Artifcial Neural Networks Research Articles

Artifcial intelligence capabilities in evaluating effectiveness of non-medicinal treatment of obesity in children

Introduction. Non-drug therapy for obesity cannot always guarantee a positive result, which forces doctors and scientists from all over the world to look for new methods for analyzing the effectiveness of treatment, including using artifcial intelligence. Its active implementation can significantly improve the quality of diagnosis and prognosis of the disease. Purpose of the study. To evaluate the possibilities of using the artifcial intelligence system in predicting the effectiveness of non-drug therapy for obesity in children.Materials and methods. An artifcial neural network was built using the Statistica Neural Networks software package based on data from patients who were hospitalized at the Voronezh Children's Clinical Hospital of the VSMU n.a. N.N. Burdenko.Results. The study group included 60 children (30 boys and 30 girls), aged 8 to 16 years. We selected the parameters that, in our opinion, have the most signifcant impact on the effect of non-drug treatment of obesity: the presence and frequency of inpatient treatment; obesity complications; compliance with the regime of physical activity and dietary recommendations; dynamics of body weight during non-drug treatment. After training, the neural network MLP 5-5-1 was selected with determination coeffcients of 0.925231; 0.981940; 0.936712 for training, test and control samples. The learning error is 0.105782, the learning algorithm is BFGS. The activation function of hidden neurons is hyperbolic, and the output function is identical.Conclusion. The results of the study show that an artifcial neural network can be used to evaluate the effectiveness of non-drug treatment with a minimum error.

Generative Adversarial Network Performance in Low-Dimensional Settings.

A generative adversarial network (GAN) is an artifcial neural network with a distinctive training architecture, designed to create examples that faithfully reproduce a target distribution. GANs have recently had particular success in applications involving high-dimensional distributions in areas such as image processing. Little work has been reported for low dimensions, where properties of GANs may be better identifed and understood. We studied GAN performance in simulated low-dimensional settings, allowing us to transparently assess effects of target distribution complexity and training data sample size on GAN performance in a simple experiment. This experiment revealed two important forms of GAN error, tail underflling and bridge bias, where the latter is analogous to the tunneling observed in high-dimensional GANs.

Prediction of carbon dioxide diffusivity in biodegradable polymers using diffusion neural network

An accurate and efficient artifcial neural network based on mega-trend diffusion algorithm (MD) is developed for predicting CO2 diffusivity in biodegradable polymers. This mega-trend diffusion neural network (MD-NN) model could predict diffusivities close to experimental data. Furthermore, the comparison of MD-NN model with free-volume model and conventional back-propagation model demonstrates that proposed model is a powerful method and has better precision.

On the design of neuro-controllers for individual and social learning behaviour in autonomous robots: an evolutionary approach

In biology/psychology, the capability of natural organisms to learn from the observation/interaction with conspecifics is referred to as social learning. Roboticists have recently developed an interest in social learning, since it might represent an effective strategy to enhance the adaptivity of a team of autonomous robots. In this study, we show that a methodological approach based on artifcial neural networks shaped by evolutionary computation techniques can be successfully employed to synthesise the individual and social learning mechanisms for robots required to learn a desired action (i.e. phototaxis or antiphototaxis).

Knowledge-Based Artificial Neural Network Models for Finline

Finline plays an important role in millimeter-wave integrated-circuit design. In this paper, a knowledge-based artifcial neural network is used to model the finline. Using prior knowledge input method and Bayesian regularization technique make the neural network models for finline reduce the amount of training data needed and prevent overfitting in neural network training. The neural network is electromagnetically developed with a set of training data that are produced by the fnite element method, which is robust both from the angle of time of computation and accuracy.

MATRIX AND CEMENT EFFECTS ON RESIDUAL OIL SATURATION IN SANDSTONE FORMATIONS: A NEURAL NETWORK APPROACH

Residual oil in sandstone is affected by mineral composition, clay matrix and cementing material. Matrix minerals affect the affinity of a fluid to spread on a rock surface significantly and in turn controls the fluid distribution within the pore spaces. At the interface between the rock surface and the contacting fluid, electrical charges are in the origin of the extent of phase wetness. Available framework grains, a dominant component of rock matrix, affect porosity and, hence, amounts of rock preferential wetness. Cement, clay matrix and quartz overgrowth, which make up for the rest of the grain population in a rock, influence wetness and, therefore, amounts of residual oil. In this paper, spectro-electromicroscopy (SEM) point-count technique in conjunction with neural network analysis were used to determine the effect of certains rock parameters on the amounts of residual oil following waterflooding operations. Using artifcial neural network, the intent was then to predict the extent of residual oil in sandstone rocks given limited information about rock matrix, cementing material and primary porosity.

Crop classification from C-band polarimetric radar data

Abstract Synthetic aperture radar “SAR” systems are an attractive source of information for agricultural crop classification applications, particularly in regions where cloud cover is a problem. The accuracy with which crops can be classified is dependent on a range of sensor properties, including the SAR operating configuration. This paper focuses on the effect of one aspect of the SAR operating configuration, polarization, on crop classification accuracy using uncalibrated C-band polarimetric SAR data. Conventional like- and cro spolarized configurations and polarimetric coefficients “the pedestal and variation coefficients” were used as discriminating variables in classifications of agricultural crops. Two approaches to classification were investigated, a discriminant analysis and an artif cial neural network and results from a set of training classifications are presented. The results show that the polarimetric coefficients used provided a high level of inter-class discrimination and that a nine-class...