Integration Of Case-based Reasoning Research Articles

Enhancing accuracy in cost estimation for façade works: integration of case-based reasoning, random forest, and artificial neural network techniques

Enhancing accuracy in cost estimation for façade works: integration of case-based reasoning, random forest, and artificial neural network techniques

CBR-RBR fusion based parametric rapid construction method of bridge BIM model

The development of BIM (Building Information Modeling) parametric modeling technology has improved the reusability and modeling efficiency of building information models. Aiming at the problems of too many parameters and long time in the rapid parametric construction of the bridge BIM model, we propose a novel parametric rapid construction method of bridge BIM model based on the integration of CBR (Case Based Reasoning) and RBR (Rule Based Reasoning), which is used to realize the rapid construction of bridge BIM model. Firstly, the modeling characteristics of CBR and RBR are analyzed, and a parametric modeling framework of CBR-RBR fusion is proposed. Secondly, a MHPO (Modified Hunter-Prey Optimization) algorithm is proposed to assign the weight of feature parameters to improve the accuracy of the prediction model. Then, modeling parameter reasoning rules are designed to realize the construction of parameter reasoning model. Finally, the prototype system is developed to verify the proposed method based on Revit and C#. Compared with manual modeling and traditional parametric modeling, the fusion method of CBR and RBR greatly shortens the modeling time and improves modeling efficiency and flexibility.

A Knowledge-Intensive Decision Support System for Industrial Machines Maintenance

-In industrial plants, the profitability of the plant is significantly affected by the quality of machines maintenance. To ensure continuous production, the high valued machines should be kept in good working conditions. This brings plants to search for means to control and reduce equipment failures. When faults emerge in plants, appropriate actions for fault diagnosis and troubleshooting must be executed promptly and effectively to prevent large costs due to breakdowns. To provide reliable and effective maintenance support, the aid of advanced decision support technology utilizing previous repair experience is of crucial importance for the expert operators as it provides them valuable troubleshooting clues for new faults. Artificial intelligence (AI) technology, particularly, knowledge-based approach is promising for this domain. It captures efficiency of problem solving expertise from the domain experts; guides the expert operators in rapid fault detection and troubleshooting. This paper focuses on the design and development of a Knowledge-Intensive Decision Support System (KI-DSS) for Maintenance, Repair and Service in industrial plants to support better maintenance decision and improve maintenance efficiency. With integration of case-based Reasoning and ontology, the Ki- DSS not only carries out data matching retrieval, but also performs semantic associated data access which is important for intelligent knowledge retrieval in decision support system. A case is executed to illustrate the use of the proposed KI-DSS to show the feasibility of our approach and the benefit of the ontology support.

Current Trends in the Integration of Case-Based Reasoning and Semantic Web

Case-Based Reasoning (CBR) and semantic web are intelligent methods or techniques that have been used in number of critical fields. Both methods have unique strengths and immense potential in problem solving in a wide range of domains. The main aim of this paper is to highlight an overview of the current efforts by researchers to integrate the capabilities of both techniques of CBR and semantic web. Through an analysis on the current literature involving 21 related articles, a taxonomy was developed indicating there were three stands of research focus. First, 38% (n = 8) of the articles involved studies of new methods that combined both techniques. Second, 33.3% (n = 7) of the articles dealt with the development of working frameworks or new approaches. Third, 28.6% (n = 6) of the articles focused on efforts to develop systems that integrated both techniques. In particular, the analysis showed that the medical field was the dominant field in which the integrated technique was widely used in solving medical-related problems. In light of this emerging potential, both techniques can also be applied to help provide efficient and effective problem-solving solutions for other important fields.

Integration of case-based reasoning and fuzzy approaches for real-time applications in dynamic environments: current status and future directions

This survey reviews recent researches conducted on the application of fuzzy approaches to Case-Based Reasoning (CBR) dealing with real-time applications. Fuzzy approaches have been effectively applied for knowledge representation, feature selection, and learning in CBR. Dealing with imprecise and uncertain knowledge, generalization, mining, and learning also in combination with low computational complexity are the main advantages of fuzzy approaches used in the CBR context. This paper presents and summarizes new findings on the integration of fuzzy approaches with CBR. The survey results highlight the advantages of fuzzy approaches in CBR for real-time applications. They show the current state of fuzzy-based CBR approaches. In addition, fuzzy approaches which are more operative for each operation in CBR are addressed. Those operations most contributing to the advantages of the fuzzy approach will be pointed out and detailed. Low accuracy, storage and computational challenges with a large amount of experiences and uncertainties are important issues in case of real-time applications. This paper proposes a general fuzzy-based CBR approach for real-time applications to benefit the advantages of previous approaches. Finally, some considerations of latest developments in fuzzy approaches which may be introduced as potential research directions for real-time applications are stated.

Research on the integrated system of case-based reasoning and Bayesian network

With the arrival of big data era, the integration of Case-Based Reasoning (CBR) and Bayesian Network (BN) has become increasingly a promising technology in implementing the intelligence of engineering application. To further improve the efficiency of the integrated system and make it adapt to the large number of parameter under big data, Within-Cross (WC) algorithm is proposed in this paper to assigned big data to each slave node of Hadoop platform for parallel data processing. The WC algorithm can greatly reduce the time costing of parallel data processing and thus improve the efficiency of the integrated system by fully using the computation resource of Hadoop platform. To further enhance the accuracy of the integrated system, this paper proposes a new method called Weighted Super Parameters of Dirichlet Distribution (WSPDD) algorithm to perform probability learning. The WSPDD algorithm gives a weight to each super parameter of Dirichlet Distribution of all problem parameters to adjust the result of probability statistics and then improve probability learning. Therefore, the accuracy of the integrated system is greatly improved. Finally, an application domain is taken as a case study to validate the proposed method.

Knowledge Representation Model Based on Case-Based Reasoning and the Domain Ontology: Application to the IT Consultation

The article proposes an approach to the improvement of transfer, storage and retrieval of knowledge in the field of IT consultation. The approach is based on the integration of case-based reasoning and the ontology. The description of the main concepts of the ontology has been presented. A class Precedent structure is proposed that allows to enter information about the cases in the IT consultation field, as well as to establish its links with the ontology. The proposed method assesses the closeness of cases with the current problem by means of semantic correspondence of the ontology concepts associated with the cases, by setting weighted associative links from the case instances to the concepts, allowing. to retrieve cases taking into account their semantic meaning.

A Decision Support System for Diabetes Mellitus Management

Diabetes mellitus is considered as a dangerous chronic disease. Diagnosis is the first step in its management. Clinical decision support system (CDSS) for diabetes diagnosis improves its detection and decreases the opportunity for its complications. However, its diagnosis is a theory-less problem. Case-based reasoning (CBR) is a problem-solving paradigm that uses past experiences to solve new problems. Integration of CBR and formal ontologies enhances the intelligence of this paradigm. Utilizing patients’ electronic health records (EHRs) for building case-base knowledge solves the problem of knowledge acquisition bottleneck; however, preparation steps are required. Moreover, using standard medical ontologies, such as SNOMED-CT, enhances the interoperability and integration of CDSS with the healthcare system. If ontology-based CBR systems utilize vague or imprecise knowledge, the semantic effectiveness is further improved. This paper proposes an advanced and complete fuzzy-ontology-based CBR framework that manages and utilizes imprecise knowledge. We implement the most critical steps in CBR (i.e., case representation and retrieval). The implemented framework has been tested on the diabetes diagnosis problem using a case-base of 60 real cases from The EHR of the Mansoura University Hospitals, Mansoura, Egypt. The proposed system has an accuracy of 97.67%.

Multi Agent System-Based on Case Based Reasoning for Cloud Computing System

Cloud Computing is a new technology which use the Internet and central remote servers in order to maintain data and applications. The aim of this paper is to describe about Case Based Reasoning (CBR) which is based on Multi Agent System (MAS) and the implementation in Cloud Computing Environment to assist the Cloud Service Provider (CSP) to deliver a number of services to the cloud users according to their needs. The Introduction Section gives an introduction about Cloud Computing, MAS and CBR Integration, and defines the characteristics and process cycle of CBR. The Second Section describes about the MAS-Based Cloud Computing. The Third Section describes about the CBR in Cloud Computing. The Fourth Section describes the CBR (MAS-Based) for Cloud Computing and Interaction between CBR Agents and components of Cloud Computing. The Final section gives a conclusion about the paper.

Integration of case-based reasoning and object-based image classification to classify SPOT images: a case study of aquaculture land use mapping in coastal areas of Guangdong province, China

We present a method to integrate case-based reasoning (CBR) with object-oriented image classification to classify SPOT images. Images were first segmented into discrete objects at multiple scales. CBR was then used to classify these objects by comparing their geometric shapes, spectral characteristics, and textural measurements with those of the past objects prepared from archived SPOT images and land use data. Once enough past objects were accumulated, this method was able to successfully classify image objects with promising results as demonstrated by a case study of aquaculture land use mapping in coastal areas of Guangdong province, China.

Case-based reasoning in comparative effectiveness research

A medical case, as defined by the medical field, is the fundamental building block of medical knowledge. We combine the concept of medical case with indirect evidence from similar patients so as to enable the performance of CER (comparative effectiveness research) treatment comparisons for personalization of medical care. We propose that the vehicle by which this goal is achieved is provided by the integration of case-based reasoning and CER considerations. We discuss the challenges involved and propose a roadmap for creating scientifically sound real-world evidence for assisting personalized treatment decisions.

Integration of multi-technology on oil spill emergency preparedness

This paper focuses on the integration of technologies including Case-Based Reasoning (CBR), Genetic Algorithm (GA) and Artificial Neural Network (ANN) for establishing emergency preparedness for oil spill accidents. In CBR, the Frame method is used to define case representation, and the HEOM (Heterogeneous Euclidean-Overlap Metric) is improved to define the similarity of case properties. In GA, we introduce an Improved Genetic Algorithm (IGA) that achieves case adaptation, in which technologies include the Multi-Parameter Cascade Code method, the Small Section method for generation of an initial population, the Multi-Factor Integrated Fitness Function, and Niche technology for genetic operations including selection, crossover, and mutation. In ANN, a modified back-propagation algorithm is employed to train the algorithm to quickly improve system preparedness. Through the analysis of 32 fabricated oil spill cases, an oil spill emergency preparedness system based on the integration of CBR, GA and ANN is introduced. In particular, the development of ANN is presented and analyzed. The paper also discusses the efficacy of our integration approach.

An approach for definition of recommendations for prevention of repeated failures with the aid of case-based reasoning and group decision-making methods

An approach for definition of recommendations for the prevention of repeated failures is discussed. The approach is based on the integration of case-based reasoning and group decision-making methods. The case model, case retrieval and case reuse algorithms are described. Description of the software and an example of the approach application are presented.

Case-based reasoning integrations

This article presents an overview and survey of current work in case-based reasoning (CBR) integrations. There has been a recent upsurge in the integration of CBR with other reasoning modalities and computing paradigms, especially rule-based reasoning (RBR) and constraint-satisfaction problem (CSP) solving. CBR integrations with model-based reasoning (MBR), genetic algorithms, and information retrieval are also discussed. This article characterizes the types of multimodal reasoning integrations where CBR can play a role, identifies the types of roles that CBR components can fulfill, and provides examples of integrated CBR systems. Past progress, current trends, and issues for future research are discussed.

INTRODUCTION TO THE SPECIAL ISSUE ON CASE‐BASED REASONING IN THE HEALTH SCIENCES

There has been an explosion of interest in health sciences applications of case-based reasoning (CBR), not only in the traditional CBR in medicine domain, but also in bioinformatics, enabling home health-care technologies, CBR integration, and synergies between CBR and knowledge discovery. This special issue features the best papers from the third workshop on CBR in the health sciences, held at ICCBR-05 in Madrid. It is the third in a series of exciting workshops, the first two of which were held at ICCBR-03, in Trondheim, Norway, and at ECCBR-04, in Madrid, Spain. The nine high-quality papers introduced here represent the research and experience of twenty-two authors working in eight different countries on a wide range of problems and projects. These papers illustrate some of the major trends of current research in CBR in the health sciences, and represent overall an excellent sample of the most recent advances of CBR in the health sciences.

Learning HAZOP expert system by case-based reasoning and ontology

To improve the learning capability of HAZOP expert systems, a new learning HAZOP expert system called PetroHAZOP has been developed based on the integration of case-based reasoning (CBR) and ontology that can help automate “non-routine” HAZOP analysis. PetroHAZOP consists of four modules including case base module, CBR engine module, knowledge maintenance module and user graphical interface module. Within the case base, HAZOP analysis knowledge is represented as cases which are organized with a hierarchical structure. Similarity-based case retrieval algorithm is also depicted to find the closest-matching cases. In order to enhance the case retrieval, a new set of ontologies for CBR-based HAZOP analysis is created by integration of existing ontologies reported in literature. Finally the application of PetroHAZOP is demonstrated by two case studies of industrial processes.

Children’s & Young People’s Nursing in Practice: A Problem-based Learning Approach

Children’s and Young People’s Nursing in Practice: a Problem-based Learning Approach represents a comprehensive, innovative and up to date resource that would serve as a useful core text for both pre-qualifying and post-qualifying students undertaking children and young people’s nursing programmers, as well as a supporting resource for academics. The authors are not only experts in their practice field but have proven successes in the effective integration of case-based reasoning and problem solving-based learning and teaching approaches within the curriculum. This text offers readers a resource that brings together the mass of concepts, theories and evidence-based practice that are so fundamental to children and young people’s nursing.

INTRODUCTION TO THE SPECIAL ISSUE ON CASE‐BASED REASONING IN THE HEALTH SCIENCES

There has been an explosion of interest in health sciences applications of case‐based reasoning (CBR), not only in the traditional CBR in medicine domain, but also in bioinformatics, enabling home health‐care technologies, CBR integration, and synergies between CBR and knowledge discovery. This special issue features the best papers from the third workshop on CBR in the health sciences, held at ICCBR‐05 in Madrid. It is the third in a series of exciting workshops, the first two of which were held at ICCBR‐03, in Trondheim, Norway, and at ECCBR‐04, in Madrid, Spain. The nine high‐quality papers introduced here represent the research and experience of twenty‐two authors working in eight different countries on a wide range of problems and projects. These papers illustrate some of the major trends of current research in CBR in the health sciences, and represent overall an excellent sample of the most recent advances of CBR in the health sciences.

A New Model for Multi-agent Case-base Reasoning Systems

Multi-Agent Systems (MASs) and Case-Based Reasoning (CBR) are two recent and hot paradigms in artificial intelligence field. CBR is a reasoning methodology based on old experience reasoning or similarity-based reasoning while MAS is a new paradigm to organize AI applications. CBR offers multi-agent systems the capability of autonomously learning from experience. This study examines the integration of CBR, MASs and Expert Systems (ESs). In addition, it presents a knowledge-based model of multi-agent CBR systems from both a logical and a knowledge-based viewpoint.

Risk management – dependability tools and case-based reasoning integration using the object formalism

The general purpose of this work is to define a dependability analysis framework in order to implement a CASE tool dedicated to dependability. Practically, we also aim at constructing an adapted data model. Using the object formalism, this model is based on generic items used in risk management and aims at improving tools integration and co-operation by structuring knowledge and expertise. We focus here on the extension of usual mechanisms towards decision processes and tools, especially case-based reasoning.