ANALYSING PATTERNS OF TRIPEPTIDES USING STATISTICAL APPROACH AND NEURAL NETWORK PARADIGM

2005 ◽  
Author(s):  
RAISA SZABO ◽  
MATTEW HE ◽  
ERICK BURNHAM ◽  
JESSICA JURANI
Keyword(s):  
Neural Network ◽  
Statistical Approach

scholarly journals Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 588
Author(s):  
Felipe Leite Coelho da Silva ◽  
Kleyton da Costa ◽  
Paulo Canas Rodrigues ◽  
Rodrigo Salas ◽  
Javier Linkolk López-Gonzales
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Statistical Approach ◽  
Electricity Consumption ◽  
Industrial Sector ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
The One ◽  
Mlp Model

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.

scholarly journals Prediction of fragmentation of kidney stones: A statistical approach from NCCT images

2016 ◽  
Vol 10 (7-8) ◽  
pp. 237 ◽  
Author(s):  
Krishna Moorthy ◽  
Meenakshy Krishnan
Keyword(s):  
Neural Network ◽  
Renal Stone ◽  
Statistical Approach ◽  
Region Of Interest ◽  
Stone Disease ◽  
True Negative ◽  
The Neural Network ◽  
Neural Network System ◽  
Percent Accuracy ◽  
And Training

<p><strong>Introduction:</strong> We sought to develop a system to predict the fragmentation of stones using non-contrast computed tomography (NCCT) image analysis of patients with renal stone disease.</p><p><strong>Methods:</strong> The features corresponding to first order statistical (FOS) method were extracted from the region of interest in the NCCT scan image of patients undergoing extracorporeal shockwave lithotripsy (ESWL) treatment and the breakability was predicted using neural network.</p><p><strong> Results:</strong> When mean was considered as the feature, the results indicated that the model developed for prediction had sensitivity of 80.7% in true positive (TP) cases. The percent accuracy in identifying correctly the TP and true negative (TN) cases was 90%. TN cases were identified with a specificity of 98.4%.</p><p><strong>Conclusions:</strong> Application of statistical methods and training the neural network system will enable accurate prediction of the fragmentation and outcome of ESWL treatment.</p>

Driving Data Classification and Analysis using Statistical Approach & Neural Network

2021 ◽  
Author(s):  
Hanumant Kumar Yugank ◽  
Jasleen Kaur ◽  
Sanmukh Kaur ◽  
Bedatri Moulik
Keyword(s):  
Neural Network ◽  
Statistical Approach ◽  
Data Classification

scholarly journals Building neural network models for time series: a statistical approach

2005 ◽  
Vol 25 (1) ◽  
pp. 49-75 ◽  
Author(s):  
Marcelo C. Medeiros ◽  
Timo Teräsvirta ◽  
Gianluigi Rech
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Statistical Approach ◽  
Network Models ◽  
Neural Network Models
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