scholarly journals Fuzzy database for medical diagnosis.

2021 ◽  
Author(s):  
Rehana Parvin
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Database Systems ◽  
Database System ◽  
Inference System ◽  
Related Information ◽  
Data Value ◽  
Schema Design ◽  
Health Related ◽  
Fuzzy Database

A challenge of working with traditional database systems with large amounts of data is that decision making requires numerous comparisons. Health-related database systems are examples of such databases, which contain millions of data entries and require fast data processing to examine related information to make complex decisions. In this thesis, a fuzzy database system is developed by integration of fuzzy inference system (FIS) and fuzzy schema design, and implementing it by SQL in three different health-care contexts; the assessments of heart disease, diabetes mellitus, and liver disorders. The fuzzy database system is implemented with the potential of having any form of data and tested with different types of data value, including crisp, linguistic, and null (i.e., missing) data. The developed system can explore crisp and linguistic data with loosely defined boundary conditions for decision-making. FIS and neural network-based solutions are implemented in MATLAB for the mentioned contexts for the comparison and validation with the dataset used in published works.

scholarly journals Fuzzy database for medical diagnosis.

2021 ◽  
Author(s):  
Rehana Parvin
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Database Systems ◽  
Database System ◽  
Inference System ◽  
Related Information ◽  
Data Value ◽  
Schema Design ◽  
Health Related ◽  
Fuzzy Database

A challenge of working with traditional database systems with large amounts of data is that decision making requires numerous comparisons. Health-related database systems are examples of such databases, which contain millions of data entries and require fast data processing to examine related information to make complex decisions. In this thesis, a fuzzy database system is developed by integration of fuzzy inference system (FIS) and fuzzy schema design, and implementing it by SQL in three different health-care contexts; the assessments of heart disease, diabetes mellitus, and liver disorders. The fuzzy database system is implemented with the potential of having any form of data and tested with different types of data value, including crisp, linguistic, and null (i.e., missing) data. The developed system can explore crisp and linguistic data with loosely defined boundary conditions for decision-making. FIS and neural network-based solutions are implemented in MATLAB for the mentioned contexts for the comparison and validation with the dataset used in published works.

scholarly journals Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

CAUCHY ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 10 ◽  
Author(s):  
Venny Riana Riana Agustin ◽  
Wahyu Henky Irawan
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Weighted Average ◽  
Fuzzy Rules ◽  
Quality Of Services ◽  
Inference System ◽  
A Value ◽  
Fuzzy Logic Analysis

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

Depressed Latinx Older Adults’ Shared Decision-Making Preferences

2021 ◽  
Author(s):  
Sara Romero ◽  
Patrick Raue ◽  
Andrew Rasmussen
Keyword(s):  
Primary Care ◽  
Decision Making ◽  
Puerto Rican ◽  
Shared Decision Making ◽  
Decision Making Process ◽  
Shared Decision ◽  
Medical Conditions ◽  
Depression Severity ◽  
Related Information ◽  
Health Related

The shared decision-making (SDM) model is the optimal patient-centered approach to reduce racial and ethnic health disparities in primary care settings. This study examined decision-making preferences and the desire to be knowledgeable of health-related information of a multiheritage group of depressed older Latinx primary care patients. The primary aim was to determine differences in treatment preferences for both general medical conditions and depression and desire to be knowledgeable of health-related information between older Puerto Rican adults compared to older non-Puerto Rican Latinx adults. We also examined whether depression severity moderated those relationships. A sample of 178 older Latinx patients were assessed on measures of decision-making preferences, information-seeking desires, and depression severity. Regression models indicated depression severity moderated the relationship between Latinx heritage and decision-making preferences that relate to general medical decisions, but not depression treatment. Specifically, Puerto Ricans with high levels of depression preferred to be more active in making decisions related to general medical conditions compared to non-Puerto Rican patients who preferred less active involvement. There was no difference between groups at low levels of depression as both groups preferred to be similarly active in the decision-making process. This investigation adds to the literature by indicating between-group differences within a Latinx older adult sample regarding decision-making preferences and the desire to be informed of health-related information. Future research is needed to identify other sociocultural characteristics that contribute to this disparity between Latinx heritage groups in their desires to participate in the decision-making process with their primary care provider.

Fuzzy-Rule Based Adaptive Data Warehouse

2012 ◽  
Vol 3 (1) ◽  
pp. 47-65 ◽  
Author(s):  
Rajdev Tiwari ◽  
Anubhav Tiwari ◽  
Manu Pratap Singh
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Knowledge Workers ◽  
Fuzzy Rule ◽  
Data Warehouses ◽  
Large Reservoir ◽  
Inference System ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
New Generation

Data Warehouses (DWs) are aimed to empower the knowledge workers with information and knowledge which helps them in decision making. Technically, the DW is a large reservoir of integrated data that does not provide the intelligence or the knowledge demanded by users. The burden of data analysis and extraction of information and knowledge from integrated data still lies upon the analyst’s shoulder. The overhead of analysts can be taken off by architecting a new generation data warehouses systems those shall be capable of capturing, organizing and representing knowledge along with the data and information in it. This new generation DW may be called as Knowledge Warehouse (KW) shall exhibit decision making capabilities themselves and can also supplement the Decision Support Systems (DSS) in making decisions quickly and effortlessly. This paper proposes and simulates a fuzzy-rule based adaptive knowledge warehouse with capabilities to learn and represent implicit knowledge by means of adaptive neuro fuzzy inference system (ANFIS).

Including Client Opinion and Employee Engagement in the Strategic Human Resource Management

2015 ◽  
Vol 6 (3) ◽  
pp. 20-33 ◽  
Author(s):  
Rachid Belhaj ◽  
Mohamed Tkiouat
Keyword(s):  
Decision Making ◽  
Human Resources ◽  
Employee Engagement ◽  
Swot Analysis ◽  
Fuzzy Inference ◽  
Human Resources Management ◽  
Strategic Human Resource Management ◽  
Inference System ◽  
Resources Management ◽  
Strategic Human Resources

The trend in the Human Resources Management is to enhance it to the strategic level, this paper intends to provide an advanced decision making tool in the field of the Strategic Human Resources Management applying a combination of an augmented SWOT analysis approach to evaluate and to rank strategies and a Fuzzy Inference System (FIS) to eliminate the vagueness in the expressed opinions. The version of SWOT utilized here includes the client opinion and employee engagement besides to the traditional experts' assessment, while the FIS is an instrument that treats the data coming from SWOT analysis with the goal of avoiding the uncertainty that may come from the opinions expressed in the SWOT analysis and so to improve the ranking of the strategies. The results show improvements in prioritization of strategies in the way that besides knowing the decision to make for each, the model shows us also what to do regarding every strategy (what to maximize and what to minimize while talking about strengths, weaknesses, opportunities and threat).The proposed model can be applied for strategies prioritization and decision making problems in all fields.

scholarly journals M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 707 ◽  
Author(s):  
Tran Manh Tuan ◽  
Luong Thi Hong Lan ◽  
Shuo-Yan Chou ◽  
Tran Thi Ngan ◽  
Le Hoang Son ◽  
...  
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Fuzzy Inference System ◽  
Granular Computing ◽  
Fuzzy Inference ◽  
Fuzzy Theory ◽  
Rule Base ◽  
Inference System ◽  
Fuzzy Measures ◽  
Fuzzy Similarity

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

scholarly journals A New Complex Fuzzy Inference System With Fuzzy Knowledge Graph and Extensions in Decision Making

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 164899-164921
Author(s):  
Luong Thi Hong Lan ◽  
Tran Manh Tuan ◽  
Tran Thi Ngan ◽  
Le Hoang Son ◽  
Nguyen Long Giang ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Knowledge Graph ◽  
Inference System ◽  
Fuzzy Knowledge

scholarly journals Integrating Sustainability in the Quality Assessment of EHEA Institutions: A Hybrid FDEMATEL-ANP-FIS Model

2020 ◽  
Vol 12 (5) ◽  
pp. 1707 ◽  
Author(s):  
Javier Puente ◽  
Isabel Fernandez ◽  
Alberto Gomez ◽  
Paolo Priore
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Quality Assessment ◽  
Conceptual Model ◽  
Fuzzy Inference ◽  
Multicriteria Decision Making ◽  
Analytic Network Process ◽  
Fuzzy Decision Making ◽  
Inference System ◽  
European Higher Education

This paper proposes the design of a conceptual model of quality assessment in European higher education institutions (HEIs) that takes into account some of the critical reflections made by certain authors in the literature regarding standards and guidelines suggested for this purpose by the European Higher Education Area (EHEA). In addition, the evaluation of the conceptual model was carried out by means of the reliable hybrid methodology MCDM-FIS (multicriteria decision making approach–fuzzy inference system) using FDEMATEL and FDANP methods (fuzzy decision-making trial and evaluation laboratory and FDEMATEL-based analytic network process). The choice of these methodologies was justified by the existing interrelationships among the criteria and dimensions of the model and the degree of subjectivity inherent in its evaluation processes. Finally, it is suggested to include sustainability as a determining factor in the university context due to its great relevance in the training of future professionals.

Driver's Speed Decision-Making Model Based on ANFIS

2014 ◽  
Vol 488-489 ◽  
pp. 955-960 ◽  
Author(s):  
Lian Li ◽  
Song Yang ◽  
Wen Jing Cao
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Model Simulation ◽  
Original Data ◽  
Vehicle Speed ◽  
Anfis Model ◽  
Inference System ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
The Difference

To simulate the driver's ability to deal with uncertainty and solve the unsmooth problem in the driving-status-transformation between free-traveling and car-following during the microscopic traffic simulation, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was introduced to model the driver's speed decision-making behavior which integrated the free-traveling and car-following behavior. The difference between velocity and desired speed was added into the inputs of the ANFIS model besides vehicle speed, relative distance and relative velocity which commonly appeared in car-following models. In this paper, the NGSIM (Next Generation Simulation) data was used to calibrate and evaluate the model. With the analysis and pretreatment of NGSIM data, drivers reaction time was calibrated, drivers were clustered into three categories according to the level of recklessness, and the desired speed of different driver characteristic in different vehicle was approximated as the corresponding free speed. Using the processed NGSIM data, the ANFIS model was trained and the model output was validated and compared with the original data. The results showed that the ANFIS model performed well. In addition, the output of ANFIS model under car-following state was compared with that of GM model. This comparison provided a better chance to analyze the performance of the model and showed that the model simulation the driving data in a more realistic way.

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