Case-based classification of ultrasonic B-scans: Case-base organisation and case retrieval

1998 ◽  
pp. 100-111 ◽  
Author(s):  
Jacek Jarmulak
Keyword(s):  
Case Retrieval ◽  
Case Base ◽  
Case Based

scholarly journals An Associated Representation Method for Defining Agricultural Cases in a Case-Based Reasoning System for Fast Case Retrieval

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5118 ◽  
Author(s):  
Zhai ◽  
Martínez Ortega ◽  
Beltran ◽  
Lucas Martínez
Keyword(s):  
Intelligent Systems ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Traditional Methods ◽  
Case Representation ◽  
Reasoning System ◽  
Case Base ◽  
Representation Method ◽  
Associated Representation ◽  
Case Based

As an artificial intelligence technique, case-based reasoning has considerable potential to build intelligent systems for smart agriculture, providing farmers with advice about farming operation management. A proper case representation method plays a crucial role in case-based reasoning systems. Some methods like textual, attribute-value pair, and ontological representations have been well explored by researchers. However, these methods may lead to inefficient case retrieval when a large volume of data is stored in the case base. Thus, an associated representation method is proposed in this paper for fast case retrieval. Each case is interconnected with several similar and dissimilar ones. Once a new case is reported, its features are compared with historical data by similarity measurements for identifying a relative similar past case. The similarity of associated cases is measured preferentially, instead of comparing all the cases in the case base. Experiments on case retrieval were performed between the associated case representation and traditional methods, following two criteria: the number of visited cases and retrieval accuracy. The result demonstrates that our proposal enables fast case retrieval with promising accuracy by visiting fewer past cases. In conclusion, the associated case representation method outperforms traditional methods in the aspect of retrieval efficiency.

scholarly journals An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

Agriculture ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 387
Author(s):  
Zhaoyu Zhai ◽  
José-Fernán Martínez Ortega ◽  
Néstor Lucas Martínez ◽  
Huanliang Xu
Keyword(s):  
Poor Performance ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Reasoning Systems ◽  
Case Base ◽  
Low Efficiency ◽  
Association Table ◽  
Smart Agriculture ◽  
Case Based

Case-based reasoning has considerable potential to model decision support systems for smart agriculture, assisting farmers in managing farming operations. However, with the explosive amount of sensing data, these systems may achieve poor performance in knowledge management like case retrieval and case base maintenance. Typical approaches of case retrieval have to traverse all past cases for matching similar ones, leading to low efficiency. Thus, a new case retrieval algorithm for agricultural case-based reasoning systems is proposed in this paper. At the initial stage, an association table is constructed, containing the relationships between all past cases. Afterwards, attributes of a new case are compared with an entry case. According to the similarity measurement, associated similar or dissimilar cases are then compared preferentially, instead of traversing the whole case base. The association of the new case is generated through case retrieval and added in the association table at the step of case retention. The association table is also updated when a closer relationship is detected. The experiment result demonstrates that our proposal enables rapid case retrieval with promising accuracy by comparing a fewer number of past cases. Thus, the retrieval efficiency of our proposal outperforms typical approaches.

scholarly journals Development of Ground Special Vehicle PHM with Case-Based Reason Model

2021 ◽  
Vol 11 (10) ◽  
pp. 4494
Author(s):  
Qicai Wu ◽  
Haiwen Yuan ◽  
Haibin Yuan
Keyword(s):  
Cross Validation ◽  
Optimization Methods ◽  
Engineering Practice ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Data Set ◽  
Attribute Weights ◽  
Special Vehicle ◽  
Weight Allocation ◽  
Case Based

The case-based reasoning (CBR) method can effectively predict the future health condition of the system based on past and present operating data records, so it can be applied to the prognostic and health management (PHM) framework, which is a type of data-driven problem-solving. The establishment of a CBR model for practical application of the Ground Special Vehicle (GSV) PHM framework is in great demand. Since many CBR algorithms are too complicated in weight optimization methods, and are difficult to establish effective knowledge and reasoning models for engineering practice, an application development using a CBR model that includes case representation, case retrieval, case reuse, and simulated annealing algorithm is introduced in this paper. The purpose is to solve the problem of normal/abnormal determination and the degree of health performance prediction. Based on the proposed CBR model, optimization methods for attribute weights are described. State classification accuracy rate and root mean square error are adopted to setup objective functions. According to the reasoning steps, attribute weights are trained and put into case retrieval; after that, different rules of case reuse are established for these two kinds of problems. To validate the model performance of the application, a cross-validation test is carried on a historical data set. Comparative analysis of even weight allocation CBR (EW-CBR) method, correlation coefficient weight allocation CBR (CW-CBR) method, and SA weight allocation CBR (SA-CBR) method is carried out. Cross-validation results show that the proposed method can reach better results compared with the EW-CBR model and CW-CBR model. The developed PHM framework is applied to practical usage for over three years, and the proposed CBR model is an effective approach toward the best PHM framework solutions in practical applications.

scholarly journals Errors, discrepancies and underlying bias in radiology with case examples: a pictorial review

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Omer Onder ◽  
Yasin Yarasir ◽  
Aynur Azizova ◽  
Gamze Durhan ◽  
Mehmet Ruhi Onur ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Diagnostic Errors ◽  
Radiology Practice ◽  
Case Examples ◽  
Pictorial Review ◽  
Underlying Causes ◽  
Case Based

AbstractInterpretation differences between radiologists and diagnostic errors are significant issues in daily radiology practice. An awareness of errors and their underlying causes can potentially increase the diagnostic performance and reduce individual harm. The aim of this paper is to review both the classification of errors and the underlying biases. Case-based examples are presented and discussed for each type of error and bias to provide greater clarity and understanding.

scholarly journals Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiwang Zhong ◽  
Tianhua Xu ◽  
Feng Wang ◽  
Tao Tang
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Rough Set ◽  
Best Practice ◽  
Discrete Event ◽  
Scale Up ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Fault Features ◽  
Case Based

In Discrete Event System, such as railway onboard system, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis process. Efficient text mining of such maintenance data plays an important role in discovering the best-practice repair knowledge from millions of repair verbatims, which help to conduct accurate fault diagnosis and predication. This paper presents a text case-based reasoning framework by cloud computing, which uses the diagnosis ontology for annotating fault features recorded in the repair verbatim. The extracted fault features are further reduced by rough set theory. Finally, the case retrieval is employed to search the best-practice repair actions for fixing faulty parts. By cloud computing, rough set-based attribute reduction and case retrieval are able to scale up the Big Data records and improve the efficiency of fault diagnosis and predication. The effectiveness of the proposed method is validated through a fault diagnosis of train onboard equipment.

Retrieving Assembly Part Design Using Case-Based Reasoning and Genetic Algorithms

2005 ◽  
Author(s):  
Guanghsu A. Chang ◽  
Cheng-Chung Su ◽  
John W. Priest
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithms ◽  
Nearest Neighbor ◽  
Similarity Measures ◽  
Experimental Results ◽  
Case Based Reasoning ◽  
Feature Weight ◽  
Similar Part ◽  
Case Base ◽  
Case Based

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. To develop the weight of part features and retrieve a similar part design, the research proposes using Genetic Algorithms (GAs) to learn the optimum feature weight and employing nearest-neighbor technique to measure the similarity of assembly part design. Early experimental results indicate that the similar part design is effectively retrieved by these similarity measures.

Survey on structure-based case retrieval

1997 ◽  
Vol 12 (01) ◽  
pp. 41-58 ◽  
Author(s):  
FRIEDRICH GEBHARDT
Keyword(s):  
Structural Properties ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Model Based ◽  
Main Components ◽  
Case Based

The main components of case-based reasoning are case retrieval and case reuse. While case retrieval mostly uses attribute comparisons, many other possibilities exist. The case similarity concepts described in the literature that are based on more elaborate structural properties are classified here into five groups: restricted geometric relationships; graphs; semantic nets; model-based similarities; hierarchically structured similarities. Some general topics conclude this survey on structure-based case retrieval methods and systems.

scholarly journals Maintenance of Case Bases: Current Algorithms after Fifty Years

2018 ◽  
Author(s):  
Jose M. Juarez ◽  
Susan Craw ◽  
J. Ricardo Lopez-Delgado ◽  
Manuel Campos
Keyword(s):  
Big Data ◽  
50Th Anniversary ◽  
Case Based Reasoning ◽  
Changing Environments ◽  
Model Based ◽  
New Knowledge ◽  
Case Base ◽  
Case Based

Case-Based Reasoning (CBR) learns new knowledge from data and so can cope with changing environments. CBR is very different from model-based systems since it can learn incrementally as new data is available, storing new cases in its case-base. This means that it can benefit from readily available new data, but also case-base maintenance (CBM) is essential to manage the cases, deleting and compacting the case-base. In the 50th anniversary of CNN (considered the first CBM algorithm), new CBM methods are proposed to deal with the new requirements of Big Data scenarios. In this paper, we present an accessible historic perspective of CBM and we classify and analyse the most recent approaches to deal with these requirements.

scholarly journals Towards Transparent Human-in-the-Loop Classification of Fraudulent Web Shops

2020 ◽  
Author(s):  
Daphne Odekerken ◽  
Floris Bex
Keyword(s):  
Case Based Reasoning ◽  
Agent Architecture ◽  
Human In The Loop ◽  
Levels Of Detail ◽  
Legal Case ◽  
Dynamic Argumentation ◽  
Case Based

We propose an agent architecture for transparent human-in-the-loop classification. By combining dynamic argumentation with legal case-based reasoning, we create an agent that is able to explain its decisions at various levels of detail and adapts to new situations. It keeps the human analyst in the loop by presenting suggestions for corrections that may change the factors on which the current decision is based and by enabling the analyst to add new factors. We are currently implementing the agent for classification of fraudulent web shops at the Dutch Police.

scholarly journals Wear Debris Classification of Steel Production Equipment using Feature Fusion and Case-based Reasoning

2018 ◽  
Vol 58 (7) ◽  
pp. 1293-1299 ◽  
Author(s):  
Hongbing Wang ◽  
Rong Huang ◽  
Liyuan Gao ◽  
Weishen Wang ◽  
Anjun Xu ◽  
...  
Keyword(s):  
Wear Debris ◽  
Feature Fusion ◽  
Steel Production ◽  
Production Equipment ◽  
Case Based Reasoning ◽  
Case Based
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