Contextual case-based reasoning applied to a mobile device

2017 ◽  
Vol 13 (3) ◽  
pp. 282-299 ◽  
Author(s):  
Djamel Guessoum ◽  
Moeiz Miraoui ◽  
Chakib Tadj
Keyword(s):  
Mobile Device ◽  
Inertial Sensors ◽  
Contextual Information ◽  
Similarity Measures ◽  
Case Based Reasoning ◽  
Two Stage ◽  
Minimal Processing ◽  
Current Case ◽  
Content Type ◽  
Case Based

Purpose This paper aims to apply a contextual case-based reasoning (CBR) to a mobile device. The CBR method was chosen because it does not require training, demands minimal processing resources and easily integrates with the dynamic and uncertain nature of pervasive computing. Based on a mobile user’s location and activity, which can be determined through the device’s inertial sensors and GPS capabilities, it is possible to select and offer appropriate services to this user. Design/methodology/approach The proposed approach comprises two stages. The first stage uses simple semantic similarity measures to retrieve the case from the case base that best matches the current case. In the second stage, the obtained selection of services is then filtered based on current contextual information. Findings This two-stage method adds a higher level of relevance to the services proposed to the user; yet, it is easy to implement on a mobile device. Originality/value A two-stage CBR using light processing methods and generating context aware services is discussed. Ontological location modeling adds reasoning flexibility and knowledge sharing capabilities.

An agent-based supplier selection framework

2015 ◽  
Vol 8 (2/3) ◽  
pp. 180-205 ◽  
Author(s):  
Alireza Jahani ◽  
Masrah Azrifah Azmi Murad ◽  
Md. Nasir bin Sulaiman ◽  
Mohd. Hasan Selamat
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Information Overload ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Common Framework ◽  
Multi Agent ◽  
Case Based

Purpose – The purpose of this paper is to propose an approach that integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning. Unsatisfied customers, information overload and high uncertainty are the main challenges that are faced by today’s supply chains. In addition, a few existing agent-based approaches are tied to real-world supply chain functions like supplier selection. These approaches are static and do not adequately take the qualitative and quantitative factors into consideration. Therefore, an agent-based framework is needed to address these issues. Design/methodology/approach – The proposed approach integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning, as a common framework. These perspectives were rarely used together as a common framework in previous studies. Furthermore, an exploratory case study in an office furniture company is undertaken to illustrate the value of the framework. Findings – The proposed agent-based framework evaluates supply offers based on customers’ preferences, recommends alternative products in the case of stock-out and provides a collaborative environment among agents who represent different supply chain entities. The proposed fuzzy case-based reasoning (F-CBR) approach reduces the information overload by organizing them into the relevant cases that causes less overall search between cases. In addition, its fuzzy aspect addresses the high uncertainty of supply chains, especially when there are different customers’ orders with different preferences. Research limitations/implications – The present study does not include the functions of inventory management and negotiation between agents. Furthermore, only the case description and case retrieval phases of the case-based reasoning approach are investigated, and the remaining phases like case retaining, case reusing and case revising are not included in the scope of this paper. Originality/value – This framework balances the interests of different supply chain structural elements where each of them is represented by a specific agent for better collaboration, decision-making and problem-solving in a multi-agent environment. In addition, the supplier selection and order gathering mechanisms are developed based on customers’ orders.

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.

Integrating fuzzy case-based reasoning, parametric and feature-based cost estimation methods for machining process

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fentahun Moges Kasie ◽  
Glen Bright
Keyword(s):  
Cost Estimation ◽  
Intelligent System ◽  
Machining Process ◽  
Estimation Methods ◽  
Case Based Reasoning ◽  
Cost Estimates ◽  
Content Type ◽  
Estimation Techniques ◽  
Feature Based ◽  
Case Based

Purpose This paper aims to propose an intelligent system that serves as a cost estimator when new part orders are received from customers. Design/methodology/approach The methodologies applied in this study were case-based reasoning (CBR), analytic hierarchy process, rule-based reasoning and fuzzy set theory for case retrieval. The retrieved cases were revised using parametric and feature-based cost estimation techniques. Cases were represented using an object-oriented (OO) approach to characterize them in n-dimensional Euclidean vector space. Findings The proposed cost estimator retrieves historical cases that have the most similar cost estimates to the current new orders. Further, it revises the retrieved cost estimates based on attribute differences between new and retrieved cases using parametric and feature-based cost estimation techniques. Research limitations/implications The proposed system was illustrated using a numerical example by considering different lathe machine operations in a computer-based laboratory environment; however, its applicability was not validated in industrial situations. Originality/value Different intelligent methods were proposed in the past; however, the combination of fuzzy CBR, parametric and feature-oriented methods was not addressed in product cost estimation problems.

Methods of Software Quality Prediction With Similarity Measures

2017 ◽  
pp. 34-56
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Similarity Measures ◽  
Fault Prediction ◽  
Estimation Accuracy ◽  
Case Based Reasoning ◽  
Quality Prediction ◽  
Software Fault Prediction ◽  
Software Quality Prediction ◽  
Case Based

To improve the software quality the number of errors or faults must be removed from the software. This chapter presents a study towards machine learning and software quality prediction as an expert system. The purpose of this chapter is to apply the machine learning approaches such as case-based reasoning to predict software quality. Five different similarity measures, namely, Euclidean, Canberra, Exponential, Clark and Manhattan are used for retrieving the matching cases from the knowledgebase. The use of different similarity measures to find the best method significantly increases the estimation accuracy and reliability. Based on the research findings in this book it can be concluded that applying similarity measures in case-based reasoning may be a viable technique for software fault prediction

A case-based reasoning system for fault detection and isolation: a case study on complex gearboxes

2019 ◽  
Vol 25 (2) ◽  
pp. 213-235 ◽  
Author(s):  
Soumava Boral ◽  
Sanjay Kumar Chaturvedi ◽  
V.N.A. Naikan
Keyword(s):  
Decision Making ◽  
Fault Detection ◽  
Large Scale ◽  
Fault Detection And Isolation ◽  
Decision Making Process ◽  
Case Based Reasoning ◽  
Content Type ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Case Based

Purpose Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar. Design/methodology/approach CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI). Findings The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers. Originality/value The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

R4 Model for Case-Based Reasoning and Its Application for Software Fault Prediction

2016 ◽  
Vol 8 (3) ◽  
pp. 19-38 ◽  
Author(s):  
Ekbal Rashid
Keyword(s):  
Software Quality ◽  
Similarity Measures ◽  
Fault Prediction ◽  
Maintenance Cost ◽  
Quality Level ◽  
Case Based Reasoning ◽  
Quality Prediction ◽  
Error Level ◽  
Software Fault Prediction ◽  
Case Based

Making R4 model effective and efficient I have introduced some new features, i.e., renovation of knowledgebase (KBS) and reducing the maintenance cost by removing the duplicate record from the KBS. Renovation of knowledgebase is the process of removing duplicate record stored in knowledgebase and adding world new problems along with world new solutions. This paper explores case-based reasoning and its applications for software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The system predicts the error level with respect to LOC and with respect to development time, and both affects the quality level. This paper also reviews four existing models of case-based reasoning (CBR). The paper presents a work in which I have expanded our previous work (Rashid et al., 2012). I have used different similarity measures to find the best method that increases reliability. The present work is also credited through introduction of some new terms like coefficient of efficiency, i.e., developer's ability.

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.

A developed case-based reasoning system for machine tool selection

2017 ◽  
Vol 24 (5) ◽  
pp. 1364-1385 ◽  
Author(s):  
Shankar Chakraborty ◽  
Soumava Boral
Keyword(s):  
Machine Tool ◽  
Expert Knowledge ◽  
Fine Tuning ◽  
Selection Problem ◽  
Case Based Reasoning ◽  
Tool Selection ◽  
Diverse Range ◽  
Content Type ◽  
Machine Tool Selection ◽  
Case Based

Purpose Subtractive manufacturing process is the controlled removal of unwanted material from the parent workpiece for having the desired shape and size of the product. Several types of available machine tools are utilized to carry out this manufacturing operation. Selection of the most appropriate machine tool is thus one of the most crucial factors in deciding the success of a manufacturing organization. Ill-suited machine tool may often lead to reduced productivity, flexibility, precision and poor responsiveness. Choosing the best suited machine tool for a specific machining operation becomes more complex, as the process engineers have to consider a diverse range of available alternatives based on a set of conflicting criteria. The paper aims to discuss these issues. Design/methodology/approach Case-based reasoning (CBR), an amalgamated domain of artificial intelligence and human cognitive process, has already been proven to be an effective tool for ill-defined and unstructured problems. It imitates human reasoning process, using specific knowledge accumulated from the previously encountered situations to solve new problems. This paper elucidates development and application of a CBR system for machine tool selection while fulfilling varying user defined requirements. Here, based on some specified process characteristic values, past similar cases are retrieved and reused to solve a current machine tool selection problem. Findings A software prototype is also developed in Visual BASIC 6.0 and three real time examples are illustrated to validate the application potentiality of CBR system for the said purpose. Originality/value The developed CBR system for machine tool selection retrieves a set of similar cases and selects the best matched case nearest to the given query set. It can successfully provide a reasonable solution to a given machine tool selection problem where there is a paucity of expert knowledge. It can also guide the process engineers in setting various parametric combinations for achieving maximum machining performance from the selected machine tool, although fine-tuning of those settings may often be required.

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