A Study of the Psychological Resilience of the Elderly Based on the Artificial Neural Network

2018 ◽  
Author(s):  
Chang Song ◽  
Yoshito Ogata ◽  
Jia He
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
The Elderly ◽  
Psychological Resilience ◽  
Artificial Neural

scholarly journals An artificial neural network to classify healthy aging in elderly Brazilians

2020 ◽  
Vol 9 (5) ◽  
pp. 158-162
Author(s):  
Ágatha Yasmin de Sousa Araujo ◽  
Maylon Sivalcley da Costa Rocha ◽  
Elton Rafael Alves ◽  
Ana Cristina Viana Campos
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Healthy Aging ◽  
Social Inequalities ◽  
Confusion Matrix ◽  
The Elderly ◽  
Visual Basic For Applications ◽  
Collection Data ◽  
Artificial Neural

Aging in Brazil, especially in the Amazon, is a complex and irregular process. Something is happening here that cannot be explained simply due to social inequalities. The objective of this study was to present the development of an artificial neural network and the stages of training, validation and testing for the classification of healthy aging among elderly Brazilians. We constructed a protocol for rapid diagnosis and health screening for the elderly. The form was developed offline in Microsoft Excel. Macros (routines capable of performing pre-programmed tasks) were created using Microsoft's Visual Basic for Applications (VBA) language. In the analysis of the confusion matrix, good accuracy were obtained in all stages, training (61.5%), validation (60.0%) and test (80.0%), which indicates that the network learned through the inputs and outputs initially defined and during the sample divisions performed for testing and validation. In the test stage, a ROC curve was obtained with better true positive rates and lower false positive rates, being close to the Y axis (left side), thus indicating better results. We conducted a pilot study with thirty-six community active elderlies from a city in Eastern Amazonia, Brazil. This study was divided into four parts: data collection, data pre-processing, training of an artificial neural network and evaluation methods.

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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