scholarly journals A Case-Based Reasoning Framework to Choose Trust Models for Different E-Marketplace Environments

2015 ◽  
Vol 52 ◽  
pp. 477-505
Author(s):  
Athirai A. Irissappane ◽  
Jie Zhang
Keyword(s):  
Similar Case ◽  
Ground Truth ◽  
Trust Model ◽  
Case Based Reasoning ◽  
Suitable Model ◽  
Unknown Environment ◽  
Simulated Environments ◽  
Trust Models ◽  
Seller Behavior ◽  
Case Based

The performance of trust models highly depend on the characteristics of the environments where they are applied. Thus, it becomes challenging to choose a suitable trust model for a given e-marketplace environment, especially when ground truth about the agent (buyer and seller) behavior is unknown (called unknown environment). We propose a case-based reasoning framework to choose suitable trust models for unknown environments, based on the intuition that if a trust model performs well in one environment, it will do so in another similar environment. Firstly, we build a case base with a number of simulated environments (with known ground truth) along with the trust models most suitable for each of them. Given an unknown environment, case-based retrieval algorithms retrieve the most similar case(s), and the trust model of the most similar case(s) is chosen as the most suitable model for the unknown environment. Evaluation results confirm the effectiveness of our framework in choosing suitable trust models for different e-marketplace environments.

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 Similarity Measure Based on Extension Distance and Its Application in CBR

2008 ◽  
Author(s):  
Yanwei Zhao ◽  
Feng Zhang ◽  
Nan Su ◽  
Huijun Tang ◽  
Jian Chen
Keyword(s):  
Standard Deviation ◽  
Similar Case ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Similarity Model ◽  
Electric Drill ◽  
Classical Distance ◽  
Case Based

Case-based reasoning (CBR) is an effective method that integrates reasoning methodology and represents related knowledge in a domain. The success of a CBR system largely depends on case retrieval, and the similarity and determination of weight for each case features have a significant influence on the efficiency and accuracy of case retrieval. The aim of the research is to improve the efficiency and accuracy of case retrieval. Analyzing the deficiency of similarity measures based on the classical distance, different similarity measures are proposed for different kinds of attribute values based on the extension distance, especially the similarity model between numerical and set considered the customer’s preference. The standard deviation related with the similarity is introduced to distribute the dynamic attribute’s weights which also considered the customer’s interest, but not the traditional methods that the weight is a constant if determined. The presented methods will enable the system to retrieve the more similar case correctly so that reducing case adaptation. In this study, an electric drill is used as a case to verify the usefulness and effectiveness of the similarity measurements and weight assignments. It is demonstrated that this method is more beneficial to case retrieval compared with other methods.

scholarly journals Case Based Reasoning Untuk Diagnosis Penyakit Demam Berdarah

2017 ◽  
Vol 1 (1) ◽  
pp. 63 ◽  
Author(s):  
Rabiah Adawiyah
Keyword(s):  
Dengue Fever ◽  
Nearest Neighbor ◽  
Similar Case ◽  
Dengue Shock Syndrome ◽  
Hemorrhagic Fever ◽  
Diagnostic Process ◽  
Case Based Reasoning ◽  
The Status ◽  
Case Basis ◽  
Case Based

  Dengue virus is a virus that causes dengue fever (DD), dengue hemorrhagic fever (DBD) and dengue shock syndrome (SSD). This disease is included in the status of outbreaks at the Hospital Benjamin Guluh Kab. Kolaka. The inclusion of this disease in the status of outbreaks then there are many cases / patients are handled every year even every month. The symptoms of a patient diagnosed with dengue fever tend to be similar. Case Based Reasoning (CBR) is one of the methods that can make or solve problems based on the existing case as a solution of new problem. The system built in this study is the CBR system to make a diagnosis of Dengue Fever. The diagnostic process of incorporating new problems compared to old cases then calculated the value of similarity. In this research, Nearest neighbor method is used for similarity process. Tests used 54 cases as randomized test data and 85 cases were used as case basis. The results obtained are 98.14% sensitivity and 99.25% accuracy.

Hybrid Intelligence Process Parameters Setting and Optimization on Differential Pressure Vacuum Casting

2012 ◽  
Vol 522 ◽  
pp. 156-161
Author(s):  
Tian Lv ◽  
Hai Guang Zhang ◽  
Yuan Yuan Liu ◽  
Qing Xi Hu
Keyword(s):  
Process Parameters ◽  
Similar Case ◽  
Casting Machine ◽  
Fuzzy Reasoning ◽  
Case Based Reasoning ◽  
Vacuum Casting ◽  
Intelligent Decision ◽  
Hybrid Intelligence ◽  
Quality Stability ◽  
Case Based

Considering that the problem of traditional process parameters setting in vacuum casting machine is of long period, high cost and inferior quality stability, a kind of hybrid intelligent decision model which combined with case-based reasoning, neural network and fuzzy reasoning were established. First, use the case-based reasoning technology to extract the similar case from the case database. Then, use the initial parameters to run the mould trial. Finally, use the fuzzy reasoning technology to optimize the initial parameters according to the product defects. Based on the above-mentioned intelligence model, the related hardware and software system was established. The actual practice proved that the system is effective and can be used in practical production.

scholarly journals PENGEMBANGAN SISTEM CERDAS MENGGUNAKAN PENALARAN BERBASIS KASUS (CASE BASED REASONING) UNTUK DIAGNOSA PENYAKIT AKIBAT VIRUS EKSANTEMA

Telematika ◽  
2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Agus Sasmito Aribowo
Keyword(s):  
Expert System ◽  
Similar Case ◽  
Expert Knowledge ◽  
Common Disease ◽  
Case Based Reasoning ◽  
Chicken Pox ◽  
Case Based

Disease caused by a exanthema virus is a common disease in Indonesia. There are many types of diseases caused by this virus. Examples are chicken pox, measles, variola, etc. with symptoms almost similar to each other. To correctly identify the symptoms  need experts. But the problem is very limited number of experts. Then the expert system is needed which has been given by the expert knowledge to assist in the diagnosis. Expert system in this research uses a case-based reasoning approach. If there is a similar case, the reasoning for considering the case of the nearest using Probabilistic Bayes. The result is the system will still be able to provide the best recommendations solution for new cases based on the solution to an old case that the nearest level of similarity.

Applying Case-Based Reasoning Approach to Assembly Part Design

2013 ◽  
Vol 392 ◽  
pp. 237-241
Author(s):  
Guang Hsu Chang
Keyword(s):  
Artificial Intelligence ◽  
Similar Case ◽  
Similarity Measures ◽  
Quantitative Information ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Qualitative And Quantitative ◽  
Similar Part ◽  
Design Experience ◽  
Case Based

Experience and knowledge play a very important role in design; however, experts experience and knowledge are difficult to impart effectively and precisely to novices. Artificial intelligence (AI) approaches have been successfully applied to many fields. Among the numerous AI approaches, Case-Based Reasoning (CBR) is an approach that mainly focuses on the reuse of knowledge and experience. However, little work is done on applications of CBR to improve assembly part design. Similarity measures and the weight of different features are crucial in determining the accuracy of retrieving cases from the case base. This research presents a Case-Based Reasoning (CBR) system, CBR-DFA, consisting of a complete CBR cycle to retrieve and evaluate an assembly part design. Experience and knowledge in the form of suggestions include qualitative and quantitative information offered to novices by retrieving and adapting a similar case. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

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