scholarly journals A case retrieval method combined with similarity measurement and DEA model for alternative generation

2018 ◽  
Vol 11 (1) ◽  
pp. 1123 ◽  
Author(s):  
Jing Zheng ◽  
Ying-Ming Wang ◽  
Kai Zhang
Keyword(s):  
Similarity Measurement ◽  
Case Retrieval ◽  
Retrieval Method ◽  
Dea Model ◽  
Alternative Generation

Heterogeneous multi-attribute case retrieval method based on group decision making using incomplete weight information

2021 ◽  
pp. 1-13
Author(s):  
Kai Zhang ◽  
Jing Zheng ◽  
Ying-Ming Wang
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Interval Numbers ◽  
Retrieval Method ◽  
Incomplete Weight ◽  
Incomplete Weight Information ◽  
Evaluation Information

Case-based reasoning (CBR) is one of the most popular methods used in emergency decision making (EDM). Case retrieval plays a key role in EDM processes based on CBR and usually functions by retrieving similar historical cases using similarity measurements. Decision makers (DMs), thus, choose the most appropriate historical cases. Although uncertainty and fuzziness are present in the EDM process, in-depth research on these issues is still lacking. In this study, a heterogeneous multi-attribute case retrieval method based on group decision making (GDM) with incomplete weight information is developed. First, the case similarities between historical and target cases are calculated, and a set of similar historical cases is constructed. Six formats of case attributes are considered, namely crisp numbers, interval numbers, linguistic variables, intuitionistic fuzzy numbers, single-valued neutrosophic numbers (NNs) and interval-valued NNs. Next, the evaluation information from the DMs is expressed using single-valued NNs. Additionally, the evaluation utilities of similar historical cases are obtained by aggregating the evaluation information. The comprehensive utilities of similar historical cases are obtained using case similarities and evaluation utilities. In this process, the weights of incomplete information are determined by constructing optimization models. Furthermore, the most appropriate similar historical case is selected according to the comprehensive utilities. Finally, the proposed method is demonstrated using two examples; its performance is then compared with those of other similar methods to demonstrate its validity and efficacy.

scholarly journals Design and Implementation of a Student Archive Retrieval Method Based on Image Processing

2020 ◽  
Vol 34 (4) ◽  
pp. 487-494
Author(s):  
Lei An ◽  
Aihua Li
Keyword(s):  
Image Processing ◽  
Information Management ◽  
Image Feature ◽  
Similarity Measurement ◽  
Function Learning ◽  
Retrieval Method ◽  
Design And Implementation ◽  
Network Modules ◽  
Novel Method ◽  
Effectiveness And Efficiency

Compared with traditional manual archive organization and review, the student archive management system can manage massive student archives in a refined, regular, and scientific manner. The effectiveness and efficiency of the retrieval method directly bears on the utilization effect of student archives. Based on image processing, this paper puts forward a novel method for student archive retrieval, which greatly improves the classification, recognition, and information management of images in student archives during the retrieval. Firstly, a framework of student archive retrieval was introduced based on image processing. Next, a deep convolutional neural network (DCNN) was constructed for hash learning, and the functions of the three network modules were detailed, including image feature extraction, hash function learning, and similarity measurement. Finally, several indices were selected to evaluate the retrieval effect of student archives. The proposed method was proved effective and feasible through contrastive experiments. The research results provide a theoretical reference for the application of our method in other fields of image retrieval.

Case Retrieval Mechanism of the Intelligent RCM Analysis System

2010 ◽  
Vol 40-41 ◽  
pp. 686-691
Author(s):  
Zhong Hua Cheng ◽  
Lu Chao Wang ◽  
Li Bo Lv
Keyword(s):  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
Case Retrieval ◽  
Analytic Hierarchy ◽  
Retrieval Method ◽  
Analysis Process ◽  
Reliability Centered Maintenance ◽  
Retrieval Mechanism ◽  
Analysis System ◽  
Hierarchy Process

To improve Reliability Centered Maintenance (RCM) analysis efficiency, the Artificial Intelligence (AI) technology, such as case-based reasoning (CBR) is successfully introduced into RCM analysis process and an intelligent RCM analysis (IRCMA) was studied, and an intelligent RCM analysis system (IRCMAS) was developed. The idea for IRCMAS is based on the fact that the historical records of RCM analysis on similar items can be referenced and used for the current RCM analysis of a new item. Case retrieval is the key part of the IRCMAS, of which mechanism has an importance effect on reasoning efficiency of system. In this paper, the IRCMAS is introduced, retrieval mechanism and process of cases are presented, and nearest neighbor retrieval method based on analytic hierarchy process (AHP) is in detail discussed by an example. Design of case retrieval mechanism lays steady foundation for development and realization of intelligent RCM analysis system.

A new case retrieval method based on double frontiers data envelopment analysis

2019 ◽  
Vol 36 (1) ◽  
pp. 199-211 ◽  
Author(s):  
Jing Zheng ◽  
Ying-Ming Wang ◽  
Lei Chen ◽  
Kai Zhang
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Case Retrieval ◽  
Retrieval Method
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