A case based reasoning decision support model for Green ITIS diffusion in collaborative enterprise

2016 ◽  
Author(s):  
Bokolo Anthony ◽  
Mazlina Abdul Majid ◽  
Awanis Romli
Keyword(s):  
Decision Support ◽  
Case Based Reasoning ◽  
Decision Support Model ◽  
Case Based ◽  
Support Model

scholarly journals Development of a practical best management practice decision support model for engineers and planners in Nordic countries

2007 ◽  
Vol 38 (2) ◽  
pp. 107-123 ◽  
Author(s):  
Miklas Scholz
Keyword(s):  
Decision Support ◽  
Linear Model ◽  
Future Development ◽  
Management Practice ◽  
Best Management Practice ◽  
Case Based Reasoning ◽  
Decision Support Model ◽  
Best Management ◽  
Case Based ◽  
Support Model

Seventy-nine and 103 sites within Glasgow and Edinburgh, respectively, were identified to assess if best management practice (BMP) can be integrated into future development, regeneration and retrofitting plans. A practical BMP Decision Support Model based on a matrix and weighting system, incorporating the Prevalence Rating Approach for BMP Techniques (PRABT), has been developed. The findings indicate that ponds (or lined ponds) and permeable pavements are the most likely individual BMP techniques, and ponds combined with swales (or shallow swales) are the most recommended dual BMP combination. A separate case-based reasoning model compared to the ‘linear’ BMP Decision Support Model has also been developed. The output was similar to the ‘linear’ model.

scholarly journals Using extended siamese networks to provide decision support in aquaculture operations

2021 ◽  
Author(s):  
Bjørn Magnus Mathisen ◽  
Kerstin Bach ◽  
Agnar Aamodt
Keyword(s):  
Neural Networks ◽  
Decision Support ◽  
Similar Case ◽  
Decision Makers ◽  
Similarity Function ◽  
Case Based Reasoning ◽  
Similarity Learning ◽  
Reasoning System ◽  
Siamese Networks ◽  
Case Based

AbstractAquaculture as an industry is quickly expanding. As a result, new aquaculture sites are being established at more exposed locations previously deemed unfit because they are more difficult and resource demanding to safely operate than are traditional sites. To help the industry deal with these challenges, we have developed a decision support system to support decision makers in establishing better plans and make decisions that facilitate operating these sites in an optimal manner. We propose a case-based reasoning system called aquaculture case-based reasoning (AQCBR), which is able to predict the success of an aquaculture operation at a specific site, based on previously applied and recorded cases. In particular, AQCBR is trained to learn a similarity function between recorded operational situations/cases and use the most similar case to provide explanation-by-example information for its predictions. The novelty of AQCBR is that it uses extended Siamese neural networks to learn the similarity between cases. Our extensive experimental evaluation shows that extended Siamese neural networks outperform state-of-the-art methods for similarity learning in this task, demonstrating the effectiveness and the feasibility of our approach.

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