An Efficient Neural Network Model for the Identification of Stress using Electrocardiogram

People are facing numerous pressures in their daily routine in the latest society. Stress has traditionally has been described as action from a calm state to an emotional state in order to preserve the integrity of organism. Stress observation is very important for mental wellbeing and early identification of stress related disorders. Stress is to learn the body response in stressful state, whenever the body reaction is activated that means the heart rate and blood pressure will raise and several hormones enter our bloodshed. These hormones and bodily changes may increases our performances to a particular extent. Everyone's response to stress is discreet, and not all stress is bad. Someone may discover a significant condition of pressure to be enjoyable, while others may find it stressful. However, individuals also have different stress symptoms. stress area can also recognize using frequency and excitation of a speech signal, Since the biomedical signals are consistently related to central nervous system, therefore physiological parameters are the best way to understand the human emotions. The present work is focused on stress identification from Electrocardiogram using ECG physiologic net database, then entire environment of ECG signal characteristics i.e. mean heart rate variability (HRV), standard deviation of all R-R interval (SDNN), square root mean of the sum of the square difference between R-R interval (RMSSD) and number of consecutive R-R interval variations greater than 50ms (NN50), these features are extracted using Pan-Tompkins algorithm, then it is trained and validated to machine learning using back-propagation algorithm in neural network model. With the help of these features (mean HRV, SDNN, RMSSD and NN50), the study can be analyzed whether a person is under stress or not. Thus how the suggested technique provides the subjective information which helps the doctor to find out whether the person is under stress or not.

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An Efficient Neural Network Model for De-noising of MEMS-Based Inertial Data

Journal of Navigation ◽

10.1017/s0373463304002875 ◽

2004 ◽

Vol 57 (3) ◽

pp. 407-415 ◽

Cited By ~ 26

Author(s):

Ahmed El-Rabbany ◽

Mohammed El-Diasty

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Noise Suppression ◽

Back Propagation ◽

Frequency Noise ◽

Original Signal ◽

Back Propagation Algorithm ◽

Major Drawback ◽

Noise Component

Micro-Electro-Mechanical System (MEMS)-based inertial technology has recently evolved. It holds remarkable potential as the future technology for various navigation related applications. This is mainly due to the significant reduction in size, cost, and weight of MEMS sensors. A major drawback of low-cost MEMS-based inertial sensors, however, is that their output signals are contaminated by high-level noise. Unless the high frequency noise component is suppressed, optimizing the pre-filtering methodology cannot be achieved. This paper proposes a neural network-based de-noising model for MEMS-based inertial data. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Simulated and real MEMS-based inertial data sets are used to validate the model. It is shown that the model is capable of reducing the noise of the Crossbow's AHRS300CA IMU data by over one order of magnitude without altering the stochastic nature of the original signal. This is of utmost importance in developing a generic stochastic model for MEMS-based inertial data. A comparison between the developed neural network model and the wavelet de-noising method is made to further validate the model. It is shown that achieving the same level of noise suppression with wavelet-based de-noising model changes the stochastic characteristics of original signal.

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A Predictive Model for Mining Opinions of an Educational Database Using Neural Networks

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v5i5.pp1158-1163 ◽

2015 ◽

Vol 5 (5) ◽

pp. 1158 ◽

Cited By ~ 1

Author(s):

M R Narasinga Rao ◽

Deepthi Gurram ◽

Sai Mahathi Vadde ◽

Sathish Tallam ◽

N. Sai Chand ◽

...

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Educational Institution ◽

Research Performance ◽

Back Propagation ◽

Educational Data Mining ◽

Statistical Techniques ◽

Back Propagation Algorithm ◽

Prime Concern

<div class="WordSection1"><p>Assessing the performance of an educational institute is a prime concern in an educational scenario. Educational Data Mining (EDM) considers several tasks originated from an educational context. One of the tasks identified is providing feedback for supporting instructors, administrators, teachers, course authors in decision making and thereby enable them to take appropriate remedial action. In this research, we have developed a prototype Neural Network Model which is trained to predict the performance of an educational institution. A Multilayer Perceptron Neural Network (MLP) model had been developed for this proposed research. The network is trained by back propagation algorithm. Data was obtained from a well-defined questionnaire consisting of 14 questions in the domains namely Academic Schedule, International Exposure, Jobs and Internship, Quality of the college, and Life at Campus. The results of these questions have been taken as inputs and performance of the institute has been considered as the output. To, validate the results generated by the network, statistical techniques have been used for the purpose. In this proposed research performance of an educational institution has been predicted. The results generated by the Neural Network and the statistical techniques have been compared in this research and it is observed that, both the methods have generated accurate results. The results have been considered based on the Normalized System Error (NSE) values of the network. A prototype Neural Network model has been developed to assess the performance of an educational institution.</p><strong></strong></div>

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METHODOLOGY FOR DETERMINING THE TIMING OF THE MANUFACTURE OF NEW ORDERS IN SINGLE PRODUCTION AND REDUCING THE RISKS OF PRODUCTION ACTIVITIES

Izvestiya of Samara Scientific Center of the Russian Academy of Sciences ◽

10.37313/1990-5378-2021-23-4-27-34 ◽

2021 ◽

Vol 23 (4) ◽

pp. 27-34

Author(s):

E.A. Koleganova ◽

◽

V.V. Kokareva ◽

A.I. Khaimovich ◽

◽

...

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Back Propagation ◽

Neural Network Modeling ◽

High Tech ◽

Complex Method ◽

Back Propagation Algorithm ◽

Error Back Propagation ◽

Single Production

The article is devoted to the development and testing of the methodology for assigning the priority of technological operations for a number of orders and assessing the risks of setting the price and deadlines for new orders, taking into account it’s complexity and priority. It is noted the main problems of high-tech single production and the risks they create. To solve the identified problems on the basis of the analysis, a complex method was chosen, consisting of a combination of the use of simulation modeling and neural network modeling. The neural network model is based on the statistics of the production times of various parts in this area. The type of neural network model selected by the Fitting app is trained by the network using the Levenberg-Marquardt error back propagation algorithm. The simulation model of the production site is built in the Tecnomatix Plant Simulation program. As a result, thanks to the developed methodology, it became possible to obtain information about a new order before it was directly introduced into production, to diversify risks before they caused damage, as well as to improve reputation. In conclusion, as an example, the addition of a new part to other parts already being produced is given, and the time of its production is calculated.

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An Neural Network Model for the Fe/SiO2 Ratio in Copper Flash Smelting Slag Using Improved Back Propagation Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.524-527.1963 ◽

2012 ◽

Vol 524-527 ◽

pp. 1963-1966

Author(s):

Hong Lu

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Local Minimum ◽

Back Propagation ◽

Flash Smelting ◽

Smelting Process ◽

Back Propagation Algorithm ◽

Technological Parameters ◽

Smelting Slag

The Fe/SiO2 ratio in slag is one of the important control parameters for copper flash smelting process, but it is difficult to describe the complex relationship between the technological parameters and the Fe/SiO2 ratio in slag using accurate mathematic formulae, because the copper flash smelting process is a complicated nonlinear system. An neural network model for the Fe/SiO2 ratio in copper flash smelting slag was developed, whose net structure is 8-15-12-1, and input nodes include the oxygen volume per ton concentrate, the oxygen grade, the flux rate, the quantity of Cu, S, Fe, SiO2 and MgO in concentrate. In order to avoid local minimum terminations when the model is trained by back propagation (BP) algorithm, a new algorithm called GA-BP is presented by using genetic algorithm (GA) to determine the initial weights and threshold values. The results show that the model can avoid local minimum terminations and accelerate convergence, and has high prediction precision and good generalization performance. The model can be used to optimize the copper flash smelting process control.

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Neural Network Model Using Back-Propagation Algorithm with Momentum Term for Credit Risk Evaluation System

Proceedings of 2016 the 6th International Workshop on Computer Science and Engineering ◽

10.18178/wcse.2016.06.084 ◽

2016 ◽

Keyword(s):

Neural Network ◽

Credit Risk ◽

Network Model ◽

Neural Network Model ◽

Risk Evaluation ◽

Evaluation System ◽

Back Propagation ◽

Back Propagation Algorithm ◽

Propagation Algorithm ◽

Risk Evaluation System

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DEVELOPMENT OF THE NEURAL NETWORK FOR SOLVING THE PROBLEM OF SPEECH RECOGNITION

NEWS OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN ◽

10.32014/2021.2224-5294.3 ◽

2021 ◽

Vol 335 (1) ◽

pp. 19-25

Author(s):

M. Dilmagambetova ◽

O. Mamyrbayev

Keyword(s):

Neural Network ◽

Speech Recognition ◽

Network Model ◽

Neural Network Model ◽

Back Propagation ◽

Training Sample ◽

Generalization Error ◽

Back Propagation Algorithm ◽

Learning Sequence ◽

Dynamic Learning

The article discusses a method for solving the problem of speech recognition on the example of recognizing individual words of a limited dictionary using a forward propagation neural network trained by the error back propagation method. The goal was to create a neural network model for recognizing the solution of individual words, analyze the training characteristics and behavior of the constructed neural network. Based on the input data and output requirements, a feedback neural network selected. To train the selected neural network model, a back propagation algorithm was chosen. The developed neural network demonstrated the expected behavior associated with learning and generalization errors. It found that even if the generalization error decreases as the learning sequence increases, the errors begin to fluctuate regardless of the introduction of a dynamic learning rate. The network sufficiently trained to meet the generalization error requirements, but there is stillroom to improve the generalization error. Practical results of training the constructed neural network at different sizes of the training sample presented.

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DEVELOPMENT OF THE NEURAL NETWORK FOR SOLVING THE PROBLEM OF SPEECH RECOGNITION

NEWS OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN ◽

10.32014/2021.2518-1726.3 ◽

2021 ◽

Vol 335 (1) ◽

pp. 19-25

Author(s):

M. Dilmagambetova ◽

O. Mamyrbayev

Keyword(s):

Neural Network ◽

Speech Recognition ◽

Network Model ◽

Neural Network Model ◽

Back Propagation ◽

Training Sample ◽

Generalization Error ◽

Back Propagation Algorithm ◽

Learning Sequence ◽

Dynamic Learning

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Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L2,1-Norm

BioMed Research International ◽

10.1155/2021/6627650 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Xiaoyi Guo ◽

Wei Zhou ◽

Qun Lu ◽

Aiyan Du ◽

Yinghua Cai ◽

...

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Dry Weight ◽

Hemodialysis Patients ◽

Normal Weight ◽

The Body ◽

Functional Link ◽

Patient Will ◽

Better Than

Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the patient will experience hypotension and shock symptoms. Therefore, the correct assessment of the patient’s dry weight is clinically important. These methods all rely on professional instruments and technicians, which are time-consuming and labor-intensive. To avoid this limitation, we hope to use machine learning methods on patients. This study collected demographic and anthropometric data of 476 hemodialysis patients, including age, gender, blood pressure (BP), body mass index (BMI), years of dialysis (YD), and heart rate (HR). We propose a Sparse Laplacian regularized Random Vector Functional Link (SLapRVFL) neural network model on the basis of predecessors. When we evaluate the prediction performance of the model, we fully compare SLapRVFL with the Body Composition Monitor (BCM) instrument and other models. The Root Mean Square Error (RMSE) of SLapRVFL is 1.3136, which is better than other methods. The SLapRVFL neural network model could be a viable alternative of dry weight assessment.

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