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.

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.

Computation of Diet Composition for Patients Suffering from Kidney and Urinary Tract Diseases with the Fuzzy Genetic System

Determination of dietary food consumed a day for patients with diseases in general, greatly affect the health of the body and the healing process, is no exception for people with kidney disease and urinary tract. This paper presents the determination of diet composition in the form of food subtance for people with kidney and urinary tract diseases with a genetic fuzzy approach. This approach combines fuzzy logic and genetic algorithms, which utilizing fuzzy logic fuzzy tools and techniques to model the components of the genetic algorithm and adapting genetic algorithm control parameters, with the aim of improving system performance. The Mamdani fuzzy inference model and fuzzy rules based on population parameters and generation are used to determine the probability of crossover and mutation, and was using In this study, 400 food survey data along with their substances was used as test material. From the data, a varying amount of population is established. Each chromosome has 10 genes in which the value of each gene indicates the index number of foodstuffs in the database. The fuzzy genetic approach produces 10 best food substance and their compositions. The composition of these foods has nutritional value in accordance with the number of calories needed by people with kidney and urinary tract diseases by type of food.