Application Of Case-based Reasoning Research Articles

Application of case-based reasoning in hazard evaluation in complex process flow control

Application of case-based reasoning in hazard evaluation in complex process flow control

Application of Case-Based Reasoning in Product Packaging Design System

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Cutting Tools Assignment and Control Using Neutrosophic Case-Based Reasoning and Best Worst Method

Cutting tools management is one of the major issues in metal cutting operations. Most of the problems in cutting tools management were mostly addressed using optimization, heuristic, and simulation techniques. This important problem was not studied using decision-based approaches. This study proposed a decision support system (DSS) that can perform part-cutting tools assignment and control decisions by integrating a neutrosophic case-based reasoning and the best-worst method (BWM) in metal cutting processes. Specifically, this study utilized the integration of case-based reasoning (CBR) and single-valued neutrosophic set (SVNS) theories in artificial intelligence (AI). Furthermore, the proposed DSS applies the BWM to determine optimal weights for case attributes from multicriteria decision-making (MCDM). The system retrieves the most similar historical cases using a neutrosophic CBR and the BWM to adapt their cutting tool requirements to the current product orders. In addition, it revises retrieved cases (tool sets) depending on attribute differences between new and retrieved cases using rule-based reasoning (RBR) from experts. This study provided new insights regarding the application of a neutrosophic CBR and its integration with the BWM. Specifically, the integration of SVNS, CBR, and BWM was not articulated in cutting tools management problems. A numerical example was illustrated in a computer-simulated environment to show the applicability of the proposed DSS using lathe machine operations.

A supervised case-based reasoning approach for explainable thyroid nodule diagnosis

As an explainable experience-based artificial intelligence technique, case-based reasoning (CBR) has been widely used to help diagnose many diseases, but the application of CBR in the diagnosis of thyroid nodules (TDNs) is rarely studied. To fill this research gap, this paper proposes a supervised CBR approach to help diagnose TDNs. The proposed approach first investigates the correlation between the feature diagnoses of historical TDN cases and the corresponding overall diagnoses using the canonical correlation analysis technique. Then the learned canonical variables are used to reconstruct TDN cases. Based on the reconstructed historical case base, a classifier is constructed to provide pathological diagnosis predictions for new TDN cases. To explain these predictions with similar historical TDN cases, a convex optimization model is constructed to determine the similarity between historical TDN cases and new TDN cases. Finally, a weighted combination scheme is designed to generate an explainable pathological diagnosis for each new TDN case based on its similar historical TDN cases. The proposed approach not only avoids the burdensome parameter tuning task but also reduces the likelihood of retrieving noisy historical cases as similar cases of new cases with a supervised case retrieval process. Using a real diagnostic dataset collected from the ultrasound department of a local hospital, the effectiveness of the proposed approach in diagnosing TDNs is validated and its advantages are further highlighted by comparison with the traditional CBR approach and six mainstream machine learning models.

CEBRA: A CasE-Based Reasoning Application to recommend banking products

Following data ethics and respecting the clients’ privacy, the banking environment can use the client data that is available to them to offer personalized services to its clients. Intelligent recommender systems can support this attempt through specialized technological architectures. This article proposes the inclusion of CEBRA (CasE-Based Reasoning Application), a case-based reasoning system oriented to commercial banking, in a Fog Computing architecture coordinated by virtual agents. Throughout this article, the model of this architecture is presented and its life cycle is described, and improvements are proposed through the incorporation of several techniques in the retrieve and reuse phases, including the extraction of interests expressed by users on their social network profiles and collaborative filtering systems. A comprehensive case study has been carried out and a dataset of 60,000 cases has been generated to evaluate CEBRA. As a result, the Recommender System is presented, by including, the recommendation algorithm and a REST interface for its use. The recommendations are based on the user’s profile, previous ratings and/or additional knowledge such as the user’s contextual information. The proposal takes advantage of contextual information to support the promotion of banking and financial products, improving user satisfaction.

Case-based reasoning for safety assessment of critical software

The commissioning of a new guided or automated rail transport system requires an in-depth analysis of all the methods, techniques, procedures, regulations and safety standards to ensure that the risk level of the future system does not present any danger likely to jeopardize the safety of travelers. Among these numerous safety methods implemented to guarantee safety at the system, automation, hardware and software level, there is a method called “Software Errors and Effects Analysis (SEEA)” whose objective is to determine the nature and the severity of the consequences of software failures, to propose measures to detect errors and finally to improve the robustness of the software. In order to strengthen and rationalize this SEEA method, we have agreed to use machine learning techniques and in particular Case-Based Reasoning (CBR) in order to assist the certification experts in their difficult task of assessing completeness and the consistency of safety of critical software equipment. The main objective consists, from a set of data in the form of accident scenarios or incidents experienced on rail transport systems (experience feedback), to exploit by automatic learning this mass of data to stimulate the imagination of certification experts and assist them in their crucial task of researching scenarios of potential accidents not taken into account during the design phase of new critical software. The originality of the tool developed lies not only in its ability to model, capitalize, sustain and disseminate SEEA expertise, but it represents the first research on the application of CBR to SEEA. In fact, in the field of rail transport, there are currently no software tools for assisting SEEAs based on machine learning techniques and in particular based on CBR.

Scenario-Based Marine Oil Spill Emergency Response Using Hybrid Deep Reinforcement Learning and Case-Based Reasoning

Case-based reasoning (CBR) systems often provide a basis for decision makers to make management decisions in disaster prevention and emergency response. For decades, many CBR systems have been implemented by using expert knowledge schemes to build indexes for case identification from a case library of situations and to explore the relations among cases. However, a knowledge elicitation bottleneck occurs for many knowledge-based CBR applications because expert reasoning is difficult to precisely explain. To solve these problems, this paper proposes a method using only knowledge to recognize marine oil spill cases. The proposed method combines deep reinforcement learning (DRL) with strategy selection to determine emergency responses for marine oil spill accidents by quantification of the marine oil spill scenario as the reward for the DRL agent. These accidents are described by scenarios and are considered the state inputs in the hybrid DRL/CBR framework. The challenges and opportunities of the proposed method are discussed considering different scenarios and the intentions of decision makers. This approach may be helpful in terms of developing hybrid DRL/CBR-based tools for marine oil spill emergency response.

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).

Vulnerability Identification on GNU/Linux Operating Systems through Case-Based Reasoning

Operating system security has been steadily evolving over the years. Several mechanisms, softwares and guides of best practices of configuration have been developed to contribute with the security of such systems. The process that makes an operating system safer by considering the default level obtained at the installation is known as hardening. Experience and technical knowledge are important attributes for the professional performing this process. In this context, automated rule-based tools are often used to assist professionals with little experience in vulnerability identification activities. However, the use of rules establishes a dependency on developers for the development of new rules as well as to keep them updated. Failure to update rules can significantly compromise the integrity of vulnerability identification results. In this paper, the Case-Based Reasoning (CBR) technique is used to improve tools that assist inexperienced professionals in conducting vulnerability identification activities. The purpose of using CBR is to make inexperienced professionals obtain similar results as experienced professionals. In addition, the dependence on rule developers is diminished. A prototype was developed considering the GNU/Linux system in order to carry out an experimental evaluation. This evaluation demonstrated that the application of CBR improves the performance of inexperienced professionals in terms of the number of identified vulnerabilities.

On Application of Case-Based Reasoning to Personalise Learning

On Application of Case-Based Reasoning to Personalise Learning

Injection molding manufacturing process: review of case-based reasoning applications

Although manufacturing technology has been developing rapidly, injection molding is still widely used for fabricating plastic parts with complex geometries and precise dimensions. Since the occurrence of faults in injection molding is inevitable, process optimization is desirable. Artificial intelligence (AI) methods are being successfully used for optimization in different branches of science and technology. In this paper, we review the application of one such method, case-based reasoning (CBR), to injection molding. CBR is an AI approach for knowledge representation and manipulation which considers successful solutions of past problems that are likely to serve as candidate solutions for a given problem. This method is being used increasingly in academic and industrial applications. Here, we review CBR systems that are used in injection molding for different purposes, such as process design, processing parameters, fault diagnose, and enhancement of quality control. In addition, we discuss trends for utilization of CBR in different phases of injection molding. The most significant challenges associated with application of CBR to injection molding are also discussed. Finally, the review is concluded by contemplating on some open research areas and future prospects.

Penyelesaian Masalah Pengelolaan Lumbung Pangan Desa Menggunakan Case-Based Reasoning dengan Algoritma K-Nearest Neighbor

In this paper, we discuss the problem solving of village food barn management using Case-Based Reasoning (CBR) with the K-Nearest Neighbor algorithm. This research was carried out by adopting the stages of the CBR cycle and the nearest neighbor algorithm. The results of the study show that the application of CBR and K-nearest neighbor algorithms can support the resolution of knowledge problems in village food barn management using technical problem solving based on the symptoms and solutions to existing problems. Based on the test results, the problem-solving accuracy was 92%.Keywords - case-based reasoning, K-nearest neighbor, food barn, problem-solving

Application of case-based reasoning in a fault detection system on production of drippers

Injection molding has been a preferred production process in the fabrication of complex components. In this technique not only the injection machine and mold play important roles, but also different process parameters have strong effects on the quality of the final products. The production process might be stopped because of different types of faults on the production line. In this paper, a case-based reasoning (CBR) methodology is employed to implement an intelligent fault detection system for the production of injection molded drippers. This CBR system utilizes similar occurred faults to solve particular new problems. Case retrieval and similarity measurements are defined based on fault occurrence weight of features (fault’s causes). Application and accuracy of the proposed system are experimentally tested and validated through analyzing the current case study. The obtained results indicated that the implemented CBR system is able to detect the faults on the injection molding machine. By utilizing the proposed system machine downtime is reduced, speeded production with high productivity is achieved.

Developing a Case-Based Retrieval System for Supporting IT Customers

The case-based reasoning (CBR) approach is a modern approach. It is adopted for designing knowledge based expertise systems. The aforementioned approach depends much on the stored experiences. These experiences serve as cases that can be employed for solving new problems. That is done through retrieving similar cases from the system and utilizing their solutions. The latter approach aims at solving problems through reviewing, processing and applying their experiences. The present study aimed to shed a light on a CBR application. That is done to develop a new system for assisting Internet users and solving the problems faced by those users. In addition, the present study focuses on the cases retrieval stage. It aimed at designing and building an experienced inquiry system for solving any problem that internet users might face when using a case-based reasoning (CBR) dialogue. That shall enable internet users to solve the problems faced when using the Internet. The system that was developed by the researcher operates through displaying the similar cases through a dialogue. That is done through using a well-developed algorithm and reviewing the relevant previous studies. It was found that the success rate of the proposed system is high.

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization

ABSTRACTImproving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.

Developing a Case-Based Retrieval System for Supporting IT Customers

The case-based reasoning (CBR) approach is a modern approach. It is adopted for designing knowledge based expertise systems. The aforementioned approach depends much on the stored experiences. These experiences serve as cases that can be employed for solving new problems. That is done through retrieving similar cases from the system and utilizing their solutions. The latter approach aims at solving problems through reviewing, processing and applying their experiences. The present study aimed to shed a light on a CBR application. That is done to develop a new system for assisting Internet users and solving the problems faced by those users. In addition, the present study focuses on the cases retrieval stage. It aimed at designing and building an experienced inquiry system for solving any problem that internet users might face when using a case-based reasoning (CBR) dialogue. That shall enable internet users to solve the problems faced when using the Internet. The system that was developed by the researcher operates through displaying the similar cases through a dialogue. That is done through using a well-developed algorithm and reviewing the relevant previous studies. It was found that the success rate of the proposed system is high.

Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy management

This paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users.

The application of case-based reasoning in construction management research: An overview

Case-based reasoning (CBR) has been widely adopted in construction management (CM) research due to the similar mind-sets of CBR and CM problem solving. However, to date, there has been no systematic review of CBR applications in previous CM studies, raising the question of what is the current status quo of CBR applications in CM research? By using the method of content analysis, this study provides a comprehensive literature review of CM CBR articles published between 1996 and January 2015. It is found that the popularity of CBR applications in CM research is increasing, especially after 2006, with a majority emanating from South Korea. In addition, 17 CBR application fields are identified, with the primary research interests focusing on construction cost estimation, construction tendering, bidding and procurement, and environment and sustainability management. Issues in previous CBR model developments are also identified, mainly related to model hypothesis-testing, reuse of case outcomes, selection of model development methods, use of derivational analogy and automated implementation, together with future research suggestions and directions. This study helps provide CM academics and practitioners with a more comprehensive understanding of the development of CBR applications and implications for future studies.

Application of case-based reasoning for machining parameters selection

Process planning, as one of the most important stage of the technological production preparation, consists in selection of manufacturing operations taking into account the minimal manufacturing cost. The minimal manufacturing cost could be achieved by selection of the best sequence of manufacturing operations, machine tools, manufacturing tools, and accompanying machining parameters selection. On the other hand, it is almost impossible, especially in industrial conditions, to design an optimal process plan, first of all due to restrictions imposed by the installed in the factory machine park. Taking into consideration above, machining parameter selection seems to be one of the potential areas of optimization. In manual process planning process engineers select machining parameters using selection rules and data stored in manuals and tool catalogues. It makes this process time and labour consuming and non-error free. On the other hand, in workshop practice, machine operators select parameters having their skills and habits in mind. It could be a reason for suboptimal process planning. Considering this, new methods of machining parameters selection free of human factor influence are still sought. In our approach, we propose to apply case-based reasoning for machining parameter selection. In the paper, a detailed description of our approach is presented.

Decision Making Process for B2C Model Using Behavior Analysis with Big Data Technologies

Objectives: Business to Consumer (B2C) E-Commerce activities are developed with a large number through agent-based systems. Case Based Reasoning (CBR) has been applied in these systems by analyzing the consumer buying behavior to provide consumers, a support to the decision making process. Analysis: Current applications of CBR to E-Commerce are limited to fixed, unchangeable products. To make the environment support for configurable products, an interactive operator based customization approach from CBR can be applied. Findings: In this work, to make the process more reliable and efficient, real time data from provisional stores has been taken and the system is trained to predict the consumer buying behavior along with CBR to pave way for a consumer to make a better decision making process. Applications/ Improvements: This work also applies big data concepts in predicting the behavior of the consumers. It thereby also led the customers to mine about their preferences in purchasing necessary products.