scholarly journals Ambiguity and Social Judgment: Fuzzy Set Model and Data Analysis

10.5772/35454 ◽  
2012 ◽  
Author(s):  
Kazuhisa Takemura
Keyword(s):  
Data Analysis ◽  
Fuzzy Set ◽  
Social Judgment

Innovative Formalism for Biological Data Analysis

2018 ◽  
pp. 1814-1824
Author(s):  
Calin Ciufudean
Keyword(s):  
Information Technology ◽  
Data Analysis ◽  
Medical Devices ◽  
Fuzzy Set ◽  
Biological Data ◽  
Fuzzy Operators ◽  
Biological Data Analysis ◽  
Biological Signals ◽  
Design And Manufacture ◽  
Gathering Data

Modern medical devices involves information technology (IT) based on electronic structures for data and signals sensing and gathering, data and signals transmission as well as data and signals processing in order to assist and help the medical staff to diagnose, cure and to monitors the evolution of patients. By focusing on biological signals processing we may notice that numerical processing of information delivered by sensors has a significant importance for a fair and optimum design and manufacture of modern medical devices. We consider for this approach fuzzy set as a formalism of analysis of biological signals processing and we propose to be accomplished this goal by developing fuzzy operators for filtering the noise of biological signals measurement. We exemplify this approach on neurological measurements performed with an Electro-Encephalograph (EEG).

Innovative Formalism for Biological Data Analysis

2019 ◽  
pp. 390-402
Author(s):  
Calin Ciufudean
Keyword(s):  
Information Technology ◽  
Data Analysis ◽  
Medical Devices ◽  
Fuzzy Set ◽  
Biological Data ◽  
Fuzzy Operators ◽  
Biological Data Analysis ◽  
Biological Signals ◽  
Design And Manufacture ◽  
Gathering Data

Modern medical devices involve information technology (IT) based on electronic structures for data and signals sensing and gathering, data and signals transmission, as well as data and signals processing in order to assist and help the medical staff to diagnose, cure, and to monitor the evolution of patients. By focusing on biological signals processing we may notice that numerical processing of information delivered by sensors has a significant importance for a fair and optimum design and manufacture of modern medical devices. The authors consider for this approach fuzzy set as a formalism of analysis of biological signals processing and they propose to accomplish this goal by developing fuzzy operators for filtering the noise of biological signals measurement. The authors exemplify this approach on neurological measurements performed with an electro-encephalograph (EEG).

scholarly journals Ambiguous comparative judgment: Fuzzy set model and data analysis

2007 ◽  
Vol 49 (2) ◽  
pp. 148-156 ◽  
Author(s):  
KAZUHISA TAKEMURA
Keyword(s):  
Data Analysis ◽  
Fuzzy Set ◽  
Comparative Judgment

scholarly journals FM-test: a fuzzy-set-theory-based approach to differential gene expression data analysis

2006 ◽  
Vol 7 (Suppl 4) ◽  
pp. S7 ◽  
Author(s):  
Lily R Liang ◽  
Shiyong Lu ◽  
Xuena Wang ◽  
Yi Lu ◽  
Vinay Mandal ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Differential Gene Expression ◽  
Set Theory ◽  
Gene Expression Data ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Differential Gene

scholarly journals Using mixed research approaches to understand rural depopulation

2019 ◽  
Vol 19 (1) ◽  
pp. 99 ◽  
Author(s):  
Luisa Alamá-Sabater ◽  
Vicente Budí ◽  
José María García-Álvarez-Coque ◽  
Norat Roig-Tierno
Keyword(s):  
Data Analysis ◽  
Comparative Analysis ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Qualitative Comparative Analysis ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Regional Policies ◽  
Rural Depopulation ◽  
Urbanized Region

<span lang="EN-US">There is a growing consensus on the need to propose specific policies to face rural depopulation. This article applies fuzzy-set Qualitative Comparative Analysis (QCA) to define the presence or absence in each municipality of the conditions leading to the presence or absence of depopulation. We also perform Exploratory Spatial Data Analysis (ESDA) of population growth to identify hotspots of rural depopulation. The methodologies prove useful to evaluate and guide regional policies that address depopulation processes in the context of a relatively urbanized region.</span>

scholarly journals Using mixed research approaches to understand rural depopulation

2019 ◽  
Vol 19 (1) ◽  
pp. 99
Author(s):  
Luisa Alamá-Sabater ◽  
Vicente Budí ◽  
Jose Maria García-Álvarez-Coque ◽  
Norat Roig-Tierno
Keyword(s):  
Data Analysis ◽  
Comparative Analysis ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Qualitative Comparative Analysis ◽  
Spatial Data Analysis ◽  
Exploratory Spatial Data Analysis ◽  
Regional Policies ◽  
Rural Depopulation ◽  
Urbanized Region

<span lang="EN-US">There is a growing consensus on the need to propose specific policies to face rural depopulation. This article applies fuzzy-set Qualitative Comparative Analysis (QCA) to define the presence or absence in each municipality of the conditions leading to the presence or absence of depopulation. We also perform Exploratory Spatial Data Analysis (ESDA) of population growth to identify hotspots of rural depopulation. The methodologies prove useful to evaluate and guide regional policies that address depopulation processes in the context of a relatively urbanized region.</span>

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