scholarly journals Artificial neural network technology for lips reading

2021 ◽  
Vol 333 ◽  
pp. 01009
Author(s):  
Anna Pyataeva ◽  
Anton Dzyuba
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Video Sequence ◽  
Experimental Studies ◽  
Network Technology ◽  
Haar Features ◽  
Two Stages ◽  
Face Search ◽  
Neural Network Technology

The paper presents the use of neural networks for the task of automated speech reading by lips articulation. Speech recognition is performed in two stages. First, a face search is performed and the lips area is selected in a separate frame of the video sequence using Haar features. Then the sequence of frames goes to the input of deep learning convolutional and recurrent neural networks for speech viseme recognition. Experimental studies were carried out using independently obtained videos with Russian-speaking speakers.

Simulation of hydraulic power plant transients using neural networks

1997 ◽  
Vol 211 (5) ◽  
pp. 393-398 ◽  
Author(s):  
G Bidini ◽  
F Mariani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Power Plant ◽  
Linear Problem ◽  
Network Technology ◽  
Transient Phase ◽  
Hydraulic Power ◽  
Set Up ◽  
Turbine Speed ◽  
Neural Network Technology

In this work the efficiency of the artificial neural network technology for evaluating the turbine speed and drain synchronous valve during the turn-off transient phase of an actual hydraulic power plant has been evaluated. These results have been achieved avoiding a detailed time consuming analysis of the power plant. Several net architectures have been set up and verified with regard to their response to this strongly non-linear problem.

Using intelligent technologies for tourism

2021 ◽  
pp. 96-105
Author(s):  
Nataliia Lytvyn ◽  
Svitlana Panchenko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Knowledge Management ◽  
Research Methodology ◽  
Network Technology ◽  
Inaccurate Information ◽  
Tourism Product ◽  
The Subject ◽  
Neural Network Technology

The purpose of the article is to explore the essence and features of using intelligent technologies in tourism and to develop proposals for their implementation. The subject of research – intelligent technologies in tourism, the technology of forming the “profile” of the tourist. The research methodology consists in the application of methods of analysis, synthesis, comparison, generalization, forecasting, as well as in the use of systematic, activity approaches. The article presents the technology of forming the “profile” of the tourist. It is established that it is necessary to create a world of tourist models, the “profile” of the tourist, as it is a matter of formalizing such poorly structured concepts as “impressions”, “intentions”, etc., it is necessary to use artificial intelligence technologies, in particular neural networks. The scientific novelty is that this article proves the effectiveness of the use of intelligent technologies to create a model of the tourist, his “profile” using neural networks. Conclusions. Effective using of information from various sources in the field of tourism is an important and difficult task. Managers are often forced to make decisions based on partial, incomplete and inaccurate information. The article considers knowledge management in a rapidly changing environment for the task of promoting a tourism product. Neural network technology allows for the effective formation of the “tourist profile” and use all the information in available databases. Key words: tourism, intelligent technologies for tourism, neural networks, tourist profile, tourist product.

scholarly journals Differential Diagnosis of Parasitic Diseases of the Gastrointestinal Tract Using Artificial Neural Networks

2021 ◽  
Vol 3 (5) ◽  
pp. 01-05
Author(s):  
U N Musevi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Differential Diagnosis ◽  
Gastrointestinal Tract ◽  
Parasitic Diseases ◽  
Disease Prediction ◽  
Network Technology ◽  
Test Results ◽  
The Neural Network ◽  
Neural Network Technology

Disorders of the functional state of the gastrointestinal tract associated with the influence of various parasites are considered. The symptoms of diseases caused by parasites and their location in the gastrointestinal tract are given. The possibility of using neural network technology in diagnosing illnesses as a result of the influence of various parasites is estimated. The structure of the neural network is given, indicating the set of inputs and outputs, as well as the result of its training. For the created neural network, test results for the respective symptoms and disease prediction results for these symptoms were obtained.

Neural networks as an intelligence amplification tool: A review of applications

Geophysics ◽  
2002 ◽  
Vol 67 (3) ◽  
pp. 979-993 ◽  
Author(s):  
Mary M. Poulton
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Economic Value ◽  
Synthetic Data ◽  
Relevant Information ◽  
Geophysical Data ◽  
Small Scale ◽  
Network Technology ◽  
Exploration Process ◽  
Neural Network Technology

The sophisticated algorithms we use to process, analyze, and interpret geophysical data automate tasks we used to do by hand, transform data into domains where patterns are more obvious, and allow us to calculate quantities where we used to rely on intuition or back‐of‐envelope estimates. But, the crux of the exploration problem is still interpretation—associating abstract patterns with geologic formations of economic value. Artificial neural networks are able to couple the speed and efficiency of the computer with the pattern recognition and association capabilities of the brain to aid the exploration process. The key concept to understand in the application of neural network technology is that they should not be used as an artificial intelligence tool to replace a human interpreter; rather, neural networks are an intelligence amplification toolkit that allows the interpreter to focus on the important information. More than 102 neural network papers have been published by SEG since 1988, and more than 550 neural network papers pertaining to any aspect of geophysics were published in that same time period. Neural network applications in exploration geophysics can generally be divided into two eras. The focus through 1994 was largely on learning what neural networks could do with different data sets, and how to prepare data for them and analyze the results. Networks were usually trained with synthetic data and then tested with field data. The second era, from 1995 to the present, has focused on some specific application areas such as reservoir characterization. The current emphasis is to integrate neural networks within a comprehensive interpretation scheme instead of as a stand‐alone application. Neural network technology has helped us turn data into information by allowing us to find nontrivial correlations between geophysical data and petrophysical properties; relate detailed changes in wavelet morphology to small‐scale changes in lithology; and find features in the wavelets that allow us to locate first breaks, track horizons, identify gas chimneys, or trace faults; and attenuate multiples. As the science and engineering of data acquisition progresses, neural networks will play an increasingly vital role in helping us find relevant information in the vast streams of data under the constraints of lower costs, less time, and fewer people.

MONITORING AND CLASSIFYING THE STATE OF HARD DISKS USING RECURRENT NEURAL NETWORKS

2021 ◽  
pp. 36-43
Author(s):  
L. A. Demidova ◽  
A. V. Filatov
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Experimental Studies ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Dense Layer ◽  
Hard Disks ◽  
Destructive Testing ◽  
Data Set ◽  
State Classification

The article considers an approach to solving the problem of monitoring and classifying the states of hard disks, which is solved on a regular basis, within the framework of the concept of non-destructive testing. It is proposed to solve this problem by developing a classification model using machine learning algorithms, in particular, using recurrent neural networks with Simple RNN, LSTM and GRU architectures. To develop a classification model, a data set based on the values of SMART sensors installed on hard disks it used. It represents a group of multidimensional time series. At the same time, the structure of the classification model contains two layers of a neural network with one of the recurrent architectures, as well as a Dropout layer and a Dense layer. The results of experimental studies confirming the advantages of LSTM and GRU architectures as part of hard disk state classification models are presented.

scholarly journals Recurrent Neural Networks for Narrowband Signal Detection in the Time-Frequency Domain

2004 ◽  
Vol 213 ◽  
pp. 483-486
Author(s):  
David Brodrick ◽  
Douglas Taylor ◽  
Joachim Diederich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Signal Detection ◽  
Frequency Domain ◽  
Recurrent Neural Networks ◽  
Radio Frequency Interference ◽  
Recurrent Networks ◽  
Time Frequency ◽  
Narrowband Signal ◽  
Radio Signals

A recurrent neural network was trained to detect the time-frequency domain signature of narrowband radio signals against a background of astronomical noise. The objective was to investigate the use of recurrent networks for signal detection in the Search for Extra-Terrestrial Intelligence, though the problem is closely analogous to the detection of some classes of Radio Frequency Interference in radio astronomy.

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