scholarly journals A hybrid reasoning system for supporting estuary modelling

2005 ◽  
Vol 7 (3) ◽  
pp. 185-198
Author(s):  
Sara Passone ◽  
Vahid Nassehi ◽  
Paul W. H. Chung
Keyword(s):  
Genetic Algorithm ◽  
Physical Phenomenon ◽  
Computing Methods ◽  
Case Based Reasoning ◽  
Illustrative Case ◽  
Detailed Knowledge ◽  
Reasoning System ◽  
Hybrid Reasoning ◽  
Illustrative Case Study ◽  
Case Based

In this paper the development of a Case-Based reasoning system for Estuarine Modelling (CBEM) is presented. The aim of the constructed CBEM system is to facilitate the utilisation of complex modelling software by users who lack detailed knowledge about modelling techniques and require training and assistance to implement sophisticated software effectively. The system is based on modern computing methods and is constructed as a hybrid of three modules which operate conjunctively to guide the user to obtain the best possible simulation for realistic problems. These modules are: a case-based reasoning scheme, a genetic algorithm and a library of numerical estuarine models. Based on the features of a given estuary and the physical phenomenon to be modelled, an appropriate solution algorithm from the system's library is retrieved by the case-based module after a specifically designed reasoning process. The selected model is then analysed and further treated by the genetic algorithm component to find the optimum parameters which can appropriately model the conditions and characteristics of any given estuary. Using these modules the steps that yield the best solution for a problem from the available hydrographic data under a set of specified conditions are explained. This is further elucidated by an illustrative case study which shows the applicability of the present CBEM system under realistic conditions. This case deals with the simulation of salinity distribution in the Tay estuary (Scotland, UK).

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.

A Case Based Reasoning System For Recommendation Of Restaurant In Jimbaran Using K-Nearest Neighbor

2020 ◽  
Vol 9 (2) ◽  
pp. 267
Author(s):  
I Gede Teguh Mahardika ◽  
I Wayan Supriana
Keyword(s):  
Recommendation System ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Retrieval Process ◽  
K Nearest Neighbor ◽  
Reasoning System ◽  
Human Ability ◽  
And Function ◽  
Case Based ◽  
Selection Of

Culinary is one of the favorite businesses today. The number of considerations to choose a restaurant or place to visit becomes one of the factors that is difficult to determine the restaurant or place to eat. To get the desired place to eat advice, one needs a recommendation system. Decisions made by the recommendation system can be used as a reference to determine the choice of restaurants. One method that can be used to build a recommendation system is Case Based Reasoning. The Case Based Reasoning (CBR) method mimics human ability to solve a problem or cases. The retrieval process is the most important stage, because at this stage the search for a solution for a new case is carried out. The study used the K-Nearest Neighbor method to find closeness between new cases and case bases. With the selection of features used as domains in the system, the results of recommendations presented can be more suggestive and accurate. The system successfully provides complex recommendations based on the type and type of food entered by the user. Based on blackbox testing, the system has features that can be used and function properly according to the purpose of creating the system.

A Hierarchical Case-Based Reasoning System for Quality Design

2010 ◽  
Vol 20-23 ◽  
pp. 1015-1020
Author(s):  
Cai Yan Liu ◽  
You Fa Sun
Keyword(s):  
High Efficiency ◽  
Low Cost ◽  
Case Based Reasoning ◽  
Retrieval Accuracy ◽  
Reasoning System ◽  
Quality Specifications ◽  
Computation Formula ◽  
Quality Design ◽  
Similarity Computation ◽  
Case Based

Quality design means designing quality specifications and processing specifications of products with low cost and high efficiency. This paper presents a hierarchical case-based reasoning approach for quality design. The structure and expression of case-base, the hierarchical case retrieval algorithms and similarity computation formula between cases are all studied. Such a hierarchical case-based reasoning method will greatly improve the retrieval accuracy and efficiency.

Identification of Raman spectra through a case-based reasoning system: application to artistic pigments

2011 ◽  
Vol 42 (7) ◽  
pp. 1553-1561 ◽  
Author(s):  
M. Castanys ◽  
R. Perez-Pueyo ◽  
M. J. Soneira ◽  
E. Golobardes ◽  
A. Fornells
Keyword(s):  
Raman Spectra ◽  
System Application ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
Case Based
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