Case-based Reasoning Techniques Research Articles

A novel dynamic enterprise architecture model: leveraging MAPE-K loop and case-based reasoning for context awareness

Nowadays, enterprises are required to take the uncertainty of the environment as decisive factor of success. For this reason, Enterprises should be prepared up-stream to react dynamically to the turbulent context. Considering that enterprise architecture is a tool drawing a blueprint that gives a holistic view of the enterprise, this blueprint should be able to represent this awareness to context and implements the techniques and mechanisms to react in a dynamic manner depending on the triggers of change. In this paper, the proposed model stipulates a “context-awareness” that monitors the internal and external context, and then adapt its reaction in alignment with the prefixed goals. The operationalization of our conception is realized through the monitor-analyze-plan-execute-knowledge (MAPE-K) loop, the case-based reasoning and machine learning techniques organized and orchestrated through a global algorithm of 6 main functions to monitor, compare, analyze, plan, execute and enrich the knowledge base. The results are verified in the light of a case study that demonstrates the applicability of our proposed model.

An intelligent garment recommendation system based on case-based reasoning technology

Garment purchasing through the Internet has become an important trend for consumers. However, various garment e-shopping systems, systematically lack personalized recommendations, like sales advisors in classical shops, to propose the most relevant products to different consumers according to their consumer profiles and successful recommendation cases. In this paper, we propose a consumer-oriented recommendation system by Case-based reasoning techniques and Similarity degree of fuzzy sets, which can be used in a garment online shopping system like a virtual sales advisor. This system has been developed by integrating successful recommendation cases and taking into account consumer profiles. It can effectively help consumers to choose garments from the Internet. Compared with other prediction methods, the proposed method is more robust and interpretable owing to its capacity to treat uncertainty. This paper presents an original method for predicting one or several relevant product profiles from the similarity degree between a specific consumer profile and a successful cases database.

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

Review and analysis of existing methods of scheduling classes

One of the most important steps in organizing the quality of the educational process in educational institutions and the effectiveness of the use of scientific and pedagogical potential is the task of forming a high-quality lesson schedule. A qualitatively designed lesson schedule should ensure the uniform loading of student groups and teaching staff. The problem of university course planning (UCTP) is a specific problem of course planning at a university, with simultaneous use of resources such as students, faculty, and classrooms. These problems are considered non-polynomial time (NP) and Combinatorial Optimization (COP) problems, which means that they can be solved by optimization algorithms to obtain the desired table. Several methods have been used in universities to solve table problems, and most of them use optimization methods. This article discusses six methods for solving problems with the schedule of classes in universities: Sequential methods, сluster methods, сonstraint-based methods, meta-heuristic methods, generalized search, hybrid evolutionary algorithms, multi-criteria approaches, case-based reasoning techniques, hyper-heuristics, adaptive approaches. The purpose of this article is a comprehensive review of optimization approaches to solving the problems of scheduling in universities. In addition, when solving such problems, the accepted algorithms for Meta-heuristic optimization are shown. In addition, methods have been developed to analyze and measure the performance of a solution by specifying frequently used control data sets.

An optimized case-based software project effort estimation using genetic algorithm

Software development companies have long suffered from inaccurate estimation of their software projects. This in turn led to huge losses, especially in the financial resources available for the project as well as the time required to complete it. As a result of this, the research community has developed different methods for estimating effort in software projects in the hope of achieving high levels of accuracy and efficiency in the use of available resources. Among those methods that have proven to be accurate in estimating the effort of software projects is the use of machine learning (ML) techniques, especially the case-based reasoning technique (CBR). This technique is based on adapting previously successful solutions for similar software projects. However, the CBR technique suffers from a problem which is its multiple parameters that are difficult to be tuned. This justifies the importance of the adaptation and adjustment process as an essential part of CBR to produce accurate and efficient results with least absolute estimation error. In this paper, one of the most efficient multi-objective evolutionary techniques, the Genetic Algorithm (GA), are used to help find the best set of classical CBR parameters (feature selection, feature weighting, similarity measures, and k number of nearest neighbors) to produce the most accurate effort estimates for software projects. The proposed CBR-GA model showed the effectiveness of using the GA algorithm to search for the best combination of CBR parameters and thus improve its accuracy. This in turn is beneficial for project managers in the early financial planning phase for effort estimation and thus project cost control. To validate the proposed CBR-GA model, we used a set of public benchmark datasets available on PROMISE data repository, in addition we used a set of reliable evaluation metrics. The obtained results are promising in terms of accuracy and significance tests. This implies the importance of search-based techniques for tuning effort estimation methods.

Intelligent Case-based Hybrid Model for Process and Endpoint Prediction of Converter via Data Mining Technique

An intelligent case-based hybrid converter model has been established to predict the converter endpoint and process operations. The case-based reasoning (CBR) technique combined with the similarity calculation system was used in the intelligent converter model. The actual production data during the converter process in a time span from 2018 to 2020 was collected in the model database. The influencing factors during converter stage were divided into five categories, including steel composition, steel temperature, total processing time, steel cleanliness and total steelmaking cost of converter process in the model. All these factors were used in the similarity degree calculation, and then the historical cases in the model database were retrieved to guide the actual operation by analyzing its similarity degree to new case. The prediction accuracy was improved through self-learning with constant supplement of massive data. The prediction results based on 120-ton industrial converter showed a reasonable operation selection, the mean deviation of endpoint [C] content and [P] was 9.66% and 6.24%, respectively. Moreover, the difference between the predicted endpoint temperature and the measured endpoint temperature is within 10°C. Accordingly, the intelligent converter model was stable and suitable for large-scale prediction in converter endpoint and process operations.

Urban-GAN: An artificial intelligence-aided computation system for plural urban design

The current urban design computation is mostly centered on the professional designer while ignoring the plural dimension of urban design. In addition, available public participation computational tools focus mainly on information and idea sharing, leaving the public excluded in design generation because of their lack of design expertise. To address such an issue, this study develops Urban-GAN, a plural urban design computation system, to provide new technical support for design empowerment, allowing the public to generate their own designs. The sub-symbolic representation and artificial intelligence techniques of deep convolutional neural networks, case-based reasoning, and generative adversarial networks are used to acquire and embody design knowledge as the density function, and generate design schemes with this knowledge. The system consists of an urban form database and five process models through which the user with little design expertise can select urban form cases, generate designs similar to those cases, and make design decisions. The Urban-GAN is applied to hypothetical design experiments, which show that the user is able to apply the system to successfully generate distinctive designs following the urban form “styles” in Manhattan, Portland, and Shanghai. This study further extends the discussion about the plural urban design computation to general reflections on the goals and values in AI technique application in planning and design.

Dynamic Expansion of Behaviour Trees

Artificial intelligence in games is typically used for creating player's opponents. Manual edition of intelligent behaviors for Non-Player Characters (NPCs) of games is a cumbersome task that needs experienced designers. Our research aims to assist designers in this task. Behaviours typically use recurring patterns, so that experience and reuse are crucial aspects for behavior design. The use of hierarchical state machines allows working on different abstraction levels, sharing transitions and reusing pieces from the more detailed levels. However, the static nature of the design process does not release the designer from the burden to completely specify each behaviour. Our approach applies Case-Based Reasoning (CBR) techniques to retrieve and reuse stored behaviors represented as hierarchical state machines (actually, behaviour trees). In this paper we focus on dynamic retrieval of behaviours taking into account the world state and the underlying goals to select the most appropriate state machine to guide the NPC behaviour. The global behaviour of the NPC is dynamically built in run time querying the CBR system. We exemplify our approach through a serious game, developed by our research group, with gameplay elements from First-Person Shooter (FPS) games.

A New Cost-Quality Estimation Model Based on Case-Based Reasoning Technique

A New Cost-Quality Estimation Model Based on Case-Based Reasoning Technique

Zakat Management System with Allocation Prediction Using Case-Based Reasoning

Zakat has become one of the vital opportunity to be given to the poor and needy. However, there are problems faced by the institution of zakat with the inefficiency and inaccurate issue, especially in the zakat allocation and distribution aspects. Moreover, the zakat allocation and distribution process is time consuming due to the variety of the criteria to be considered, especially when it involves an educational institution. Since the problem usually originates from the organization of zakat itself, it is essential to minimize the difficulties so that zakat can be distributed in a proper way to the qualified person with a suitable allocation. Therefore, the purpose of this project is to develop a web-based Zakat Management and Allocation Prediction System using Case-based Reasoning(CBR) technique. The proposed method consists of two components: (1) Web-based zakat management system which aims to properly manage all related data of the zakat applicant, and (2) Zakat allocation module using CBR to suggests the allocation amount of zakat by finding the similarities between the previous cases and the new cases. For the prediction purposes, the significant main features are identified and suitable weightage is assigned to be able the CBR engine to produce a suggestion. Experimental results using real data collected from UiTM(Perak) Tapah Campus show that our proposed model achieves a significant improvement in the efficiency of managing and allocating the amount of zakat.

An Ontological Metro Accident Case Retrieval Using CBR and NLP

Metro accidents are apt to cause serious consequences, such as casualties or heavy economic loss. Once accidents occur, quick and accurate decision-making is essential to prevent emergent accidents from getting worse, which remains a challenge due to the lack of efficient knowledge representation and retrieval. In this research, an ontological method that integrates case-based reasoning (CBR) and natural language processing (NLP) techniques was proposed for metro accident case retrieval. An ontological model was developed to formalize the representation of metro accident knowledge, and then, the CBR aimed to retrieve similar past cases for supporting decision-making after the accident cases were annotated by the NLP technique. Rule-based reasoning (RBR), as a complementary of CBR, was used to decide the appropriate measures based on those that are recorded in regulations, such as emergency plans. A total of 120 metro accident cases were extracted from the safety monthly reports during metro operations and then built into the case library. The proposed method was tested in MyCBR and evaluated by expert reviews, which had an average precision of 91%.

A new approach for web usage mining using case based reasoning

In this study, we present a new approach for Web Usage Mining using Case Based Reasoning. Case-Based Reasoning techniques are a knowledge-based problem-solving approach which is based on the reuse of previous work experience. Thus, the past experience can be deemed as an efficient guide for solving new problems. Web personalization systems which have the capability to adapt the next set of visited pages to individual users according to their interests and navigational behaviors have been proposed. The proposed architecture consists of a number of components, namely, basic log preprocessing, pattern discovery methods (By Case Based Reasoning and peer to peer similarity—Clustering—association rules mining methods), and recommendations. One of the issues considered in this study is that there are no recommendations to those who are different from the existing users in the log file. Also, it is one of the challenges facing the recommendations systems. To deal with this problem, Apriori algorithm was designed individually in order to be utilized in presenting recommendations; in other words, in cases where recommendations may be inadequate, using association rules can enhance the overall system performance recommendations. A new method used in this study is clustering algorithms for Nominal web data. Our evaluations show that the proposed method along with Standard case-classified Log provides more effective recommendations for the users than the Logs with no case classification.

Knowledge Geometry in Phenomenon Perception and Artificial Intelligence

Artificial Intelligence (AI) pervades industry, entertainment, transportation, finance, and health. It seems to be in a kind of golden age, but today AI is based on the strength of techniques that bear little relation to the thought mechanism. Contemporary techniques of machine learning, deep learning and case-based reasoning seem to be occupied with delivering functional and optimized solutions, leaving aside the core reasons of why such solutions work. This paper, in turn, proposes a theoretical study of perception, a key issue for knowledge acquisition and intelligence construction. Its main concern is the formal representation of a perceived phenomenon by a casual observer and its relationship with machine intelligence. This work is based on recently proposed geometric theory, and represents an approach that is able to describe the inuence of scope, development paradigms, matching process and ground truth on phenomenon perception. As a result, it enumerates the perception variables and describes the implications for AI.

Fuzzy case‐based‐reasoning‐based imputation for incomplete data in software engineering repositories

AbstractMissing data is a serious issue in software engineering because it can lead to information loss and bias in data analysis. Several imputation techniques have been proposed to deal with both numerical and categorical missing data. However, most of those techniques used is simple reuse techniques originally designed for numerical data, which is a problem when the missing data are related to categorical attributes. This paper aims (a) to propose a new fuzzy case‐based reasoning (CBR) imputation technique designed for both numerical and categorical data and (b) to evaluate and compare the performance of the proposed technique with the k‐nearest neighbor (KNN) imputation technique in terms of error and accuracy under different missing data percentages and missingness mechanisms in four software engineering data sets. The results suggest that the proposed fuzzy CBR technique outperformed KNN in terms of imputation error and accuracy regardless of the missing data percentage, missingness mechanism, and data set used. Moreover, we found that the missingness mechanism has an important impact on the performance of both techniques. The results are encouraging in the sense that using an imputation technique designed for both categorical and numerical data is better than reusing methods originally designed for numerical data.

Fatigue residual life estimation of jib structure based on improved v‐SVR algorithm obtaining equivalent load spectrum

AbstractFatigue tests of truck crane jib structure have great difficulty, long testing cycle, and high cost. Therefore, with the portfolio strategy for “collection, prediction, measurement and simulation” being introduced, an improved support vector regression (v‐SVR) algorithm is proposed to predict the equivalent load spectrum based on the small measured load spectrum and the fatigue residual life evaluation model is built. First, the v‐SVR algorithm corrected in kernel function, decision function, and parameter optimization is utilized to acquire the equivalent load spectrum. Second, the distribution of critical points is determined by the case‐based reasoning technique. The simulation model of first main stress‐time histories for critical points is established, and the two‐dimensional stress spectrum is obtained by the rain‐flow counting method. Finally, the fatigue residual life of jib structure is estimated by the Forman formula. Taking ZLJ5551JQZ110V truck crane jib structure as an example, the effectiveness of the above method is validated.

STGA-CBR: A Case-Based Reasoning Method Based on Spatiotemporal Trajectory Similarity Assessment

Decision making are critical for disaster prevention and emergency response. To fully improve the effectiveness of emergency decisions by taking spatiotemporal information into consideration, this paper proposes a new method STGA-CBR for spatiotemporal case matching by using an integrated approach comprising a newly proposed spatiotemporal trajectory similarity measurement algorithm (Position-Frequency algorithm), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) similar spatiotemporal trajectory retrieval; (2) weight determination; and (3) attribute similarity calculation. The proposed approach was employed in typhoon disaster, which contains a variety of spatiotemporal information. The results of matching were validated by comparing STGA-CBR with ST-CBR, GA-CBR and traditional simple CBR. The experimental results proved that the proposed STGA-CBR effectively screens out similar spatiotemporal trajectories and demonstrates higher matching performance than there other methods, indicating the high efficiency of the proposed similar case retrieval approach. The case pair selected are then used for prediction of the post-disaster social and economic loss and the average accuracy of prediction results are calculated, among which the integrated model rank the highest, thus rendering our approach superior in comparison to other traditional methods.

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.

A Case-Based Reasoning Approach for the Cybersecurity Incident Recording and Resolution

Intelligent computing techniques have a paramount importance to the treatment of cybersecurity incidents. In such Artificial Intelligence (AI) context, while most of the algorithms explored in the cybersecurity domain aim to present solutions to intrusion detection problems, these algorithms seldom approach the correction procedures that are explored in the resolution of cybersecurity incident problems that already took place. In practice, knowledge regarding cybersecurity resolution data and procedures is being under-used in the development of intelligent cybersecurity systems, sometimes even lost and not used at all. In this context, this work proposes the Case-based Cybersecurity Incident Resolution System (CCIRS), a system that implements an approach to integrate case-based reasoning (CBR) techniques and the IODEF standard in order to retain concrete problem-solving experiences of cybersecurity incident resolution to be reused in the resolution of new incidents. Different types of experimental results so far obtained with the CCIRS show that information security knowledge can be retained with our approach in a reusable memory improving the resolution of new cybersecurity problems.

Solving the motion planning problem using learning experience through case-based reasoning and machine learning algorithms

Abstract This article presents two novel methodologies for solving the motion planning problem through retained experience. Both approaches employ AI’s case-based reasoning (CBR) technique. Case-based reasoning is an expert system development methodology which reuses past solutions to solve new problems. The first approach uses CBR to retain K similar cases to solve the motion planning problem by merging those solutions into a set. Afterwards, it picks from this set based on a heuristic function to assemble a final solution. Regarding the second approach, it employs the retained K similar cases differently. It uses those solution to build a graph which can be queried using traditional graph search algorithms. Results prove the success of such approaches concerning solution quality and success rate compared to different experience-based algorithms. Such utilization for CBR systems develops new research directions for building systems that can solve NP problems based on retained experiences exclusively.

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.