scholarly journals A decision-making support system for child care facilities using GIS

1996 ◽  
Vol 4 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Sachiko SEGAWA ◽  
Yukio SADAHIRO
Keyword(s):  
Decision Making ◽  
Child Care ◽  
Support System ◽  
Care Facilities ◽  
Child Care Facilities ◽  
Decision Making Support

A Study on the Regional Inequality of Child Care Facilities

2019 ◽  
Vol 31 (3) ◽  
pp. 525-554
Author(s):  
Sock-Hwan Lee ◽  
Jin-Young Kim
Keyword(s):  
Child Care ◽  
Regional Inequality ◽  
Care Facilities ◽  
Child Care Facilities

Child-specific Health Risk Assessment of 25 Indoor Air pollutants in Child-care Facilities and Indoor Playgrounds

Epidemiology ◽  
2011 ◽  
Vol 22 ◽  
pp. S277
Author(s):  
Young Wook Lim ◽  
Ho Hyun Kim ◽  
Chung Soo Lee ◽  
Su Hee Yang ◽  
Sun Duk Kim ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Child Care ◽  
Health Risk ◽  
Health Risk Assessment ◽  
Indoor Air ◽  
Air Pollutants ◽  
Care Facilities ◽  
Child Care Facilities ◽  
Indoor Air Pollutants ◽  
Specific Health

Use of a Statewide System to Improve Health and Safety in Child Care Facilities

2001 ◽  
Vol 1 (2) ◽  
pp. 73-78 ◽  
Author(s):  
R. Amofah Dayie ◽  
S.S. Aronson ◽  
L. Jansen-McWilliams ◽  
K.J. Kelleher
Keyword(s):  
Child Care ◽  
Health And Safety ◽  
Care Facilities ◽  
Child Care Facilities ◽  
Improve Health

scholarly journals Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources

2021 ◽  
Vol 11 (13) ◽  
pp. 6237
Author(s):  
Azharul Islam ◽  
KyungHi Chang
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Random Forest ◽  
Support System ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Unstructured Data ◽  
Stochastic Gradient Descent ◽  
Decision Making Support

Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an informational and decision-making support system (DMSS). We often have assortments of information collected by mining data from various sources, where the key challenge is to extract valuable information. We observe substantial classification accuracy enhancement for our datasets with both machine learning and deep learning algorithms. The highest classification accuracy (99% in training, 96% in testing) was achieved from a Covid corpus which is processed by using a long short-term memory (LSTM). Furthermore, we conducted tests on large datasets relevant to the Disaster corpus, with an LSTM classification accuracy of 98%. In addition, random forest (RF), a machine learning algorithm, provides a reasonable 84% accuracy. This research’s main objective is to increase the application’s robustness by integrating intelligence into the developed DMSS, which provides insight into the user’s intent, despite dealing with a noisy dataset. Our designed model selects the random forest and stochastic gradient descent (SGD) algorithms’ F1 score, where the RF method outperforms by improving accuracy by 2% (to 83% from 81%) compared with a conventional method.

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