scholarly journals Multimodal neural network classifier of the functional state of the respiratory system

2021 ◽  
Vol 2094 (3) ◽  
pp. 032064
Author(s):  
S A Filist ◽  
M B Myasnyankin ◽  
R I Safronov ◽  
A A Kuzmin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Functional State ◽  
Respiratory System ◽  
Intelligent Systems ◽  
Neural Network Classifier ◽  
Living Objects ◽  
Source Of Information

Abstract At present, the intelligent systems are used to study the functional state of living objects. They are often built on a multimodal basis, i.e. decision-making is carried out through several information channels with the subsequent aggregation of these decisions. In the proposed method, the electrocardiosignal is a source of information. The principle of multimodality is implemented by analyzing of various methods and aggregating the results using neural networks of block and hierarchical structure. The method was tested on the classification of patients with pneumonia with a clear diagnosis. A group of volunteers without pulmonary pathologies was used as an indifferent class.

scholarly journals Faint Object Classification Using Artificial Neural Networks

1994 ◽  
Vol 161 ◽  
pp. 249-252
Author(s):  
M. Serra-Ricart
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Faint Object ◽  
Artificial Neural ◽  
Astronomical Images

Artificial Neural Network techniques are applied to the classification of faint objects, detected in digital astronomical images, and a Bayesian classifier (the neural network classifier, NNC hereafter) is proposed. This classifier can be implemented using a feedforward multilayered neural network trained by the back-propagation procedure (Werbos 1974).

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

Inclusion of Multiple Cycling of the Potential into Deep Neural Network Classification of Voltammetric Reaction Mechanisms

2021 ◽  
Author(s):  
Luke Gundry ◽  
Gareth Kennedy ◽  
Alan Bond ◽  
Jie Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reaction Mechanisms ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Neural Network Classification ◽  
Initial Cycle

The use of Deep Neural Networks (DNNs) for the classification of electrochemical mechanisms based on training with simulations of the initial cycle of potential have been reported. In this paper,...

N. Belknap's Four-Valued Logic, Belknap Computer and New Proximity Functions for Comparing Discrete Objects

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Valerii Dmitrienko ◽  
Sergey Leonov ◽  
Mykola Mezentsev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
The Other ◽  
Proximity Function ◽  
Input Information ◽  
Proximity Measures ◽  
The One ◽  
True Values

The idea of ​​Belknap's four-valued logic is that modern computers should function normally not only with the true values ​​of the input information, but also under the conditions of inconsistency and incompleteness of true failures. Belknap's logic introduces four true values: T (true - true), F (false - false), N (none - nobody, nothing, none), B (both - the two, not only the one but also the other).  For ease of work with these true values, the following designations are introduced: (1, 0, n, b). Belknap's logic can be used to obtain estimates of proximity measures for discrete objects, for which the functions Jaccard and Needhem, Russel and Rao, Sokal and Michener, Hamming, etc. are used. In this case, it becomes possible to assess the proximity, recognition and classification of objects in conditions of uncertainty when the true values ​​are taken from the set (1, 0, n, b). Based on the architecture of the Hamming neural network, neural networks have been developed that allow calculating the distances between objects described using true values ​​(1, 0, n, b). Keywords: four-valued Belknap logic, Belknap computer, proximity assessment, recognition and classification, proximity function, neural network.

scholarly journals Analysis and classification of tem¬perature measurements during melting and casting of alloys using neural networks

2020 ◽  
Vol 63 (10) ◽  
pp. 856-861
Author(s):  
A. V. Fedosov ◽  
G. V. Chumachenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Human Factor ◽  
High Efficiency ◽  
Thermal Conditions ◽  
Second Stage ◽  
Three Stages ◽  
Fully Automatic ◽  
Artificial Neural

The article considers the issues of monitoring the thermal conditions of alloys melting and casting at foundries. It is noted that the least reliable method is when the measurement and fixing the temperature is assigned to the worker. On the other hand, a fully automatic approach is not always available for small foundries. In this regard, the expediency of using an automated approach is shown, in which the measurement is assigned to the worker, and the values are recorded automatically. This method assumes implementation of an algorithm for automatic classification of temperature measurements based on an end-to-end array of data obtained in the production stream. The solving of this task is divided into three stages. Preparing of raw data for classification process is provided on the first stage. On the second stage, the task of measurement classification is solved by using neural network principles. Analysis of the results of the artificial neural network has shown its high efficiency and degree of their correspondence with the actual situation on the work site. It was also noted that the application of artificial neural networks principles makes the classification process flexible, due to the ability to easily supplement the process with new parameters and neurons. The final stage is analysis of the obtained results. Correctly performed data classification provides an opportunity not only to assess compliance with technological discipline at the site, but also to improve the process of identifying the causes of casting defects. Application of the proposed approach allows us to reduce the influence of human factor in the analysis of thermal conditions of alloys melting and casting with minimal costs for melting monitoring.

scholarly journals Pattern Recognition by Hierarchical Feature Extraction

2003 ◽  
Vol 15 (3) ◽  
pp. 278-285
Author(s):  
Daigo Misaki ◽  
◽  
Shigeru Aomura ◽  
Noriyuki Aoyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Input Data ◽  
Local Features ◽  
Detailed Classification

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called ""landmark""s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

scholarly journals DESIGN INFORMATION SYSTEM OF EMPLOYEES TARGET WITH NEURAL NETWORK BACKPROPAGATION

2018 ◽  
Vol 2 (1) ◽  
pp. 5
Author(s):  
Agus Sifaunajah ◽  
Kusworo Adi ◽  
Faikhin .
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information System ◽  
Information Systems ◽  
Data Collection ◽  
Data Classification ◽  
Civil Servants ◽  
Web Based ◽  
Design Information

Assessment of the performance of civil servants (PNS) is still considered less objective and subjective tended to by some, so we need a solution to improve the objectivity of assessment. The target of employee work (SKP) is one solution to improve objectivity in the assessment of civil servants. Backpropagation is one of the methods in neural networks which is implemented in the information systems of SKP for used classification of data performance. Observation and literature became the method of data collection in this study. Web-based information systems of skp are facilitated for employees in the preparation of assessments. Backpropagation can be implemented to perform data classification of performance. Keyword: Neural network; Backpropagation, Classification, SKP Received: 2 February, 2017; Accepter: 15 March, 2017

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