knowing the causes and effects of the cancer cells. Mammographic is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent techniques are suggested for the detection of Microcalcification, Clusters, Masses, Spiculate lesions, Asymmetry and Architectural distortions in the mammograms. But the prediction of the cancer levels needs more research light. For the determination of the higher level of accuracy and prediction, the proposed algorithm called Enhanced Gray Scale Adaptive Method (EGAM) which works on the principle of combination of K-GLCM and Extreme Fuzzy Learning Machines (EFLM). The proposed algorithm has achieved 99% accuracy and less computation time in terms of classification, detection and prediction when compared with the existing intelligent algorithms.

C. Selvi - One of the best experts on this subject based on the ideXlab platform.

  • A Novel Enhanced Gray Scale Adaptive Method for Prediction of Breast Cancer
    Journal of Medical Systems, 2018
    Co-Authors: C. Selvi, M. Suganthi
    Abstract:

    Breast cancer is the important problem across the globe in which, most of the women are suffering without knowing the causes and effects of the cancer cells. Mammographic is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent techniques are suggested for the detection of Microcalcification, Clusters, Masses, Spiculate lesions, Asymmetry and Architectural distortions in the mammograms. But the prediction of the cancer levels needs more research light. For the determination of the higher level of accuracy and prediction, the proposed algorithm called Enhanced Gray Scale Adaptive Method (EGAM) which works on the principle of combination of K-GLCM and Extreme Fuzzy Learning Machines (EFLM). The proposed algorithm has achieved 99% accuracy and less computation time in terms of classification, detection and prediction when compared with the existing intelligent algorithms.

  • A Novel Enhanced Gray Scale Adaptive Method for Prediction of Breast Cancer
    Journal of Medical Systems, 2018
    Co-Authors: C. Selvi, M. Suganthi
    Abstract:

    Breast cancer is the important problem across the globe in which, most of the women are suffering without knowing the causes and effects of the cancer cells. Mammographic is the most powerful tool for the diagnosis of the Breast cancer. The analysis of this mammogram images proves to be more vital in terms of diagnosis but the accuracy level still needs improvisation. Several intelligent techniques are suggested for the detection of Microcalcification, Clusters, Masses, Spiculate lesions, Asymmetry and Architectural distortions in the mammograms. But the prediction of the cancer levels needs more research light. For the determination of the higher level of accuracy and prediction, the proposed algorithm called Enhanced Gray Scale Adaptive Method (EGAM) which works on the principle of combination of K-GLCM and Extreme Fuzzy Learning Machines (EFLM). The proposed algorithm has achieved 99% accuracy and less computation time in terms of classification, detection and prediction when compared with the existing intelligent algorithms.

Bayram Yenikaya - One of the best experts on this subject based on the ideXlab platform.

  • an Adaptive Method with rigorous error control for the hamilton jacobi equations part ii the two dimensional steady state case
    Journal of Computational Physics, 2005
    Co-Authors: Bernardo Cockburn, Bayram Yenikaya
    Abstract:

    In this paper, we devise and study an Adaptive Method for finding approximations to the viscosity solution of Hamilton-Jacobi equations. The Method, which is an extension to two space dimensions of a similar Method previously proposed for one space dimension, is studied in the framework of steady-state Hamilton-Jacobi equations with periodic boundary conditions. It seeks numerical approximations whose L^~-distance to the viscosity solution is no bigger than a prescribed tolerance. A thorough numerical study is carried out which shows that a strict error control is achieved and that the Method exhibits an optimal computational complexity which does not depend on the value of the tolerance or on the type of Hamiltonian.

  • an Adaptive Method with rigorous error control for the hamilton jacobi equations part i the one dimensional steady state case
    Applied Numerical Mathematics, 2005
    Co-Authors: Bernardo Cockburn, Bayram Yenikaya
    Abstract:

    In this paper, we introduce a new Adaptive Method for finding approximations for Hamilton-Jacobi equations whose L∞-distance to the viscosity solution is no bigger than a prescribed tolerance. This is done on the simple setting of a one-dimensional model problem with periodic boundary conditions. We consider this to be a stepping stone towards the more challenging goal of constructing such Methods for general Hamilton-Jacobi equations. The Method proceeds as follows. On any given grid, the approximate solution is computed by using a well-known monotone scheme; then, the quality of the approximation is tested by using an approximate a posteriori error estimate. If the error is bigger than the prescribed tolerance, a new grid is computed by solving a differential equation whose devising is the main contribution of the paper. A thorough numerical study of the Method is performed which shows that rigorous error control is achieved, even though only an approximate a posteriori error estimate is used; the Method is thus reliable. Furthermore, the numerical study also shows that the Method is efficient and that it has an optimal computational complexity. These properties are independent of the value of the tolerance. Finally, we provide extensive numerical evidence indicating that the Adaptive Method converges to an approximate solution that can be characterized solely in terms of the tolerance, the artificial viscosity of the monotone scheme and the exact solution.

Lars O. Dahl - One of the best experts on this subject based on the ideXlab platform.

  • AN Adaptive Method FOR EVALUATING MULTIDIMENSIONAL CONTINGENT CLAIMS: PART II
    International Journal of Theoretical and Applied Finance, 2003
    Co-Authors: Lars O. Dahl
    Abstract:

    This is part two of a work on Adaptive integration Methods aimed at multidimensional option pricing problems in finance. It presents simulation results of an Adaptive Method developed in the companion article [3] for the evaluation of multidimensional integrals over the unit cube. The article focuses on a rather general test problem constructed to give insights in the success of the Adaptive Method for option pricing problems. We establish a connection between the decline rate of the ordered eigenvalues of the pricing problem and the efficiency of the Adaptive Method relative to the non-Adaptive. This gives criteria for when the Adaptive Method can be expected to outperform the non-Adaptive for other pricing problems. In addition to evaluating the Method for different problem parameters, we present simulation results after adding various techniques to enhance the Adaptive Method itself. This includes using variance reduction techniques for each sub-problem resulting from the partitioning of the integration domain. All simulations are done with both pseudo-random numbers and quasi-random numbers (low discrepancy sequences), resulting in Monte Carlo (MC) and quasi-Monte Carlo (QMC) estimators and the ability to compare them in the given setting. The results show that the Adaptive Method can give performance gains in the order of magnitudes for many configurations, but it should not be used incautious, since this ability depends heavily on the problem at hand.

  • AN Adaptive Method FOR EVALUATING MULTIDIMENSIONAL CONTINGENT CLAIMS: PART I
    International Journal of Theoretical and Applied Finance, 2003
    Co-Authors: Lars O. Dahl
    Abstract:

    The paper presents an Adaptive Method for the evaluation of multidimensional integrals over the unit cube. The measure used to partition the domain is suited for integrands which are monotonic in each dimension individually, and is therefore suitable for problems stemming from finance where this is often the case. We use a QMC Method for each sub-problem resulting from the partitioning of the domain. The article is part one of a work on this topic, and presents the Method together with various local variance reduction techniques. The material is presented with an alignment to option pricing problems. In the companion paper we present an option pricing problem and simulation results on different setups of this. We compare the convergence properties of the Adaptive Method with the convergence properties of the QMC Method used directly on the problem. We find that the Adaptive Method in many configurations outperform the conventional QMC Method, and we develop criteria on the problem for when the Adaptive Method can be expected to outperform the conventional.

Bernardo Cockburn - One of the best experts on this subject based on the ideXlab platform.

  • an Adaptive Method with rigorous error control for the hamilton jacobi equations part ii the two dimensional steady state case
    Journal of Computational Physics, 2005
    Co-Authors: Bernardo Cockburn, Bayram Yenikaya
    Abstract:

    In this paper, we devise and study an Adaptive Method for finding approximations to the viscosity solution of Hamilton-Jacobi equations. The Method, which is an extension to two space dimensions of a similar Method previously proposed for one space dimension, is studied in the framework of steady-state Hamilton-Jacobi equations with periodic boundary conditions. It seeks numerical approximations whose L^~-distance to the viscosity solution is no bigger than a prescribed tolerance. A thorough numerical study is carried out which shows that a strict error control is achieved and that the Method exhibits an optimal computational complexity which does not depend on the value of the tolerance or on the type of Hamiltonian.

  • an Adaptive Method with rigorous error control for the hamilton jacobi equations part i the one dimensional steady state case
    Applied Numerical Mathematics, 2005
    Co-Authors: Bernardo Cockburn, Bayram Yenikaya
    Abstract:

    In this paper, we introduce a new Adaptive Method for finding approximations for Hamilton-Jacobi equations whose L∞-distance to the viscosity solution is no bigger than a prescribed tolerance. This is done on the simple setting of a one-dimensional model problem with periodic boundary conditions. We consider this to be a stepping stone towards the more challenging goal of constructing such Methods for general Hamilton-Jacobi equations. The Method proceeds as follows. On any given grid, the approximate solution is computed by using a well-known monotone scheme; then, the quality of the approximation is tested by using an approximate a posteriori error estimate. If the error is bigger than the prescribed tolerance, a new grid is computed by solving a differential equation whose devising is the main contribution of the paper. A thorough numerical study of the Method is performed which shows that rigorous error control is achieved, even though only an approximate a posteriori error estimate is used; the Method is thus reliable. Furthermore, the numerical study also shows that the Method is efficient and that it has an optimal computational complexity. These properties are independent of the value of the tolerance. Finally, we provide extensive numerical evidence indicating that the Adaptive Method converges to an approximate solution that can be characterized solely in terms of the tolerance, the artificial viscosity of the monotone scheme and the exact solution.