Application of Fuzzy Cognitive Maps with Evolutionary Learning Algorithm to Model Decision Support Systems Based on Real-Life and Historical Data

This paper determines the risk for cardiovascular diseases (CVDs), and nutrition level in infants aged 0–6 months using Fuzzy Cognitive Maps (FCMs). The aim of this study is to facilitates the medical experts to early detects these diseases with accuracy, so that overall death ratio can be reduced. Firstly, we have introduced the concepts of FCMs and briefly refer to the applications of these methods in medical. After that, two intelligent decision support systems for cardiovascular and malnutrition are developed using FCMs. The proposed cardiovascular risk assessment system takes six inputs: chest pain, cholesterol, heart rate, blood pressure, blood sugar, and old peak and determines CVDs risk. The second decision support system of malnutrition diagnosis takes twelve inputs: breastfeeding, daily income, maternal education, colostrum intake, energy intake, protein intake, vitamin A intake, iron intake, family size, height, weight, head circumference, and skin fold thickness and diagnoses the nutrition level in infants. We have explained the working of both decision support systems using case studies.

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Strategic decision support systems design: integration approach between expert knowledge and historical data

Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004. ◽

10.1109/ictta.2004.1307880 ◽

2004 ◽

Author(s):

A.R. Ghomari

Keyword(s):

Decision Support ◽

Decision Support Systems ◽

Historical Data ◽

Support Systems ◽

Expert Knowledge ◽

Systems Design ◽

Strategic Decision ◽

Integration Approach ◽

Design Integration ◽

Strategic Decision Support

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A Methodology for Developing Adaptive Fuzzy Cognitive Maps for Decision Support

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2000.p0403 ◽

2000 ◽

Vol 4 (6) ◽

pp. 403-407 ◽

Cited By ~ 8

Author(s):

M. Shamim Khan ◽

◽

Alex Chong ◽

Tom Gedeon

Keyword(s):

Decision Support ◽

Dynamic System ◽

Hebbian Learning ◽

Real Life ◽

Cognitive Maps ◽

Causal Link ◽

Fuzzy Cognitive Maps ◽

Event Sequence ◽

Event Sequences ◽

Sequence Encoding

Differential Hebbian Learning (DHL) was proposed by Kosko as an unsupervised learning scheme for Fuzzy Cognitive Maps (FCMs). DHL can be used with a sequence of state vectors to adapt the causal link strengths of an FCM. However, it does not guarantee learning of the sequence by the FCM and no concrete procedures for the use of DHL has been developed. In this paper a formal methodology is proposed for using DHL in the development of FCMs in a decision support context. The four steps in the methodology are: (1) Creation of a crisp cognitive map; (2) Identification of event sequences for use in DHL; (3) Event sequence encoding using DHL; (4) Revision of the trained FCM. Feasibility of the proposed methodology is demonstrated with an example involving a dynamic system with feedback based on a real-life scenario.

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Advances in thermal level measurement techniques using mathematical models, statistical models and decision support systems in blast furnace

Metallurgical Research & Technology ◽

10.1051/metal/2019019 ◽

2019 ◽

Vol 116 (4) ◽

pp. 421

Author(s):

Ashish Agrawal ◽

Anil Kumar Kothari ◽

Arun Kumar ◽

Manish Kumar Singh ◽

Shivendra Kumar Dubey ◽

...

Keyword(s):

Blast Furnace ◽

Decision Support ◽

Mathematical Models ◽

Decision Support Systems ◽

Statistical Models ◽

Historical Data ◽

Support Systems ◽

Measurement Techniques ◽

Level Measurement ◽

Thermal Level

The estimation of thermal level in blast furnace is of utmost importance, because the processes occurring inside the blast furnace are complex in nature and any drift in thermal level could lead to abnormal furnace state. The present review is made to understand the methods for estimating thermal level in blast furnace, and the drift in estimation of the thermal level. The thermal level estimation is divided into 3 categories, viz. mathematical models, statistical models and decision support systems. The mathematical models are based on the first principle of thermodynamics and give an estimate of the thermal level in blast furnace. On the other hand, the statistical models are mainly the data-based approach that uses the historical data to predict the instability in blast furnace. Lastly, the decision support systems are the prescriptive models that give the recommendations for making the necessary corrections in the process parameters to avoid occurrence of abnormality in blast furnace. Further, the drifts in estimation of thermal level by these techniques are identified and recommendations are made to improve the accuracy of thermal level estimation. The recommendations to control thermal level in blast furnace are provided which when applied in the industrial blast furnace, can avoid the occurrence of catastrophic condition.

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