Distributed computing methodology for training neural networks in an image-guided diagnostic application

Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used

Optimization the initial weights of artificial neural networks via genetic algorithm applied to hip bone fracture prediction

This paper aims to find the optimal set of initial weights to enhance the accuracy of artificial neural networks (ANNs) by using genetic algorithms (GA). The sample in this study included 228 patients with first low-trauma hip fracture and 215 patients without hip fracture, both of them were interviewed with 78 questions. We used logistic regression to select 5 important factors (i.e., bone mineral density, experience of fracture, average hand grip strength, intake of coffee, and peak expiratory flow rate) for building artificial neural networks to predict the probabilities of hip fractures. Three-layer (one hidden layer) ANNs models with back-propagation training algorithms were adopted. The purpose in this paper is to find the optimal initial weights of neural networks via genetic algorithm to improve the predictability. Area under the ROC curve (AUC) was used to assess the performance of neural networks. The study results showed the genetic algorithm obtained an AUC of 0.858±0.00493 on modeling data and 0.802 ± 0.03318 on testing data. They were slightly better than the results of our previous study (0.868±0.00387 and 0.796±0.02559, resp.). Thus, the preliminary study for only using simple GA has been proved to be effective for improving the accuracy of artificial neural networks.This research was supported by the National Science Council (NSC) of Taiwan (Grant no. NSC98-2915-I-155-005), the Department of Education grant of Excellent Teaching Program of Yuan Ze University (Grant no. 217517) and the Center for Dynamical Biomarkers and Translational Medicine supported by National Science Council (Grant no. NSC 100- 2911-I-008-001)

Determination of baseflow quantity by using unmanned aerial vehicle (UAV) and Google Earth

Baseflow is most important in low-flow hydrological features [1]. It is a function of a large number of variables that include factors such as topography, geology, soil, vegetation, and climate. In many catchments, base flow is an important component of streamflow and, therefore, base flow separations have been widely studied and have a long history in science. Baseflow separation methods can be divided into two main groups: non-tracer-based and tracer- based separation methods of hydrology. Besides, the base flow is determined by fitting a unit hydrograph model with information from the recession limbs of the hydrograph and extrapolating it backward

Neural Estimation of Food Age with Adaline-based Multi-Layer Perceptron

This study employs a 4-input and 1-output feedforward neural network with adalines used to implement learning via error back-propagation (EBP) using least mean square rule. The neural network is used to predict the condition of both cooked and uncooked food as well as fresh vegetables by determining food age (in days). Neurosolutions training software is used to simulate the neural network. Training data is obtained from a constructed metal oxide semiconductor (MOS) ammonia circuit. Results show that a 95% overall accuracy of neural network results is obtained. This demonstrates the capability of neural networks in accurate classification of sample data points. Food samples used to obtain inference database include rice, beans, fresh vegetables, yam and potatoes.Keywords/Index Terms: neural network, supervised learning, back propagation, e-nose, artificial intelligenc

Design of an optimal multi-layer neural network for eigenfaces based face recognition

Face recognition is one of the most popular problems in the field of image analysis. In this paper, we discuss the design of an optimal multi-layer neural network for the task of face recognition. There are many issues while designing the neural network like number of nodes in input layer, output layer and hidden layer(s), setting the values of learning rate and momentum, updating of weights. Lastly, the criteria for evaluating the performance of the neural network and stopping the learning are to be decided. We discuss all these design issues in the light of the eigenfaces based face recognition. We report the effects of variations of these parameters on number of training cycles required to get optimal results. We also list the optimized values for these parameters. In our experiments, we use two face databases namely ORL and UMIST. These databases are used to construct the eigenfaces. The original faces are reconstructed using the top eigenfaces. The factors used in the reconstruction of the faces are used as the inputs to the neural network

Premature saturation in backpropagation networks: Mechanism and necessary conditions

The mechanism that gives rise to the phenomenon of premature saturation of the output units of feedforward multilayer neural networks during training with the standard backpropagation algorithm is described. The entire process of premature saturation is characterized by three distinct stages and it is concluded that the momentum term plays the leading role in the occurrence of the phenomenon. The necessary conditions for the occurrence of premature saturation are presented and their validity is illustrated through simulation results