Neural network based glucose - insulin metabolism models for children with Type 1 diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier

In this paper, a computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented. Regions of interest (ROIs) taken from nonenhanced CT images of normal liver, hepatic cysts, hemangiomas, and hepatocellular carcinomas have been used as input to the system. The proposed system consists of two modules: the feature extraction and the classification modules. The feature extraction module calculates the average gray level and 48 texture characteristics, which are derived from the spatial gray-level co-occurrence matrices, obtained from the ROIs. The classifier module consists of three sequentially placed feed-forward neural networks (NNs). The first NN classifies into normal or pathological liver regions. The pathological liver regions are characterized by the second NN as cyst or "other disease." The third NN classifies "other disease" into hemangioma or hepatocellular carcinoma. Three feature selection techniques have been applied to each individual NN: the sequential forward selection, the sequential floating forward selection, and a genetic algorithm for feature selection. The comparative study of the above dimensionality reduction methods shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.

A neural network approach for insulin regime and dose adjustment in type 1 diabetes

A decision support system based on a neural network approach is proposed to advise on insulin regime and dose adjustment for type 1 diabetes patients.

Simbrain: A visual framework for neural network analysis and education

Simbrain is a visually-oriented framework for building and analyzing neural networks. It emphasizes the analysis of networks which control agents embedded in virtual environments, and visualization of the structures which occur in the high dimensional state spaces of these networks. The program was originally intended to facilitate analysis of representational processes in embodied agents, however it is also well suited to teaching neural networks concepts to a broader audience than is traditional for neural networks courses. Simbrain was used to teach a course at a new university, UC Merced, in its inaugural year. Experiences from the course and sample lessons are provided.

Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network

An aerodynamic optimization of the train aerodynamic characteristics in term of front wind action sensitivity is carried out in this paper. In particular, a genetic algorithm (GA) is used to perform a shape optimization study of a high-speed train nose. The nose is parametrically defined via Bézier Curves, including a wider range of geometries in the design space as possible optimal solutions. Using a GA, the main disadvantage to deal with is the large number of evaluations need before finding such optimal. Here it is proposed the use of metamodels to replace Navier-Stokes solver. Among all the posibilities, Rsponse Surface Models and Artificial Neural Networks (ANN) are considered. Best results of prediction and generalization are obtained with ANN and those are applied in GA code. The paper shows the feasibility of using GA in combination with ANN for this problem, and solutions achieved are included.

Improving Wishart Classification of Polarimetric SAR Data Using the Hopfield Neural Network Optimization Approach.

This paper proposes the optimization relaxation approach based on the analogue Hopfield Neural Network (HNN) for cluster refinement of pre-classified Polarimetric Synthetic Aperture Radar (PolSAR) image data. We consider the initial classification provided by the maximum-likelihood classifier based on the complex Wishart distribution, which is then supplied to the HNN optimization approach. The goal is to improve the classification results obtained by the Wishart approach. The classification improvement is verified by computing a cluster separability coefficient and a measure of homogeneity within the clusters. During the HNN optimization process, for each iteration and for each pixel, two consistency coefficients are computed, taking into account two types of relations between the pixel under consideration and its corresponding neighbors. Based on these coefficients and on the information coming from the pixel itself, the pixel under study is re-classified. Different experiments are carried out to verify that the proposed approach outperforms other strategies, achieving the best results in terms of separability and a trade-off with the homogeneity preserving relevant structures in the image. The performance is also measured in terms of computational central processing unit (CPU) times.

Neural network controller for active demand side management with PV energy in the residential sector

In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation.

Air pollution data classification by SOM Neural Network

Over the last ten years, Salamanca has been considered among the most polluted cities in México. This paper presents a Self-Organizing Maps (SOM) Neural Network application to classify pollution data and automatize the air pollution level determination for Sulphur Dioxide (SO2) in Salamanca. Meteorological parameters are well known to be important factors contributing to air quality estimation and prediction. In order to observe the behavior and clarify the influence of wind parameters on the SO2 concentrations a SOM Neural Network have been implemented along a year. The main advantages of the SOM is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. The results show a significative correlation between pollutant concentrations and some environmental variables.

Using a causal smoothing to improve the performance of an on-line neural network glucose prediction algorithm

This work evaluates a spline-based smoothing method applied to the output of a glucose predictor. Methods:Our on-line prediction algorithm is based on a neural network model (NNM). We trained/validated the NNM with a prediction horizon of 30 minutes using 39/54 profiles of patients monitored with the Guardian® Real-Time continuous glucose monitoring system The NNM output is smoothed by fitting a causal cubic spline. The assessment parameters are the error (RMSE), mean delay (MD) and the high-frequency noise (HFCrms). The HFCrms is the root-mean-square values of the high-frequency components isolated with a zero-delay non-causal filter. HFCrms is 2.90±1.37 (mg/dl) for the original profiles.

Adaptive Pointing Theory (APT) Artificial Neural Network

The choice value and the testing process against the vigilance parameter, characteristic of ART Neural Network, are merged. Only, a single unique test is required to determine if a committed category node can represent the current input or not. Advantages of APT over ART are: 1-Avoid testing every committed category node before deciding to train a committed category node or a new node must be committed, 2-The vigilance parameter is fixed during training, and 3-The choice value parameter is eliminated.

Artificial analysis of molecular marker loci linked to tree resistance response by an artificial neural network

One of the biggest challenges that software developers face is to make an accurate estimate of the project effort. Radial basis function neural networks have been used to software effort estimation in this work using NASA dataset. This paper evaluates and compares radial basis function versus a regression model. The results show that radial basis function neural network have obtained less Mean Square Error than the regression method.

Sistemas de ayuda al diagnóstico y reconocimiento de microcalcificaciones en mamografía mediante descriptores de escala y redes jerárquicas

La presente Tesis investiga el campo del reconocimiento automático de imágenes mediante ordenador aplicado al análisis de imágenes médicas en mamografía digital. Hay un interés por desarrollar sistemas de aprendizaje que asistan a los radiólogos en el reconocimiento de las microcalcificaciones para apoyarles en los programas de cribado y prevención del cáncer de mama. Para ello el análisis de las microcalcificaciones se ha revelado como técnica clave de diagnóstico precoz, pero sin embargo el diseño de sistemas automáticos para reconocerlas es complejo por la variabilidad y condiciones de las imágenes mamográficas. En este trabajo se analizan los planteamientos teóricos de diseño de sistemas de reconocimiento de imágenes, con énfasis en los problemas específicos de detección y clasificación de microcalcificaciones. Se ha realizado un estudio que incluye desde las técnicas de operadores morfológicos, redes neuronales, máquinas de vectores soporte, hasta las más recientes de aprendizaje profundo mediante redes neuronales convolucionales, contemplando la importancia de los conceptos de escala y jerarquía a la hora del diseño y sus implicaciones en la búsqueda de la arquitectura de conexiones y capas de la red. Con estos fundamentos teóricos y elementos de diseño procedentes de otros trabajos en este área realizados por el autor, se implementan tres sistemas de reconocimiento de mamografías que reflejan una evolución tecnológica, culminando en un sistema basado en Redes Neuronales Convolucionales (CNN) cuya arquitectura se diseña gracias al análisis teórico anterior y a los resultados prácticos de análisis de escalas llevados a cabo en nuestra base de datos de imágenes. Los tres sistemas se entrenan y validan con la base de datos de mamografías DDSM, con un total de 100 muestras de entrenamiento y 100 de prueba escogidas para evitar sesgos y reflejar fielmente un programa de cribado. La validez de las CNN para el problema que nos ocupa queda demostrada y se propone un camino de investigación para el diseño de su arquitectura. ABSTRACT This Dissertation investigates the field of computer image recognition applied to medical imaging in mammography. There is an interest in developing learning systems to assist radiologists in recognition of microcalcifications to help them in screening programs for prevention of breast cancer. Analysis of microcalcifications has emerged as a key technique for early diagnosis of breast cancer, but the design of automatic systems to recognize them is complicated by the variability and conditions of mammographic images. In this Thesis the theoretical approaches to design image recognition systems are discussed, with emphasis on the specific problems of detection and classification of microcalcifications. Our study includes techniques ranging from morphological operators, neural networks and support vector machines, to the most recent deep convolutional neural networks. We deal with learning theory by analyzing the importance of the concepts of scale and hierarchy at the design stage and its implications in the search for the architecture of connections and network layers. With these theoretical facts and design elements coming from other works in this area done by the author, three mammogram recognition systems which reflect technological developments are implemented, culminating in a system based on Convolutional Neural Networks (CNN), whose architecture is designed thanks to the previously mentioned theoretical study and practical results of analysis conducted on scales in our image database. All three systems are trained and validated against the DDSM mammographic database, with a total of 100 training samples and 100 test samples chosen to avoid bias and stand for a real screening program. The validity of the CNN approach to the problem is demonstrated and a research way to help in designing the architecture of these networks is proposed.