scholarly journals Collaborative Case-Based Reasoning for Knowledge Discovery of Elders Health Assessment System

2014 ◽  
Vol 8 (1) ◽  
pp. 68-74 ◽  
Author(s):  
Ping Hu ◽  
Dong-xiao Gu ◽  
Yu Zhu
Keyword(s):  
Single Case ◽  
Health Assessment ◽  
Assessment System ◽  
Gray Relational Analysis ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Knowledge Reuse ◽  
Case Retrieval ◽  
Case Library ◽  
Case Representation

The existing Elders Health Assessment (EHA) system based on single-case-library reasoning has low intelligence level, poor coordination, and limited capabilities of assessment decision support. To effectively support knowledge reuse of EHA system, this paper proposes collaborative case reasoning and applies it to the whole knowledge reuse process of EHA system. It proposes a multi-case library reasoning application framework of EHA knowledge reuse system, and studies key techniques such as case representation, case retrieval algorithm, case optimization and correction, and reuse etc.. In the aspect of case representation, XML-based multi-case representation for case organization and storage is applied to facilitate case retrieval and management. In the aspect of retrieval method, Knowledge-Guided Approach with Nearest-Neighbor is proposed. Given the complexity of EHA, Gray Relational Analysis with weighted Euclidean Distance is used to measure the similarity so as to improve case retrieval accuracy.

Classification Algorithm of Case Retrieval Based on Granularity Calculation of Quotient Space

2020 ◽  
pp. 2150003
Author(s):  
Jiaxing Lu ◽  
Jiang Qing ◽  
Huang He ◽  
Zhang Zhengyong ◽  
Wang Rujing
Keyword(s):  
Quotient Space ◽  
Inner Product ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Current Case ◽  
Quotient Spaces ◽  
Similarity Calculation ◽  
Key Steps ◽  
Case Based

Case retrieval is one of the key steps of case-based reasoning. The quality of case retrieval determines the effectiveness of the system. The common similarity calculation methods based on attributes include distance and inner product. Different similarity calculations have different influences on the effect of case retrieval. How to combine different similarity calculation results to get a more widely used and better retrieval algorithm is a hot issue in the current case-based reasoning research. In this paper, the granularity of quotient space is introduced into the similarity calculation based on attribute, and a case retrieval algorithm based on granularity synthesis theory is proposed. This method first uses similarity calculation of different attributes to get different results of case retrieval, and considers that these classification results constitute different quotient spaces, and then organizes these quotient spaces according to granularity synthesis theory to get the classification results of case retrieval. The experimental results verify the validity and correctness of this method and the application potential of granularity calculation of quotient space in case-based reasoning.

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 Case-based Reasoning of Damaged Ancient Buildings based on Ontology

2020 ◽  
Vol 165 ◽  
pp. 04007
Author(s):  
Ru Wang ◽  
Wei Chen
Keyword(s):  
Building Damage ◽  
Case Based Reasoning ◽  
Knowledge Reuse ◽  
Case Representation ◽  
Information Interaction ◽  
Knowledge Expression ◽  
Implementation Method ◽  
Definition Of ◽  
Case Reasoning ◽  
Case Based

At present, China has a large number of ancient buildings, and correspondingly, it also faces many problems. Staff of different professions have inconsistent perceptions of ancient buildings, resulting in inability to interact with information. In addition, the ancient architecture case did not carry out efficient reuse of knowledge. Therefore, this article applies ontology to the field of ancient architecture, and proposes the knowledge expression of ancient architecture based on ontology. And SWRL is used to describe the ancient building rules. Secondly, through the application of case-reasoning technology, the reuse of case knowledge of ancient architecture is realized. Ontology-based case representation can provide a unified definition of ancient building knowledge for different participants and lay the foundation for information interaction. Case-based reasoning provides an implementation method for knowledge reuse of ancient building damage cases.

scholarly journals Case-based Reasoning for Crisis Response: Case Representation and Case Retrieval

2020 ◽  
Vol 176 ◽  
pp. 1063-1072
Author(s):  
Walid Bannour ◽  
Ahmed Maalel ◽  
Henda Hajjami Ben Ghezala
Keyword(s):  
Crisis Response ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Case Representation ◽  
Case Based

SEED-Config: A case-based reasoning system for conceptual building design

2000 ◽  
Vol 14 (5) ◽  
pp. 415-430 ◽  
Author(s):  
HUGUES RIVARD ◽  
STEVEN J. FENVES
Keyword(s):  
Building Design ◽  
Design Tool ◽  
Database Management System ◽  
Design Solution ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Design Environment ◽  
Case Library ◽  
Conceptual Building Design ◽  
Case Based

A case-based design functionality is a natural and intuitive addition to a design tool that can augment human capabilities and help designers remember and retrieve appropriate cases. SEED-Config, a design environment for conceptual building design, was developed to incorporate a case-based reasoning functionality to provide designers with initial potential solutions. The case representation in SEED-Config is the BENT information model, which records design knowledge, supports the hierarchical decomposition of design cases, offers multiple views, and encapsulates the outcome of the design in addition to the problem specification and the design solution. The case library was implemented in an object-oriented database management system to accumulate cases automatically and to provide efficient query facilities. The case retrieval aspect of SEED-Config offers three different methods to find the most useful cases stored in the case library: task-based, lineage-based, and customized. Case retrieval responds to the exploratory nature of the design process and supports versatile case retrieval by providing multiple paths to each case. The case adaptation aspect, which adjusts the selected case to the new problem to provide a complete solution, uses an adaptation method called derivational replay. The case-based design capabilities are completely integrated within the design environment from which the cases originate.

scholarly journals A Hybrid Retrieval Strategy for Case-based Reasoning Using Soft Likelihood Functions

2021 ◽  
Author(s):  
Yameng Wang ◽  
Liguo Fei ◽  
Yuqiang Feng ◽  
Yanqing Wang ◽  
Luning Liu
Keyword(s):  
Operator Method ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Local Similarity ◽  
Retrieval Strategy ◽  
Case Retrieval ◽  
Likelihood Functions ◽  
The Core ◽  
Global Similarity ◽  
Case Based

Abstract Case-based reasoning (CBR) is the retrieval of one or more similar cases from an existing case base for the problem to be solved according to the characteristics of the new problem. The core idea of CBR is that similar cases have similar solutions, so whether the CBR system can play a powerful advantage depends on the quality of case retrieval strategy. At present, the commonly used case retrieval algorithm is based on the mean operator method, which is very hard, and a certain local similarity is low will affect the overall result. In order to calculate the global similarity of cases from a new and softer point of view, this paper introduces the soft likelihood functions into case retrieval, combines the soft likelihood functions with KNN, and proposes a hybrid retrieval strategy. The core of the retrieval strategy is to define the global similarity through SLFs, aggregate the local similarity and characteristic similarity together, and also take the attitude characteristics of decision makers into consideration. Through simulation experiments on real data sets, the accuracy rate is more than 81%, which verifies the effectiveness of the retrieval strategy.

Aided Design of Precision Seeder Based on Case Based Reasoning

2012 ◽  
Vol 542-543 ◽  
pp. 128-131
Author(s):  
Yu Yang
Keyword(s):  
Artificial Intelligence ◽  
Design Method ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Dynamic Configuration ◽  
Entropy Weight ◽  
Low Efficiency ◽  
Aided Design ◽  
Case Based

In connection with the low efficiency of traditional design method, case-based reasoning of artificial intelligence was applied in design of precision seeder.Combining with case representing based on characteristic method, determination method for resemblance level of machinery parts characteristics during case retrieval procedure was analyzed. Case-database was organized by MOP. The modified grey relational analysis was employed to calculate the seeder case similarity. A new case retrieval algorithm based on dynamic configuration was proposed. The weight for characteristic properties of the machinery parts was determined through standard deviation method and entropy weight method.Finally,the proposed model was demonstrated by an application instance of 2BQ-2 precision seeder. The results objectively revealed aided design method based on artificial intelligence can improve the design efficiency and shorten the design cycle effectively.

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.

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