A Shrewd Artificial Neural Network-Based Hybrid Model for Pervasive Stress Detection of Students Using Galvanic Skin Response and Electrocardiogram Signals

Big Data ◽  
2021 ◽  
Author(s):  
Sadhana Tiwari ◽  
Sonali Agarwal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hybrid Model ◽  
Galvanic Skin Response ◽  
Stress Detection ◽  
Electrocardiogram Signals ◽  
Skin Response ◽  
Artificial Neural

MATHEMATICAL-ARTIFICIAL NEURAL NETWORK HYBRID MODEL TO PREDICT ROLL FORCE DURING HOT ROLLING OF STEEL

2013 ◽  
Vol 02 (01) ◽  
pp. 1350004 ◽  
Author(s):  
S. RATH ◽  
P. P. SENGUPTA ◽  
A. P. SINGH ◽  
A. K. MARIK ◽  
P. TALUKDAR
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Artificial Neural Network ◽  
Flow Stress ◽  
Mathematical Models ◽  
Hybrid Model ◽  
Data Driven ◽  
Roll Force ◽  
Hot Strip ◽  
Artificial Neural

Accurate prediction of roll force during hot strip rolling is essential for model based operation of hot strip mills. Traditionally, mathematical models based on theory of plastic deformation have been used for prediction of roll force. In the last decade, data driven models like artificial neural network have been tried for prediction of roll force. Pure mathematical models have accuracy limitations whereas data driven models have difficulty in convergence when applied to industrial conditions. Hybrid models by integrating the traditional mathematical formulations and data driven methods are being developed in different parts of world. This paper discusses the methodology of development of an innovative hybrid mathematical-artificial neural network model. In mathematical model, the most important factor influencing accuracy is flow stress of steel. Coefficients of standard flow stress equation, calculated by parameter estimation technique, have been used in the model. The hybrid model has been trained and validated with input and output data collected from finishing stands of Hot Strip Mill, Bokaro Steel Plant, India. It has been found that the model accuracy has been improved with use of hybrid model, over the traditional mathematical model.

scholarly journals Analysis of ARIMA-Artificial Neural Network Hybrid Model in Forecasting of Stock Market Returns

2020 ◽  
pp. 42-53
Author(s):  
Yakubu Musa ◽  
Stephen Joshua
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Stock Market ◽  
Hybrid Model ◽  
Market Returns ◽  
Stock Market Returns ◽  
Artificial Neural ◽  
Modelling And Forecasting

This study focuses on the modelling of Nigerian stock market all–shares index and evaluations of predictions ability using ARIMA, Artificial Neural Network and a hybrid ARIMA-Artificial Neural Network model. The ARIMA (1,1,1) model and neural network with architecture (6:1:3) turns out to be the most fitted among the considered models, these models were used for forecasting the returns, and their performances have been compared according to some statistical measure of accuracy. A hybrid model has been constructed using ARIMA-Artificial Neural Networks model, the computational results on the data reveal that the hybrid model using Artificial Neural Network, provides better forecasts, and will enhance forecasting over the single ARIMA and Artificial Neural Networks models. The study recommends the use of ARIMA-Artificial neural network for modelling and forecasting stock market returns.

Methodology

2018 ◽  
pp. 81-113
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Swarm Intelligence ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Artificial Neural ◽  
Heart Disorders ◽  
Intelligent Methods

This chapter uses intelligent methods based on swarm intelligence and artificial neural network to detect heart disorders based on electrocardiogram signals. This chapter has introduced the methodology undertaken in the denoising, feature extraction, and classification of ECG signals to four heart disorders including the normal heartbeat. It also presents denoising using intelligent methods.

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