scholarly journals IMPROVEMENT OF THE F-PERCEPTORY APPROACH THROUGH MANAGEMENT OF FUZZY COMPLEX GEOGRAPHIC OBJECTS

2015 ◽  
Vol II-3/W5 ◽  
pp. 235-241 ◽  
Author(s):  
B. Khalfi ◽  
C. de Runz ◽  
S. Faiz ◽  
H. Akdag
Keyword(s):  
Decision Making ◽  
Data Structures ◽  
Real World ◽  
Geographic Information ◽  
Second Step ◽  
Fuzzy Data ◽  
Real World Data ◽  
Time Space ◽  
Uml Modeling ◽  
Modeling Data

In the real world, data is imperfect and in various ways such as imprecision, vagueness, uncertainty, ambiguity and inconsistency. For geographic data, the fuzzy aspect is mainly manifested in time, space and the function of objects and is due to a lack of precision. Therefore, the researchers in the domain emphasize the importance of modeling data structures in GIS but also their lack of adaptation to fuzzy data. The F-Perceptory approachh manages the modeling of imperfect geographic information with UML. This management is essential to maintain faithfulness to reality and to better guide the user in his decision-making. However, this approach does not manage fuzzy complex geographic objects. The latter presents a multiple object with similar or different geographic shapes. So, in this paper, we propose to improve the F-Perceptory approach by proposing to handle fuzzy complex geographic objects modeling. In a second step, we propose its transformation to the UML modeling.

Inverse DEA: Review, Extension and Application

2015 ◽  
Vol 14 (04) ◽  
pp. 805-824 ◽  
Author(s):  
Saeid Ghobadi ◽  
Saeid Jahangiri
Keyword(s):  
Decision Making ◽  
Real World ◽  
Fuzzy Data ◽  
Sufficient Condition ◽  
Real World Data ◽  
World Data ◽  
Multi Objective ◽  
Theoretical Part ◽  
Methodological Approaches ◽  
Theoretical Results

This paper deals with inverse DEA from both theoretical and applied viewpoints. First, some methodological approaches and theoretical results are outlined; and then an application of DEA with real-world data (for assessing educational departments in a university) is addressed. Afterwards, possible extensions and applications of the existing approaches in the presence of fuzzy data are developed. The final theoretical part of the paper contains a main theorem which provides a sufficient condition for efficiency maintaining in the presence of fuzzy data. To do this, we have used some notions/results from multi-objective decision-making theory.

Quality criteria for Real-World Data in pharmaceutical research and healthcare decision making. An Austrian Expert Consensus (Preprint)

2021 ◽  
Author(s):  
Peter Klimek ◽  
Dejan Baltic ◽  
Martin Brunner ◽  
Alexander Degelsegger-Marquez ◽  
Gerhard Garhöfer ◽  
...  
Keyword(s):  
Decision Making ◽  
Medical Research ◽  
Real World ◽  
Pharmaceutical Medicine ◽  
Pharmaceutical Research ◽  
Quality Criteria ◽  
Clinical Decision ◽  
European Medicines Agency ◽  
Real World Data ◽  
World Data

UNSTRUCTURED Real-world data (RWD) collected in routine healthcare processes and transformed to real-world evidence (RWE) has become increasingly interesting within research and medical communities to enhance medical research and support regulatory decision making. Despite numerous European initiatives, there is still no cross-border consensus or guideline determining which quality RWD must meet in order to be acceptable for decision making within regulatory or routine clinical decision support. An Austrian expert group led by GPMed (Gesellschaft für Pharmazeutische Medizin, Austrian Society for Pharmaceutical Medicine) reviewed drafted guidelines, published recommendations or viewpoints to derive a consensus statement on quality criteria for RWD to be used more effectively for medical research purposes beyond registry-based studies discussed in the European Medicines Agency (EMA) guideline for registry-based studies

Automated Diagnosis through Ontologies and Logical Descriptions

2013 ◽  
pp. 117-134
Author(s):  
Alejandro Rodríguez-González ◽  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
José Emilio Labra Gayo ◽  
Juan Miguel Gómez-Berbís ◽  
...  
Keyword(s):  
Decision Making ◽  
Health Systems ◽  
Real World ◽  
Information Age ◽  
Semantic Technologies ◽  
Automated Diagnosis ◽  
Real World Data ◽  
World Data ◽  
Degree Of Maturity ◽  
Automate Diagnosis

The combination of the burgeoning interest in efficient and reliable Health Systems and the advent of the Information Age represent both a challenge and an opportunity for new paradigms and cutting-edge technologies reaching a certain degree of maturity. Hence, the use of Semantic Technologies for Automated Diagnosis could leverage the potential of current solutions by providing inference-based knowledge and support on decision-making. This paper presents the ADONIS approach, which harnesses the use of ontologies and the underlying logical mechanisms to automate diagnosis and provide significant quality results in its evaluation on real-world data scenarios.

Model Data

2021 ◽  
pp. 95-188
Author(s):  
Kai R. Larsen ◽  
Daniel S. Becker
Keyword(s):  
Real World ◽  
Diabetes Patient ◽  
Model Data ◽  
Real World Data ◽  
Future Events ◽  
Patient’S Information ◽  
The Subject ◽  
Modeling Data ◽  
Using Data ◽  
World Environment

After preparing your dataset, the business problem should be quite familiar, along with the subject matter and the content of the dataset. This section is about modeling data, using data to train algorithms to create models that can be used to predict future events or understand past events. The section shows where data modeling fits in the overall machine learning pipeline. Traditionally, we store real-world data in one or more databases or files. This data is extracted, and features and a target (T) are created and submitted to the “Model Data” stage (the topic of this section). Following the completion of this stage, the model produced is examined (Section V) and placed into production. With the model in the production system, present data generated from the real-world environment is inputted into the system. In the example case of a diabetes patient, we enter a new patient’s information electronic health record into the system, and a database lookup retrieves additional data for feature creation.

Activities for Students: As the Ball Rolls: A Quadratic Investigation Using Multiple Representations

2009 ◽  
Vol 103 (1) ◽  
pp. 62-68
Author(s):  
Kathleen Cage Mittag ◽  
Sharon Taylor
Keyword(s):  
Data Collection ◽  
Real Time ◽  
Physical Model ◽  
Real World ◽  
Graphing Calculator ◽  
Data Sets ◽  
Real World Data ◽  
World Data ◽  
Hands On ◽  
Modeling Data

Using activities to create and collect data is not a new idea. Teachers have been incorporating real-world data into their classes since at least the advent of the graphing calculator. Plenty of data collection activities and data sets exist, and the graphing calculator has made modeling data much easier. However, the authors were in search of a better physical model for a quadratic. We wanted students to see an actual parabola take shape in real time and then explore its characteristics, but we could not find such a hands-on model.

scholarly journals Neural Networks in Credit Risk Classification of Companies in the Construction Sector

2018 ◽  
Vol 2 (2) ◽  
pp. 63-77 ◽  
Author(s):  
Aleksandra Wójcicka
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Credit Risk ◽  
Financial Institutions ◽  
Real World ◽  
Decision Making Process ◽  
Construction Sector ◽  
Real World Data ◽  
Input Variables

The financial sector (banks, financial institutions, etc.) is the sector most exposed to financial and credit risk, as one of the basic objectives of banks' activity (as a specific enterprise) is granting credit and loans. Because credit risk is one of the problems constantly faced by banks, identification of potential good and bad customers is an extremely important task. This paper investigates the use of different structures of neural networks to support the preliminary credit risk decision-making process. The results are compared among the models and juxtaposed with real-world data. Moreover, different sets and subsets of entry data are analyzed to find the best input variables (financial ratios).

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