scholarly journals A Method for Assessing the Retention of Trace Elements in Human Body Using Neural Network Technology

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Yulia Tunakova ◽  
Svetlana Novikova ◽  
Aligejdar Ragimov ◽  
Rashat Faizullin ◽  
Vsevolod Valiev
Keyword(s):  
Neural Network ◽  
Trace Elements ◽  
Drinking Water ◽  
Trace Element ◽  
Human Body ◽  
Network Models ◽  
The Body ◽  
Human Organism ◽  
Neural Network Models ◽  
The Many

Models that describe the trace element status formation in the human organism are essential for a correction of micromineral (trace elements) deficiency. A direct trace element retention assessment in the body is difficult due to the many internal mechanisms. The trace element retention is determined by the amount and the ratio of incoming and excreted substance. So, the concentration of trace elements in drinking water characterizes the intake, whereas the element concentration in urine characterizes the excretion. This system can be interpreted as three interrelated elements that are in equilibrium. Since many relationships in the system are not known, the use of standard mathematical models is difficult. The artificial neural network use is suitable for constructing a model in the best way because it can take into account all dependencies in the system implicitly and process inaccurate and incomplete data. We created several neural network models to describe the retentions of trace elements in the human body. On the model basis, we can calculate the microelement levels in the body, knowing the trace element levels in drinking water and urine. These results can be used in health care to provide the population with safe drinking water.

DEVELOPMENT OF AN EXPERT SYSTEM FOR DIAGNOSTICS OF LUNG DISEASES BASED ON NEURAL NETWORK MODELING

2021 ◽  
pp. 155-159
Author(s):  
Екатерина Ивановна Новикова ◽  
Екатерина Александровна Андрианова ◽  
Елена Евгеньевна Удодова ◽  
Анастасия Юрьевна Корниенко ◽  
Александр Станиславович Панов
Keyword(s):  
Neural Network ◽  
Lung Diseases ◽  
Network Models ◽  
The Body ◽  
Neural Network Modeling ◽  
Diagnostic Study ◽  
Topological Groups ◽  
Social Significance ◽  
Neural Network Models ◽  
Information Process

В статье рассматриваются вопросы диагностики заболеваний легких, таких как кавернозный, инфильтративный, очаговой, диссеминированный туберкулез, онкология и пневмония. Медико-социальное значение болезней органов дыхания в современных условиях велико и определяется, прежде всего, их крайне высокой частотой среди различных контингентов населения. Учитывая значимость дыхания для организма, необходимо вовремя выявлять различные патологии и применять незамедлительные меры лечения. Одним из средств повышения эффективности диагностики данных патологий является автоматизация обработки диагностических данных с использованием современных технологий, а также создание компьютерной системы поддержки принятия решений, которая принимала бы во внимание большой объем диагностической информации и исключала ошибки субъективного характера. Выделение топологических групп по легочным заболеваниям проводилось с использованием самоорганизующихся карт Кохонена. По результатам классификации было проведено обучения нейронных сетей, используя алгоритм «многослойного персептрона» методом «обратного распространения», и получены математические модели. В медицинской практике постоянно следует учитывать то обстоятельство, что достоверные и адекватные медицинские данные, например, лабораторные анализы, результаты инструментального диагностического исследования, данные опроса больного или физикального исследования, потеряют свою актуальность, если информационный процесс длительно растянут по времени. Разработанные нейросетевые модели, были реализованы в информационно-программном обеспечении, которое позволит повысить эффективность процесса диагностики заболеваний легких The article deals with the diagnosis of lung diseases such as cavernous, infiltrative, focal, disseminated tuberculosis, oncology and pneumonia. The medical and social significance of respiratory diseases in modern conditions is great and is determined, first of all, by their extremely high frequency among various contingents of the population. Given the importance of breathing for the body, it is necessary to timely identify various pathologies and apply immediate treatment measures. One of the means of increasing the efficiency of diagnosing these pathologies is the automation of the processing of diagnostic data using modern technologies, as well as the creation of a computer decision support system that would take into account a large amount of diagnostic information and exclude subjective errors. The selection of topological groups for pulmonary diseases was carried out using self-organizing Kohonen maps. Based on the classification results, neural networks were trained using the "multilayer perceptron" algorithm by the "backpropagation" method and mathematical models were obtained. In medical practice, one should constantly take into account the fact that reliable and adequate medical data, for example, laboratory tests, the results of an instrumental diagnostic study, data from a patient survey or physical examination, will lose their relevance if the information process is prolonged for a long time. The developed neural network models were implemented in information and software that will improve the efficiency of the process of diagnosing lung diseases

Building on prior knowledge without building it in

2017 ◽  
Vol 40 ◽  
Author(s):  
Steven S. Hansen ◽  
Andrew K. Lampinen ◽  
Gaurav Suri ◽  
James L. McClelland
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Prior Knowledge ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Network Models ◽  
Causal Models ◽  
Neural Network Models ◽  
Start Up ◽  
The Many

AbstractLake et al. propose that people rely on “start-up software,” “causal models,” and “intuitive theories” built using compositional representations to learn new tasks more efficiently than some deep neural network models. We highlight the many drawbacks of a commitment to compositional representations and describe our continuing effort to explore how the ability to build on prior knowledge and to learn new tasks efficiently could arise through learning in deep neural networks.

Approximation theory of the MLP model in neural networks

Acta Numerica ◽  
1999 ◽  
Vol 8 ◽  
pp. 143-195 ◽  
Author(s):  
Allan Pinkus
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Approximation Theory ◽  
Network Models ◽  
Neural Network Models ◽  
The Many ◽  
Mlp Model

In this survey we discuss various approximation-theoretic problems that arise in the multilayer feedforward perceptron (MLP) model in neural networks. The MLP model is one of the more popular and practical of the many neural network models. Mathematically it is also one of the simpler models. Nonetheless the mathematics of this model is not well understood, and many of these problems are approximation-theoretic in character. Most of the research we will discuss is of very recent vintage. We will report on what has been done and on various unanswered questions. We will not be presenting practical (algorithmic) methods. We will, however, be exploring the capabilities and limitations of this model.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

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