A Predictive Fuzzy Expert System for Crop Disease Diagnostic and Decision Support

2020 ◽  
pp. 175-194 ◽  
Author(s):  
Prateek Pandey ◽  
Ratnesh Litoriya
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fuzzy Inference ◽  
Disease Treatment ◽  
Soybean Crop ◽  
Oilseed Production ◽  
Crop Disease ◽  
Extent Of Disease

Soybean accounts for 38% of the total oilseed production in India, and around 50% of the total oilseed production in Kharif season. This crop has shown tremendous growth over the last four decades with an average national yield of 1264 kg/hectare. Currently, soybean is severely attacked by more than 10 major diseases. Yield losses due to different diseases ranges from 20 to 100%. Timely detection of soybean crop disease would help farmers save their money, effort, and crop from being destroyed. This chapter presents a case study on the development of a decision support system for prediction of soybean crop disease severity. The outcome of this system will aid farmers to decide the extent of disease treatment to be employed. Such predictions make use of human involvement, and thus are a source of ambiguities. To deal with such ambiguities in decision making, this decision support system uses fuzzy inference method based on triangular fuzzy sets.

Decision Support System for Radiotherapy Patient Scheduling: Thai Cancer Center Case Study

2021 ◽  
Author(s):  
Chawis Boonmee ◽  
Nirand Pisutha-Arnond ◽  
Wichai Chattinnawat ◽  
Pooriwat Muangwong ◽  
Wannapha Nobnop ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Cancer Center ◽  
Patient Scheduling

scholarly journals Design of Condition-Based Decision Support System for Preventive Maintenance

2017 ◽  
Vol 20 (1) ◽  
pp. 19-22
Author(s):  
Róbert Galamboš ◽  
Jana Galambošová ◽  
Vladimír Rataj ◽  
Miroslav Kavka
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Preventive Maintenance ◽  
Maintenance Costs

Abstract Presented paper deals with the topic of preventive maintenance. A decision support system was designed, incorporating historical as well as forecast information to calculate the time remaining to preventive maintenance. The designed system optimizes maintenance costs without any further investment and running costs. An algorithm of the designed system is introduced and a case study of its implementation is described in the paper.

scholarly journals Validation of a Model Appropriateness Framework Using the Elbe Decision Support System

2011 ◽  
pp. 193-218
Author(s):  
Yue-Ping Xu ◽  
Martijn J. Booij
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
River Basin ◽  
Support System ◽  
River Basin Management ◽  
Development Stage ◽  
Decision Makers ◽  
Basin Management ◽  
Use Of Models

This paper describes validation of an appropriateness framework, which has been developed in a former study, to determine appropriate models under uncertainty in a decision support system for river basin management. Models are regarded as ‘appropriate’ if they produce final outputs within adequate uncertainty bands that enable decision-makers to distinguish or rank different river engineering measures. The appropriateness framework has been designed as a tool to stimulate the use of models in decision-making under uncertainty and to strengthen the communication between modelers and decision-makers. Through the application to a different river with different objectives in this validation study from the river used in the development stage, this paper investigates whether the appropriateness framework works in a different situation than it was designed for. Recommendations from the development stage are taken into account in this validation case study as well. The final results from the study showed a successful validation of the appropriateness framework and suggested further possibilities for the application in decision support systems for river basin management.

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