Performance evaluation of fuzzy clustered case-based reasoning

Khan, Malik Jahan; Khan, Cynthia M.

doi:10.1080/0952813x.2020.1744194

Cited by 6 publications

(3 citation statements)

References 42 publications

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“…The fuzzy c-means (FCM) algorithm allows the inclusion of different degrees of similarity between structures and thereby improves the effectiveness of case-based reasoning. Khan and Khan described the method of fuzzy clustering of a database in order to reduce a set of potentially similar cases [20]. However, the method does not use the information about the fuzzy cluster membership in the reasoning phase.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Case-Based Assembly Management Method for the Shipbuilding Industry

Iwańkowicz

2021

Polish Maritime Research

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This article describes a method for planning the assembly of ship hulls that focuses on a welding sequence, takes into account subassembly processes and makes use of a previously built database of structures. Different degrees of similarity between structures are taken into account. The described research led to the development of an intelligent hybrid sequencing method for structure assembly that uses fuzzy clustering, case-based reasoning and evolutionary optimization. The method is called ‘Multi-case-Based Assembly Planning (MBAP)’. The method is developed to provide satisfactory solutions with low user effort. The analyses carried out show that the calculations are highly time-efficient. The developed evolutionary algorithm converges on sub-optimal solutions. The MBAP method can be directly implemented by any shipbuilder that assembles hulls. Apart from this, fuzzy clustering integrated with case-based reasoning can be applied in practice. The integration of fuzzy clustering and case-based reasoning has been taken to a level higher than previously described in the literature.

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Section: Literature Reviewmentioning

confidence: 99%

A Multi-Case-Based Assembly Management Method for the Shipbuilding Industry

Iwańkowicz

2021

Polish Maritime Research

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“…CBR involves searching historical cases for similarities based on the target case information, wherein these resemblant cases offer solutions to the problem posed by the target case ( Slam et al, 2020 ; Khan and Khan, 2021 ). At present, more and more studies on the decision-making mechanism of training prescription are based on the case data collected from hospitals, and the decision-making is made through the way of CBR.…”

Section: Introductionmentioning

confidence: 99%

A decision support system for upper limb rehabilitation robot based on hybrid reasoning with RBR and CBR

Li,

Xiang,

et al. 2024

Front. Bioeng. Biotechnol.

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The rehabilitation robot can assist hemiplegic patients to complete the training program effectively, but it only focuses on helping the patient’s training process and requires the rehabilitation therapists to manually adjust the training parameters according to the patient’s condition. Therefore, there is an urgent need for intelligent training prescription research of rehabilitation robots to promote the clinical applications. This study proposed a decision support system for the training of upper limb rehabilitation robot based on hybrid reasoning with rule-based reasoning (RBR) and case-based reasoning (CBR). The expert knowledge base of this system is established base on 10 professional rehabilitation therapists from three different rehabilitation departments in Shanghai who are enriched with experiences in using desktop-based upper limb rehabilitation robot. The rule-based reasoning is chosen to construct the cycle plan inference model, which develops a 21-day training plan for the patients. The case base consists of historical case data from 54 stroke patients who underwent rehabilitation training with a desktop-based upper limb rehabilitation robot. The case-based reasoning, combined with a Random Forest optimized algorithm, was constructed to adjust the training parameters for the patients in real-time. The system recommended a rehabilitation training program with an average accuracy of 91.5%, an average AUC value of 0.924, an average recall rate of 88.7%, and an average F1 score of 90.1%. The application of this system in rehabilitation robot would be useful for therapists.

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“…To address this issue, scholars have applied clustering to the CBR case library [33][34][35], dividing the case library into several different clusters and limiting the CBR process to specific clusters, which reduces the comparison times and lowers the computational cost. However, this method may lead to the loss of cluster boundary information and result in a lower accuracy of model prediction [36]. In addition, concerning the determination of the coal spontaneous combustion hazard grades, scholars have proposed various methods for determining the coal spontaneous combustion hazard grades [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Pei,

Jia,

Liu

et al. 2024

Fire

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Accurate prediction of the coal spontaneous combustion hazard grades is of great significance to ensure the safe production of coal mines. However, traditional coal temperature prediction models have low accuracy and do not predict the coal spontaneous combustion hazard grades. In order to accurately predict coal spontaneous combustion hazard grades, a prediction model of coal spontaneous combustion based on principal component analysis (PCA), case-based reasoning (CBR), fuzzy clustering (FM), and the snake optimization (SO) algorithm was proposed in this manuscript. Firstly, based on the change rule of the concentration of signature gases in the process of coal warming, a new method of classifying the risk of spontaneous combustion of coal was established. Secondly, MeanRadius-SMOTE was adopted to balance the data structure. The weights of the prediction indicators were calculated through PCA to enhance the prediction precision of the CBR model. Then, by employing FM in the case base, the computational cost of CBR was reduced and its computational efficiency was improved. The SO algorithm was used to determine the hyperparameters in the PCA-FM-CBR model. In addition, multiple comparative experiments were conducted to verify the superiority of the model proposed in this manuscript. The results indicated that SO-PCA-FM-CBR possesses good prediction performance and also improves computational efficiency. Finally, the authors of this manuscript adopted the Random Balance Designs—Fourier Amplitude Sensitivity Test (RBD-FAST) to explain the output of the model and analyzed the global importance of input variables. The results demonstrated that CO is the most important variable affecting the coal spontaneous combustion hazard grades.

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Performance evaluation of fuzzy clustered case-based reasoning

Cited by 6 publications

References 42 publications

A Multi-Case-Based Assembly Management Method for the Shipbuilding Industry

A Multi-Case-Based Assembly Management Method for the Shipbuilding Industry

A decision support system for upper limb rehabilitation robot based on hybrid reasoning with RBR and CBR

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

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