Use Of Case-based Reasoning Research Articles

A pre-control method in safety risk management of metro construction

ABSTRACT With the rapid development of metro construction in China, construction safety accidents occur frequently due to the lack of effective safety risk controls. Continuously improving the level of safety risk pre-control has become a significant factor in the safety managements of metro construction. This paper presents a reasoning model for safety risk pre-control measures in metro construction using case-based reasoning. To establish the case database, a frame-based knowledge representation method is adopted based on the characteristics of metro construction safety accident cases. Additionally, a two-level retrieval strategy combining hierarchical retrieval and nearest-neighbor algorithms is used for case retrieval. Furthermore, a new approach, the cloud model, is proposed to determine the attribute weight of metro construction safety accidents. This approach addresses quantitative attributes with fuzziness and randomness. Finally, a specific metro station is taken as a case to validate the feasibility of the approach. The results showed that the case-based reasoning method enables engineers to quickly and effectively develop risk pre-control measures based on previous cases. In addition, the use of case-based reasoning in decision-making enhances knowledge sharing and reuse in the metro construction industry. This paper presents an effective reasoning approach for pre-control measure analysis in metro construction, allowing engineers to quickly and efficiently carry out scientific and reasonable pre-control measures from past cases.

Extended ArchiMate Metamodel with a context-awareness layer for a dynamic Enterprise architecture model

Nowadays, in a turbulent environment, enterprise must be proactive and even predictive to survive in front of the plenty natures of dynamics. Achieving the desired proactivity and predictability, supposes that the enterprise works in advance on its internal transformation plans, but also develops an awareness to its context, to intercept, analyze and simulate any potential trigger of change that could affect its stability or more precisely its state of equilibrium. In this work, the concept of enterprise architecture is considered as a tool for establishing alignment between business, strategy, and IT. This paper is a continuation to previous works seeking for a dynamic model of enterprise architecture, able to respond in an autonomic manner to the turbulent environment. The self-adaptiveness was highlighted basically using the well-known loop, monitor, analyze, plan, execute and Knowledge. The use of Case-Based Reasoning was suggested to store a collection of problems and their resolutions, then using algorithms of similarity and adaptation to suggest the problem’s best fit solution. To make the reasoning mechanism and similarity process as reliable as possible, it was important to maximize experiences and expand the knowledge base. For thus, the proposition was to share knowledge bases. In this paper, the context awareness is considered as second source to enrich the knowledge base and to stay vigilant and sensitive to triggers of change.

Evaluation of case-based reasoning to estimate liquefaction manifestation

This article develops a framework for and explores the use of case-based reasoning (CBR) to predict seismically induced liquefaction manifestation. CBR is an artificial intelligence process that solves new problems using the known answers to similar past problems. CBR sorts a database of case histories based on their similarity to a design case and predicts the outcome of the design case as the observed outcome of the most similar case history or majority outcome of the most similar case histories. Two databases of liquefaction case histories are used to develop and validate numerous CBR models. Different input parameters and aspects of the CBR method and their influence on the predictive capability of the models are evaluated. Some of the developed CBR models were shown to have a better predictive power than currently existing models. However, more research is needed to refine these models before they can be used in practice. Nevertheless, this study shows the potential of CBR as a method to estimate liquefaction manifestation and suggests several avenues of future research.

Use of Case Based Reasoning (CBR) Methods to Diagnosis Diseases in Pregnancy

Diseases in pregnant women are diseases that many people need to pay attention to, because diseases that occur in pregnant women will not only endanger one life, but more than that. Hypertension currently occupies the 2nd position as a type of disease that threatens the lives of many pregnant women. The application of Case Based Reasoning (CBR) in diagnosing diseases that occur during pregnancy is motivated by the difficulty of consulting an obstetrician due to costs, time or even the limited number of doctors in a hospital. The use of CBR aims to solve new problems by adapting solutions to problems that occurred before. The expert system itself is one of the solutions to solve problems faced by users in the health sector, this system can minimize costs incurred to consult about diseases in pregnancy to specialist doctors. "Use of Case Base Reasoning (CBR) to Diagnose Diseases in Pregnancy" is expected to help the general public, especially pregnant women, make a simple diagnosis of symptoms and diseases in pregnancy.

Improving the evaluation of change requests using past cases

As one of the leading causes of project failures, requirements changes are inevitable in any software project. Hence, we propose an intelligent approach to facilitate the risk analysis of a change request by providing information about past cases found in similar change requests, the solutions adopted, and a support tool. The proposed approach uses case-based reasoning to retrieve previous cases similar to the current case. This approach also uses association rules to analyze patterns in the dataset and calculate the probability of risks associated with change requests. We prepared a case study to validate the proposal by analyzing the most frequent challenges in change management and considering how it can solve or minimize such problems. Results show that the proposed approach successfully assists decision-making, predicts potential risks, and suggests coherent solutions to the user. We have developed a support tool to evaluate this approach in practice with experts and obtained four different outcomes. Only a small set of cases failed to provide relevant results to the user. The use of case-based reasoning and association rules has proven to be advantageous in change management despite validity threats associated with the small number of test cases and experts involved.

Training students for intercultural communication in a dynamic world: a challenge in continuing education

The article discusses the problems related to preparing students for intercultural communication within the context of continuing education. The authors develop a set of definitions concerning the process of training students for intercultural communication. Additionally, the current need to review conventional approaches to the preparation of students for intercultural communication vis-à-vis the implementation of novel strategies is highlighted. The primary goal of the research is to establish a repeatable preparation process, which is practice-focused. The range of tasks suggested by the authors reflects the practical application of these strategies. Thus the following aspects have been identified as the most critical drivers: (1) a continuous and practice-focused process which facilitates the preparation for intercultural communication; (2) the use of case-based reasoning for researching and updating the tools and methods within the process of preparation of students for intercultural communication; (3) and a systematic approach to task development within the learning process (e.g., case studies, role plays, mixed interaction, networking, etc.). This method enables the adoption and concurrent adjustment of the teaching practices in order to fulfil the educational goals of diverse intercultural communication programs. Optimized practices and strategies can subsequently be collected, disseminated, and ultimately replicated within other post-secondary settings and contexts.

Theory-Guided Analytics Process: Using Theories to Underpin an Analytics Process for New Banking Product Development Using Segmentation-Based Marketing Analytics Leveraging on Marketing Intelligence

Retail banking is undergoing considerable product competitiveness and disruptions. New product development is necessary to tackle such challenges and reinvigorate product lines. This study presents an instrumental real-life banking case study, where marketing analytics was utilized to drive a product differentiation strategy. In particular, the study applied unsupervised machine learning techniques of link analysis, latent class analysis, and association analysis to undertake behavioral-based market segmentation, in view of attaining a profitable competitive advantage. To underpin the product development process with well grounded theoretical framing, this study asked the research question: “How may we establish a theory-driven approach for an analytics-driven process?” Findings of this study include a theoretical conceptual framework that underpinned the end-to-end segmentation-driven new product development process, backed by the empirical literature. The study hopes to provide: (i) for managerial practitioners, the use of case-based reasoning for practice-oriented new product development design, planning, and diagnosis efforts, and (ii) for researchers, the potentiality to test of the validity and robustness of an analytical-driven NPD process. The study also hopes to drive a wider research interest that studies the theory-driven approach for analytics-driven processes.

Digital-supported problem solving for shopfloor steering using case-based reasoning and Bayesian networks

Uncertainty and incompleteness of data challenge the design of knowledge systems for problem solving in shopfloor management. The paper proposes a data-driven design that incorporates traditional means of quality management and goals of production planning and control. It integrates data of a failure mode effects analysis (FMEA) and an 8D problem-solving process into a Bayesian network (BN) embedded case-based reasoning (CBR) cycle. Reducing inconsistencies within the BN, an optimization method uses scoring schemes and structural equation modeling for learning its structure. The results suggest that the optimized BN-CBR system outperforms the single use of CBR in terms of accuracy.

Coal and Gas Outburst Risk Prediction and Management Based on WOA-ELM

A gas outburst risk level prediction method, based on the Whale Optimization Algorithm (WOA) Improved Extreme Learning Machine (ELM), is proposed to predict the coal and gas outburst hazard level more accurately. Based on this method, recommendations are given according to the gas outburst risk level with the help of the Case-Based Reasoning (CBR) method. Firstly, we analyze the accident reports of gas outburst accidents, select the gas outburst risk prediction index, and construct the gas outburst risk prediction index system by combining the gas outburst prevention and control process. The WOA-ELM model was used to predict the gas outburst risk level by selecting data from 150 accident reports from 2008 to 2021. Again, based on the coal and gas outburst risk level, CBR is used to match the cases and give corresponding suggestions for different levels of gas outburst risk conditions to help reduce the gas outburst risk. The results show that the WOA-ELM algorithm has better performance and faster convergence than the ELM algorithm, when compared in terms of accuracy and the error of gas outburst hazard prediction. The use of CBR to manage prediction results can be helpful for decision-makers.

On Case Base Formation in Real-Time Heuristic Search

Real-time heuristic search algorithms obey a constant limit on planning time per move. Agents using these algorithms can execute each move as it is computed, suggesting a strong potential for application to real-time video-game AI. Recently, a breakthrough in real-time heuristic search performance was achieved through the use of case-based reasoning. In this framework, the agent optimally solves a set of problems and stores their solutions in a case base. Then, given any new problem, it seeks a similar case in the case base and uses its solution as an aid to solve the problem at hand. A number of ad hoc approaches to the case base formation problem have been proposed and empirically shown to perform well. In this paper, we investigate a theoretically driven approach to solving the problem. We mathematically relate properties of a case base to the suboptimality of the solutions it produces and subsequently develop an algorithm that addresses these properties directly. An empirical evaluation shows our new algorithm outperforms the existing state of the art on contemporary video-game pathfinding benchmarks.

Application of Case-Based Reasoning to the safety assessment of critical software used in rail transport

The risk assessment of railway accidents requires the implementation of several safety analysis methods such as Preliminary Hazard Analysis, Functional Safety Analysis, Analysis of Failure Modes, their Effects and their Criticality and Software Error Effect Analysis (SEEA). The study proposed within the framework of this article concerns the SEEA method whose objective is to determine the nature and severity of the consequences of software failures, to propose measures to detect errors and improve the robustness of the software. The goal is to develop a new approach to analysis and evaluation of the safety of critical software, based on machine learning and more precisely on the Case-Based Reasoning (CBR). The approach adopted involves two main activities: The first step in acquiring safety knowledge consists in extracting, modeling and archiving dangerous situations to produce a library of standard cases which covers the entire problem. The second stage of machine learning is to exploit historical knowledge (experience feedback) in order to assist safety experts in their critical task of analyzing and assessing the safety of software involved in guided or automated rail transport systems. This second activity involves the use of Case-Based Reasoning (CBR).

Contribution of artificial intelligence and machine learning to the assessment of the safety of critical software used in railway transport

As part of the process of certification and commissioning of a new guided or automated rail transport system, the domain experts and in particular the National Safety Authority are responsible for reviewing the safety of the system to ensure that the safety level of the new transport system is at least equivalent to the railway systems already in service and deemed safe. This critical task of evaluating safety essentially concerns all the safety files prepared by the manufacturer and in particular safety studies such as the Preliminary Hazard Analysis (PHA), the functional safety analysis (FSA), the analysis of failure modes, their effects and of their criticality (AFMEC) or Software Error Effect Analysis (SEEA). The study presented in this paper is part of the SEEA analysis. To respect the completeness and consistency of this safety analysis (SEEA), the experts carry out complementary analyses of safety. They are brought to imagine new scenarios of potential accidents to perfect the exhaustiveness of the safety studies. In this process, one of the difficulties then consists in finding the abnormal scenarios being able to lead to a particular potential accident. This is the fundamental point that motivated this work. To help experts in this complex process of evaluating safety studies, we agreed to use artificial intelligence techniques and in particular machine learning to systematize, streamline and strengthen conventional approaches to safety analysis and critical software certification. The approach which was adopted in order to design and implement an assistance tool for safety analysis involved the following two main activities: – Extracting, formalizing and storing hazardous situations to produce a library of standard cases which covers the entire problem. This process entailed the use of knowledge acquisition techniques; – Exploiting the stored historical knowledge in order to develop safety analysis know-how which can assist experts to judge the thoroughness of the manufacturer’s suggested safety analysis. This second activity involves the use of machine learning techniques in particular the use of case-based reasoning (CBR).

Improving emergency response to cascading disasters: Applying case-based reasoning towards urban critical infrastructure

With the increase of the interconnections and interdependencies between urban critical infrastructures (UCIs), natural and anthropogenic triggers like typhoons, earthquakes, and fires are more likely to generate secondary emergencies and escalate into cascading disasters. However, most of the existing studies are focused on single disaster response, with limited capability to influence emergency management in complex events. This paper aims to fill this gap, analyzing the possible use of case-based reasoning (CBR) and the historical cases to increase the effectiveness of emergency decision-making in cascading disasters. We show that CBR can support the deployment of alternative emergency response tasks in the initial phase of response, helping to not only cope with the trigger disaster, but also anticipate and address the possible escalations of secondary disasters. In conclusion, the proposed method is validated through the description of a case study in China and the analysis indicates that the generated emergency response solutions can properly meet the practical requirements to deal with the complex disaster situation towards UCI, explaining its replicability and highlighting the open questions for future studies.

Toward an Agile Approach to Managing the Effect of Requirements on Software Architecture during Global Software Development

Requirement change management (RCM) is a critical activity during software development because poor RCM results in occurrence of defects, thereby resulting in software failure. To achieve RCM, efficient impact analysis is mandatory. A common repository is a good approach to maintain changed requirements, reusing and reducing effort. Thus, a better approach is needed to tailor knowledge for better change management of requirements and architecture during global software development (GSD).The objective of this research is to introduce an innovative approach for handling requirements and architecture changes simultaneously during global software development. The approach makes use of Case-Based Reasoning (CBR) and agile practices. Agile practices make our approach iterative, whereas CBR stores requirements and makes them reusable. Twin Peaks is our base model, meaning that requirements and architecture are handled simultaneously. For this research, grounded theory has been applied; similarly, interviews from domain experts were conducted. Interview and literature transcripts formed the basis of data collection in grounded theory. Physical saturation of theory has been achieved through a published case study and developed tool. Expert reviews and statistical analysis have been used for evaluation. The proposed approach resulted in effective change management of requirements and architecture simultaneously during global software development.

SciELO suggester: An intelligent support tool for cataloging library resources

Existing cataloging interfaces are designed to reduce the bottleneck of creating, editing, and refining bibliographic records by offering a convenient framework for data entry. However, the cataloger still has to deal with the difficult task of deciding what information to include. The SciELO Suggester system is an innovative tool developed to overcome certain general limitations encountered in current mechanisms for entering descriptions of library records. The proposed tool provides useful suggestions about what information to include in newly created records. Thus, it assists catalogers with their task, as they are typically unfamiliar with the heterogeneous nature of the incoming material. The suggester tool applies case-based reasoning to generate suggestions taken from material previously cataloged in the SciELO scientific electronic library. The system is implemented as a web service and it can be easily used by installing an add-on for the Mozilla Firefox browser. The tool has been evaluated through a human-subject study with catalogers and through an automatic test using a collection consisting of 5742 training examples and 120 test cases from 12 different subject areas. In both experiments the system has shown very good performance. These evaluations indicate that the use of case-based reasoning provides a powerful alternative to traditional ways of identifying subject areas and keywords in library resources. In addition, a heuristic evaluation of the tool was carried out by taking as a starting point the Sirius heuristic-based framework, resulting in a very good score. Finally, a specially designed cognitive walk was completed with catalogers, providing additional insights into the strengths and weaknesses of the tool.

Case Retrieval Algorithm Using Similarity Measure and Adaptive Fractional Brain Storm Optimization for Health Informaticians

Managing and utilizing health information is recently a challenging task for health informaticians to provide the highest quality healthcare delivery. Here, storage, retrieval, and interpretation of healthcare information are important phases in health informatics. Accordingly, the retrieval of similar cases based on the current patient data can help doctors to identify the similar kind of patients and their methods of treatments. By taking into consideration this as an objective of the work, a hybrid model is developed for retrieval of similar cases through the use of case-based reasoning. Here, a new measure called parametric-enabled similarity measure is proposed and a new optimization algorithm called adaptive fractional brain storm optimization by modifying the well-known brain storm optimization algorithm with inclusion of fractional calculus is proposed. For experimentation, six different patient datasets from UCI machine learning repository are used and the performance is compared with existing method using accuracy and F-measure. The average accuracy and F-measure reached by the proposed method with six different datasets are 89.6 and 88.8%, respectively.

The role of professional knowledge in case-based reasoning in practical ethics.

The use of case-based reasoning in teaching professional ethics has come of age. The fields of medicine, engineering, and business all have incorporated ethics case studies into leading textbooks and journal articles, as well as undergraduate and graduate professional ethics courses. The most recent guidelines from the National Institutes of Health recognize case studies and face-to-face discussion as best practices to be included in training programs for the Responsible Conduct of Research. While there is a general consensus that case studies play a central role in the teaching of professional ethics, there is still much to be learned regarding how professionals learn ethics using case-based reasoning. Cases take many forms, and there are a variety of ways to write them and use them in teaching. This paper reports the results of a study designed to investigate one of the issues in teaching case-based ethics: the role of one's professional knowledge in learning methods of moral reasoning. Using a novel assessment instrument, we compared case studies written and analyzed by three groups of students whom we classified as: (1) Experts in a research domain in bioengineering. (2) Novices in a research domain in bioengineering. (3) The non-research group--students using an engineering domain in which they were interested but had no in-depth knowledge. This study demonstrates that a student's level of understanding of a professional knowledge domain plays a significant role in learning moral reasoning skills.

Improving Accuracy of Early Stage Cost Estimation by Revising Categorical Variables in a Case-Based Reasoning Model

AbstractFor the overall success of a construction project, it is very important to accurately estimate the construction cost from the early stage. However, the limited information available in the early stage makes the cost estimation challenging. Recently, there has been an increase in the use of case-based reasoning (CBR) to estimate construction cost in the early stage. Based on the hypothesis that similar problems have similar solutions, CBR searches for the most similar cases (e.g., previous projects) for a given problem (e.g., a new project). However, because no previous project is exactly the same as a new project, the solutions applied to the past project may not work for the new project, especially when there are no sufficient cases stored in the case base. To overcome this limitation, some studies have highlighted the importance of revision algorithms to account for the deviation of the new project and the identified similar past projects. These studies, however, were limited in considering the ...

A review on fundamentals of case-based reasoning and its recent application in different domains

Case-based reasoning (CBR) means to understand and solve new (target) problems using past experiences. It solves target problems based on the solutions of similar past problems or by adapting solutions of past problems. It is a new branch of artificial intelligence (AI) approach to problem solving and learning that has attained huge attention over the last few years. This paper brings current state-of-art of working principle as well as its application domains with much other advancement together, which is really very rare in a single research article. This research article starts with about CBR and its basic working principle. The article states a brief-history of CBR, walks around its applications for a newly interested CBR reader or researcher, describes the methods involved in CBR with brief explanation and discusses the merits of CBR methodology and their differences from other related techniques and methodologies. Besides, the article digs out the prominent and essential further potential research challenges and issues in CBR. It also reports about the use of CBR as an integrated approach of reasoning and learning with other learning methods and makes critical assessment together with suggestion for further improvement.

Structured Language Requirement Elicitation Using Case Base Reasoning

Requirement elicitation is very difficult process in highly challenging and business based software as well as in real time software. Common problems associated with these types of software are rapidly changing the requirements and understanding the language of the layman person. In this study, a framework for requirement elicitation by using knowledge based system is proposed, which is very helpful for knowledge documentation, intelligent decision support, self-learning and more specifically it is very helpful for case based reasoning and explanation. Basically in this method requirements are gathered from Artificial Intelligence (AI) expert system from various sources e.g., via interviews, scenarios or use cases. Then, these are converted into structured natural language using ontology and this new problem/case is put forward to Case Based Reasoning (CBR). CBR based on its previous information having similar requirements combines with new case and suggests a proposed solution. Based on this solution a prototype is developed and delivered to customer. The use of case-based reasoning in requirements elicitation process has greatly reduced the burden and saved time of requirement analyst and results in an effective solution for handling complex or vague requirements during the elicitation process.