scholarly journals Artificial neural network identifies nonsteroidal anti‐inflammatory drugs exacerbated respiratory disease (N‐ERD) cohort

Allergy ◽  
2020 ◽  
Vol 75 (7) ◽  
pp. 1649-1658
Author(s):  
Katarzyna Ewa Tyrak ◽  
Kinga Pajdzik ◽  
Ewa Konduracka ◽  
Adam Ćmiel ◽  
Bogdan Jakieła ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Respiratory Disease ◽  
Inflammatory Drugs ◽  
Artificial Neural ◽  
Anti Inflammatory

Artificial neural network for the voltamperometric quantification of diclofenac in presence of other nonsteroidal anti-inflammatory drugs and some commercial excipients

2017 ◽  
Vol 801 ◽  
pp. 527-535 ◽  
Author(s):  
G.Y. Aguilar-Lira ◽  
J.M. Gutiérrez-Salgado ◽  
A. Rojas-Hernández ◽  
J.A. Rodríguez-Ávila ◽  
M.E. Páez-Hernández ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inflammatory Drugs ◽  
Artificial Neural ◽  
Anti Inflammatory

scholarly journals Intestinal anti-inflammatory effects of artichoke pectin and modified pectin fractions in the dextran sulfate sodium model of mice colitis. Artificial neural network modelling of inflammatory markers

2019 ◽  
Vol 10 (12) ◽  
pp. 7793-7805 ◽  
Author(s):  
Carlos Sabater ◽  
Jose Alberto Molina-Tijeras ◽  
Teresa Vezza ◽  
Nieves Corzo ◽  
Antonia Montilla ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inflammatory Markers ◽  
Dextran Sulfate ◽  
Dextran Sulfate Sodium ◽  
Network Modelling ◽  
Neural Network Modelling ◽  
Artificial Neural ◽  
Anti Inflammatory ◽  
Artificial Neural Network Modelling

Anti-inflammatory properties of artichoke pectin and modified fractions (arabinose- and galactose-free) used at two doses (40 and 80 mg kg−1) in mice with colitis induced by dextran sulfate sodium have been investigated.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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