arXiv subjects

Agus Harjoko

Publications and source records attributed to Agus Harjoko.

3 recordsLinked to original sources

arXiv2014

Selection of Most Appropriate Backpropagation Training Algorithm in Data Pattern Recognition

There are several training algorithms for backpropagation method in neural network. Not all of these algorithms have the same accuracy level demonstrated through the percentage level of suitability in recognizing patterns in the data. In this research tested 12 training algorithms specifically in recognize data patterns of test validity. The basic network parameters used are the maximum allowable epoch = 1000, target error = 10-3, and learning rate = 0.05. Of the twelve training algorithms each performed 20 times looping. The test results obtained that the percentage rate of the great match is trainlm algorithm with alpha 5% have adequate levels of suitability of 87.5% at the level of significance of 0.000. This means the most appropriate training algorithm in recognizing the the data pattern of test validity is the trainlm algorithm.

cs.NE↗

arXiv2014

Five Modular Redundancy with Mitigation Technique to Recover the Error Module

Hazard radiation can lead the system fault therefore Fault Tolerance is required. Fault Tolerant is a system, which is designed to keep operations running, despite the degradation in the specific module is happening. Many fault tolerances have been developed to handle the problem, to find the most robust and efficient in the possible technology. This paper will present the Five Modular Redundancy (FMR) with Mitigation Technique to Recover the Error Module. With Dynamic Partial Reconfiguration technology that have already available today, such fault tolerance technique can be implemented successfully. The project showed the robustness of the system is increased and module which is error can be recovered immediately.

cs.AR↗

arXiv2013

Selection Mammogram Texture Descriptors Based on Statistics Properties Backpropagation Structure

Computer Aided Diagnosis (CAD) system has been developed for the early detection of breast cancer, one of the most deadly cancer for women. The benign of mammogram has different texture from malignant. There are fifty mammogram images used in this work which are divided for training and testing. Therefore, the selection of the right texture to determine the level of accuracy of CAD system is important. The first and second order statistics are the texture feature extraction methods which can be used on a mammogram. This work classifies texture descriptor into nine groups where the extraction of features is classified using backpropagation learning with two types of multi-layer perceptron (MLP). The best texture descriptor as selected when the value of regression 1 appears in both the MLP-1 and the MLP-2 with the number of epoches less than 1000. The results of testing show that the best selected texture descriptor is the second order (combination) using all direction (0, 45, 90 and 135) that have twenty four descriptors.

cs.CV↗