scholarly journals Integrating multiple fuzzy expert systems under restricting requirements 

2012 ◽  
Vol 52 (No. 4) ◽  
pp. 187-196
Author(s):  
S. Aly ◽  
I. Vrana
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Business Processes ◽  
New Product ◽  
Decision Problems ◽  
Food Chains ◽  
Problem Context ◽  
Multiple Factors ◽  
Decision Consistency ◽  
Fuzzy Expert Systems

The multiple, different and specific expertises are often needed in making YES-or-NO (YES/NO) decisions for treating a variety of business, economic, and agricultural decision problems. This is due to the nature of such problems in which decisions are influenced by multiple factors, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertise due to its capability to model real world values which are not always exact, but frequently vague, or uncertain. In addition, they are able to incorporate qualitative factors. The problem of integrating multiple fuzzy expert systems involves several independent and autonomous fuzzy expert systems arranged synergistically to suit a varying problem context. Every expert system participates in judging the problem based on a predefined match between problem context and the required specific expertises. In this research, multiple FESs are integrated through combining their crisp numerical outputs, which reflect the degree of bias to the Yes/No subjective answers. The reasons for independency can be related to maintainability, decision responsibility, analyzability, knowledge cohesion and modularity, context flexibility, sensitivity of aggregate knowledge, decision consistency, etc. This article presents simple algorithms to integrate multiple parallel FES under specific requirements: preserving the extreme crisp output values, providing for null or non-participating expertises, and considering decision-related expert systems, which are true requirements of a currently held project. The presented results provides a theoretical framework, which can bring advantage to decision making is many disciplines, as e.g. new product launching decision, food quality tracking, monitoring of suspicious deviation of the business processes from the standard performance, tax and customs declaration issues, control and logistic of food chains/networks, etc. 

scholarly journals Combining the crisp outputs of multiple fuzzy expert systems using the MPDI along with the AHP

2011 ◽  
Vol 57 (No. 5) ◽  
pp. 217-225
Author(s):  
S. Aly ◽  
I. Vrana
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Real World ◽  
Analytical Hierarchy Process ◽  
Problem Context ◽  
Binary Decision ◽  
The Real ◽  
Fuzzy Expert Systems ◽  
Decision Making Problem ◽  
Hierarchy Process

Business, economic, and agricultural YES-or-NO decision making problems often require multiple, different and specific expertises. This is due to the nature of such problems in which decisions may be influenced by multiple different, relevant aspects, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertises due to their capability to model the real world values, which are not always exact, but frequently vague, or uncertain. In this paper, different expertises relevant to the decision solution are modelled using several corresponding FESs. These systems are then integrated to comprehensibly judge the YES-or-NO binary decision making problem, which requires all such expertises. This integration involves several independent and autonomous FESs arranged synergistically to suit a varying problem context. Then, the main focus of this paper is to realize such integration through combining the crisp numerical outputs produced by multiple FESs. The newly developed methods MPDI and WMPDI are utilized to combine the crisp outputs of multiple parallel FESs, whilst weights are determined through the analytical hierarchy process (AHP). The presented approach of utilizing the proved efficient MPDI combining criteria along with AHP will encourage practitioners to take advantage of integration and cooperation among multiple numerically outputting knowledge sources in general.

scholarly journals Multiple parallel fuzzy expert systems utilizing a hierarchical fuzz model

2008 ◽  
Vol 53 (No. 2) ◽  
pp. 89-93
Author(s):  
S. Aly ◽  
I. Vrana
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Real World ◽  
Fuzzy System ◽  
Decision Problem ◽  
Binary Decision ◽  
Decision Consistency ◽  
Fuzzy Expert Systems ◽  
Hierarchical Fuzzy System

Business, economic, and agricultural YES-or-NO decision making problems often require multiple, different and specific expertises. This is due to the nature of such problems in which decisions may be influenced by multiple different, relevant aspects, and accordingly multiple corresponding expertises are required. Fuzzy expert systems (FESs) are widely used to model expertises due to its capability to model real world values, which are not always exact, but frequently vague or uncertain. In this research, different expertises, relevant to the decision solution, are modeled using several corresponding FESs. Every FES produces a crisp numerical output expressing the degree of bias toward “Yes” or “No“ decision. A unified scale is standardized for numerical outputs of all FESs. This scale ranges from 0 to 10, where the value 0 represents a complete bias ”No“ decision and the value 10 represents a complete bias to ”Yes“ decision. Intermediate values reflect the degree of bias either to ”Yes“ or ”No“ decision. These systems are then integrated to comprehensibly judge the binary decision problem, which requires all such expertises. Practically, the main reasons for independency among the multiple FESs can be related to maintainability, decision responsibility, analyzability, knowledge cohesion and modularity, context flexibility, sensitivity of aggregate knowledge, decision consistency, etc. The proposed mechanism for realizing integration is a hierarchical fuzzy system (HFS) based model, which allows the utilization of the existing If-then knowledge about how to combine/aggregate the outputs of FESs.

Fuzzy Expert System in Agriculture Domain

2020 ◽  
pp. 1-15
Author(s):  
M. Kalpana ◽  
A. V. Senthil Kumar
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert Systems ◽  
Weed Management ◽  
Irrigation Management ◽  
Uncertain Data ◽  
Fuzzy Expert System ◽  
Land Preparation ◽  
Fuzzy Expert Systems ◽  
Insufficient Knowledge

Agriculture is an important source of livelihood and economy of a country. Decision making plays an important role in various fields. Farmers are the backbone of agriculture. They need expert systems to make decisions during land preparation, sowing, fertilizer management, irrigation management, etc. for farming. Expert systems may suggest precisely suitable solutions to farmers for all the activities. Uncertainty deals with various situations during sowing, weed management, diagnosis of disease, insect, storage, marketing of product, etc. Uncertainty is compounded by many facts that many decision-making activities in agriculture are often vague or based on perception. Imprecision, vagueness, and insufficient knowledge are handled using the concept of fuzzy logic. Fuzzy logic with expert systems helps find uncertain data. Fuzzy expert systems are oriented with numerical processing.

An Application of Fuzzy Expert Systems to Strategic Investments: The Case of Florim S.P.A.

2001 ◽  
Author(s):  
Gisella Facchinetti ◽  
Carlo Alberto Magni ◽  
Giovanni Mastroleo ◽  
Marina Vignola
Keyword(s):  
Expert Systems ◽  
Strategic Investments ◽  
Fuzzy Expert Systems

scholarly journals Factors associated with the decision to prescribe and administer antipsychotics for older people with delirium: a qualitative descriptive study

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e047247
Author(s):  
Emily J Tomlinson ◽  
Helen Rawson ◽  
Elizabeth Manias ◽  
Nicole (Nikki) M Phillips ◽  
Peteris Darzins ◽  
...  
Keyword(s):  
Decision Making ◽  
Older People ◽  
Health Professionals ◽  
Acute Care Hospital ◽  
Nursing Workload ◽  
Care Hospital ◽  
Antipsychotic Medications ◽  
Factors Associated ◽  
Multiple Factors ◽  
Qualitative Descriptive

ObjectivesTo explore factors associated with decision-making of nurses and doctors in prescribing and administering as required antipsychotic medications to older people with delirium.DesignQualitative descriptive.SettingTwo acute care hospital organisations in Melbourne, Australia.ParticipantsNurses and doctors were invited to participate. Semi-structured focus groups and individual interviews were conducted between May 2019 and March 2020. Interviews were audio-recorded and transcribed verbatim. Data were analysed using thematic analysis.ResultsParticipants were 42 health professionals; n=25 nurses and n=17 doctors. Themes relating to decisions to use antipsychotic medication were: safety; a last resort; nursing workload; a dilemma to medicate; and anticipating worsening behaviours. Nurses and doctors described experiencing pressures when trying to manage hyperactive behaviours. Safety was a major concern leading to the decision to use antipsychotics. Antipsychotics were often used as chemical restraints to ‘sedate’ a patient with delirium because nurses ‘can’t do their job’. Results also indicated that nurses had influence over doctors’ decisions despite nurses being unaware of this influence. Health professionals’ descriptions are illustrated in a decision-making flowchart that identifies how nurses and doctors navigated decisions regarding prescription and administration of antipsychotic medications.ConclusionsThe decision to prescribe and administer antipsychotic medications for people with delirium is complex as nurses and doctors must navigate multiple factors before making the decision. Collaborative support and multidisciplinary teamwork are required by both nurses and doctors to optimally care for people with delirium. Decision-making support for nurses and doctors may also help to navigate the multiple factors that influence the decision to prescribe antipsychotics.

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