Study on Logistics Center Location Judgment Based on Artificial Neural Network

2009 ◽  
Author(s):  
Jiang Hua ◽  
Liang Qi-hong
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistics Center ◽  
Center Location ◽  
Artificial Neural ◽  
Location Judgment

An Integrated Predicting Model of New-Built Regional Logistics Center’s Demand Based on the Artificial Neural Network

2012 ◽  
Vol 253-255 ◽  
pp. 1512-1517
Author(s):  
Jian Feng Luo ◽  
Tian Shan Ma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Factor Analysis ◽  
Back Propagation ◽  
Demand Forecast ◽  
Logistics Center ◽  
Bp Artificial Neural Network ◽  
Regional Logistics ◽  
Artificial Neural ◽  
Logistics Demand

In order to predict the scale of logistics demand for a new-built regional center, economic indicators and the other related measuring indicator of the scale for logistics demand is studied. The factor analysis and back propagation (BP) artificial neural network theory are applied to set up a model for predicting the scale of the logistics center’ s demand. The factor analysis is applied to this model to reduce the number of indicators of the input layer in the BP artificial neural network, and to reduce complexity. Then model is introduced to fit historical data of the scale of new –built a regional logistics center’ s demand .Finally,a third-layer BP artificial neural network is constructed. This model was applied to predict the scale of the logistics demand in an example and the forecasting result shows that forecasting accuracy of the model is good. It also provides a new way of a new-built regional logistics center’ s demand forecast.

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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