scholarly journals CLASSIFICATION OF EXPENSES AND COST FORMATION OF DENTAL CLINIC SERVICES IN A MULTI-LEVEL DATA STRUCTURE

2018 ◽  
Vol 12 (2) ◽  
pp. 104-113
Author(s):  
Maxim Gvozdev ◽  
Taya Denisova ◽  
Marina Shilova ◽  
Anna Mishchenko
Keyword(s):  
Data Structure ◽  
Dental Clinic ◽  
Level Data ◽  
Multi Level

scholarly journals Note on the Classification of Multi-Level Data

1967 ◽  
Vol 9 (4) ◽  
pp. 381-382 ◽  
Author(s):  
G. N. Lance ◽  
W. T. Williams
Keyword(s):  
Level Data ◽  
Multi Level

scholarly journals CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images

2021 ◽  
pp. 1-15
Author(s):  
Subrato Bharati ◽  
Prajoy Podder ◽  
M. Rubaiyat Hossain Mondal ◽  
V.B. Surya Prasath
Keyword(s):  
Classification Problem ◽  
Operating Characteristics ◽  
X Ray ◽  
Level Data ◽  
Receiver Operating Characteristics Curve ◽  
Multi Level ◽  
Novel Coronavirus ◽  
Normal Lungs ◽  
Healthy Lung

This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.

scholarly journals Determinants of Consistent Condom Use Vary by Partner Type among Young Men in Kisumu, Kenya: A Multi-level Data Analysis

2008 ◽  
Vol 14 (4) ◽  
pp. 949-959 ◽  
Author(s):  
Nelli Westercamp ◽  
Christine L. Mattson ◽  
Michelle Madonia ◽  
Stephen Moses ◽  
Kawango Agot ◽  
...  
Keyword(s):  
Data Analysis ◽  
Condom Use ◽  
Young Men ◽  
Consistent Condom ◽  
Consistent Condom Use ◽  
Partner Type ◽  
Level Data ◽  
Multi Level

Improved multi-level data storage properties of germanium-antimony-tellurium films by nitrogen doping

2017 ◽  
Vol 141 ◽  
pp. 120-124 ◽  
Author(s):  
Xiao Yu ◽  
Yue Zhao ◽  
Chao Li ◽  
Chaoquan Hu ◽  
Liang Ma ◽  
...  
Keyword(s):  
Data Storage ◽  
Nitrogen Doping ◽  
Level Data ◽  
Multi Level ◽  
Storage Properties

scholarly journals Critical Land Detection using Remote Sensing Device and Geographic Information (Case Study of Critical Land Basic of Catchments Area, Alang, Wonogiri)

2017 ◽  
Vol 19 (1) ◽  
pp. 1
Author(s):  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Field Survey ◽  
Critical Level ◽  
Satellite Image ◽  
Geographic Information ◽  
Level Data ◽  
The Difference

Work criteria and indicator of Catchments Area need to be determined because the success and the failure of cultivating Catchments Area can be monitored and evaluated through the determined criteria. Criteria Indicators in utilizing land, one of them is determined based on the erosion index and the ability of utilizing land, for analyzing the land critical level. However, the determination of identification and classification of land critical level has not been determined; as a result the measurement of how wide the real critical land is always changed all the year. In this study, it will be tried a formula to determine the land critical/eve/ with various criteria such as: Class KPL (Ability of Utilizing Land) and the difference of the erosion tolerance value with the great of the erosion compared with land critical level analysis using remote sensing devices. The aim of studying land critical level detection using remote sensing tool and Geographic Information System (SIG) are:1. The backwards and the advantages of critical and analysis method2. Remote Sensing Method for critical and classification3. Critical/and surveyed method in the field (SIG) Collecting and analyzing data can be found from the field survey and interpretation of satellite image visually and using computer. The collected data are analyzed as:a. Comparing the efficiency level and affectivity of collecting biophysical data through field survey, sky photo interpretation, and satellite image analysis.b. Comparing the efficiency level and affectivity of land critical level data that are found from the result of KPL with the result of the measurement of the erosion difference and erosion tolerance.

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