Case-based Reasoning Research Articles

Graded Mean Integration Representation and Intuitionistic Fuzzy Weighted Arithmetic Mean for Similarity Measures in Case-Based Reasoning

Graded Mean Integration Representation and Intuitionistic Fuzzy Weighted Arithmetic Mean for Similarity Measures in Case-Based Reasoning

Effects of explainable artificial intelligence in neurology decision support.

Artificial intelligence (AI)-based decision support systems (DSS) are utilized in medicine but underlying decision-making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how to design xAI for clinicians. Here we investigate the impact of various xAI techniques on a clinician's interaction with an AI-based DSS in decision-making tasks as compared to a general population. We conducted a randomized, blinded study in which members of the Child Neurology Society and American Academy of Neurology were compared to a general population. Participants received recommendations from a DSS via a random assignment of an xAI intervention (decision tree, crowd sourced agreement, case-based reasoning, probability scores, counterfactual reasoning, feature importance, templated language, and no explanations). Primary outcomes included test performance and perceived explainability, trust, and social competence of the DSS. Secondary outcomes included compliance, understandability, and agreement per question. We had 81 neurology participants with 284 in the general population. Decision trees were perceived as the more explainable by the medical versus general population (P < 0.01) and as more explainable than probability scores within the medical population (P < 0.001). Increasing neurology experience and perceived explainability degraded performance (P = 0.0214). Performance was not predicted by xAI method but by perceived explainability. xAI methods have different impacts on a medical versus general population; thus, xAI is not uniformly beneficial, and there is no one-size-fits-all approach. Further user-centered xAI research targeting clinicians and to develop personalized DSS for clinicians is needed.

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.

A Case-Based Reasoning (CBR) Internship Placement Model

A Case-Based Reasoning (CBR) Internship Placement Model

An improved case-based reasoning approach for sustainable rural development applied to strategic responses

Sustainable development has become a worldwide consensus in recent years. It has become increasingly important to study how to make effective management decisions in rural areas to achieve sustainable development. In this paper, we develop a new approach to strategic responses for sustainable rural development by using case-based reasoning (CBR), the bi-direction Choquet integral (BDCI), and the Shapley index (SI). First, to deal with the complexity of sustainable rural development, we develop an ontology-based sustainable rural development domain model. Second, we propose a case retrieval algorithm based on BDCI to improve the case retrieval method used in CBR to solve the problem of the relationship between the features. Third, we propose a case reuse method that is inspired by SI, in which each solution is evaluated relative to its value, and the implementation step for the response strategy is determined based on its priority. The proposed strategy response method for sustainable rural development is demonstrated through a case study and its effectiveness is demonstrated empirically.

Prediction of Coal Spontaneous Combustion Hazard Grades Based on Fuzzy Clustered Case-Based Reasoning

Accurate prediction of the coal spontaneous combustion hazard grades is of great significance to ensure the safe production of coal mines. However, traditional coal temperature prediction models have low accuracy and do not predict the coal spontaneous combustion hazard grades. In order to accurately predict coal spontaneous combustion hazard grades, a prediction model of coal spontaneous combustion based on principal component analysis (PCA), case-based reasoning (CBR), fuzzy clustering (FM), and the snake optimization (SO) algorithm was proposed in this manuscript. Firstly, based on the change rule of the concentration of signature gases in the process of coal warming, a new method of classifying the risk of spontaneous combustion of coal was established. Secondly, MeanRadius-SMOTE was adopted to balance the data structure. The weights of the prediction indicators were calculated through PCA to enhance the prediction precision of the CBR model. Then, by employing FM in the case base, the computational cost of CBR was reduced and its computational efficiency was improved. The SO algorithm was used to determine the hyperparameters in the PCA-FM-CBR model. In addition, multiple comparative experiments were conducted to verify the superiority of the model proposed in this manuscript. The results indicated that SO-PCA-FM-CBR possesses good prediction performance and also improves computational efficiency. Finally, the authors of this manuscript adopted the Random Balance Designs—Fourier Amplitude Sensitivity Test (RBD-FAST) to explain the output of the model and analyzed the global importance of input variables. The results demonstrated that CO is the most important variable affecting the coal spontaneous combustion hazard grades.

Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning

Explaining predictions of black-box neural networks is crucial when applied to decision-critical tasks. Thus, attribution maps are commonly used to identify important image regions, despite prior work showing that humans prefer explanations based on similar examples. To this end, ProtoPNet learns a set of class-representative feature vectors (prototypes) for case-based reasoning. During inference, similarities of latent features to prototypes are linearly classified to form predictions and attribution maps are provided to explain the similarity. In this work, we evaluate whether architectures for case-based reasoning fulfill established axioms required for faithful explanations using the example of ProtoPNet. We show that such architectures allow the extraction of faithful explanations. However, we prove that the attribution maps used to explain the similarities violate the axioms. We propose a new procedure to extract explanations for trained ProtoPNets, named ProtoPFaith. Conceptually, these explanations are Shapley values, calculated on the similarity scores of each prototype. They allow to faithfully answer which prototypes are present in an unseen image and quantify each pixel’s contribution to that presence, thereby complying with all axioms. The theoretical violations of ProtoPNet manifest in our experiments on three datasets (CUB-200-2011, Stanford Dogs, RSNA) and five architectures (ConvNet, ResNet, ResNet50, WideResNet50, ResNeXt50). Our experiments show a qualitative difference between the explanations given by ProtoPNet and ProtoPFaith. Additionally, we quantify the explanations with the Area Over the Perturbation Curve, on which ProtoPFaith outperforms ProtoPNet on all experiments by a factor &gt;10^3.

Study on the methodology of emergency decision-making for water transfer project contingencies: A case-based reasoning and regret theory approach.

To tackle the global water imbalance problem, a multitude of inter-basin water transfer projects have been built worldwide in recent decades. Nevertheless, given the complexity and safety challenges associated with project operation, effective emergency decision-making is crucial for addressing unforeseen incidents. Hence, this research has developed a two-stage emergency decision-making framework to tackle the uncertainty in the development trends of emergencies in inter-basin water transfer projects. (1) The first stage mainly utilizes case-based reasoning techniques to extract historical case information and disposal plans for inter-basin water transfer projects. Subsequently, a holistic similarity model is built by employing structural similarity and local attribute similarity algorithms to identify highly similar historical cases. (2) The second stage involves the optimization and adjustment of decision-making plans based on the dynamic evolution characteristics of emergencies. It utilizes the theory of decision-makers regret psychology and combines it with practical case studies to verify the scientific rationality of the method. This enables it to achieve effective multidimensional expression and rapid matching of scenarios, satisfying the decision-making requirements of "scenario response". Finally, this study compares the results obtained from this method with those computed using the traditional TOPSIS method and fuzzy comprehensive evaluation method, further validating its feasibility and effectiveness. In practice, this method can provide effective support for decision-makers work.

A two-stage case-based reasoning driven classification paradigm for financial distress prediction with missing and imbalanced data

Financial distress prediction often accompanies missing sample feature data and imbalanced normal and abnormal samples. To solve missing and imbalanced data that have significant negative impacts on the financial distress prediction model, a two-stage case-based reasoning (CBR)-driven classification paradigm is proposed to accurately and robustly predict financial distress. The proposed classification paradigm involves two main stages: CBR-driven missing data imputation and learning vector quantization (LVQ)-CBR-driven classifier prediction. In the first stage, the hybrid CBR-driven weighted imputation method is used to fill in missing values in the analytical dataset to obtain reliable and stable imputation performance, thereby solving the data missing problem. In the second stage, the LVQ-CBR-driven classification model is constructed to predict financial distress. By highlighting and fully learning minority abnormal samples, the classification model solves the low prediction accuracy of minority abnormal samples arising from data imbalance. For illustration and verification, some experiments are performed on seven Chinese-listed enterprise datasets with different missing and imbalance rates. Corresponding results show that the proposed two-stage CBR-driven classification paradigm can achieve the best imputation performance, greatly improve the prediction accuracy of minority abnormal samples, and integrally realize the best overall prediction performance compared with other imputation methods, imbalanced data processing methods, and their combinations. This implies that the proposed two-stage CBR-driven classification paradigm can be used as a competitive solution to financial distress prediction with missing and imbalanced data.

Intelligent green retrofitting of existing buildings based on case-based reasoning and random forest

The decision-making on green retrofitting of existing buildings relies on both explicit and implicit knowledge, and its efficiency and reliability need improvement. Intelligent approaches that can sufficiently utilize the text information of existing projects are required to provide more suitable strategies for green retrofitting. This paper describes a decision-making approach combining Case-Based Reasoning (CBR) and Random Forest (RF), which can identify similar cases from the database containing 109 green retrofit projects and revise outdated measures. A practical project case study shows that the revised retrofit measures can reduce Energy Use Intensity (EUI) by 37%. The proposed approach optimizes and standardizes CBR processes and provides guidance for coping with semi-structured green retrofit decision-making problems, thereby promoting sustainable development and intelligent management in the construction field. The system prototype will be developed and promoted after the case database is expanded.

Experiential-sensorial adaptive system model for hospitality based on 360° VR videos and case-based reasoning.

In this work, an adaptive software architecture is proposed for the generation of experiences for hotel promotion and marketing, based on Case-based Reasoning (CBR) that uses the attributes and user characteristics and immersive 360° videos. Considering that immersion in virtual reality (VR) environments can trigger responses in various dimensions, such as affective, cognitive, attitudinal, and behavioral dimensions, these dimensions are evaluated in immersive environments with 360° videos. To validate the results obtained with the software architecture, a quasi-experimental study was conducted through the evaluation of the experience, consisting in the visualization of the environments of a boutique hotel, with a sample of a randomly selected group of young people. The contribution of this work lies in the use of 360° VR videos, for the visualization of the hotel characteristics and environments according the user profiles, to evaluate the affective, cognitive and attitudinal and behavioral responses and their influence on the booking intention and attitude. Finally, conclusions and recommendations for future work have been established.

Comparison Of The Results Of The Jaccard Similarity And KNearest Neighbor Algorithms Using The Case Based Reasoning (CBR) Method On An Expert System For Diagnosing Pediatric Diseases

Health ranks highest in supporting the continuity of every human activity, especially children. The availability of a doctor is still relatively lacking, especially in remote areas. This makes people have difficulty in diagnosing certain diseases so that medical treatment becomes too late and can even be fatal for the patient. So it is necessary to create a system that has the ability to be able to diagnose diseases in children like an expert. The method used in this study is Case Based Reasoning (CBR) with the Jaccard Similarity Algorithm and K-Nearest Neighbor. Jaccard Similarity is one way to calculate the similarity of two objects (items) which are binary. Similarity calculations are used to generate values whether or not there is a similarity between new cases and existing cases in the case base. While the K-Nearest Neighbor (KNN) Algorithm belongs to the instance-based learning group. The KNN algorithm allows the program to find old cases that are most similar to the current case. Based on the test results using 50 sample data, the expert system can provide diagnostic results in accordance with expert diagnoses. The accuracy results for the K-Nearest Neighbor Algorithm are 72% while the accuracy results for the Jaccard Similarity Algorithm are 70%.

Research on Case Reasoning Multi-attribute Group Decision-making Method Based on Hesitant Fuzzy Set

In this study, a hesitant fuzzy set-based case-based reasoning integration method is proposed for the multi-attribute group decision-making problem with unknown attribute weights and mixed forms of attribute values. First, from two perspectives, traditional distance measure and information theory, a multi-objective optimization model is constructed using the distance similarity measure and information entropy of each type of attributes to determine the attribute weights. Secondly, considering the hybrid and nonlinear characteristics of case data, based on the principle of symmetric interaction entropy and TOPSIS method, a global similarity measure based on symmetric interaction entropy is proposed and a case inference algorithm suitable for hesitant fuzzy environment is designed. Finally, by analyzing the arithmetic cases of the target case in the case base, the most similar historical cases to the target case are retrieved to determine the decision-making scheme, and the practicality and feasibility of the decision-making method are verified. The results show that considering hesitant fuzzy theory for case-based reasoning research will help improve the accuracy and reliability of decision-making and provide more effective support for multi-attribute group decision management.

How to select plan in emergency decision-making? A two-stage method with case-based reasoning and prospect theory

Emergency events characterized by high uncertainty and complexity bring tremendous pressure and challenges to our society. Emergency decision-making (EDM) is an effective way to mitigate the losses caused by emergency events. The generation of alternatives and the selection of the best emergency plan are crucial to the successful management of an emergency event. To improve the efficiency of EDM, this study proposes a novel two-stage EDM method. In the first stage, to fully represent emergency events and measure their similarities, we adopt heterogeneous multi-attribute information in the dynamic case-based reasoning (CBR) model to generate alternatives. After that we provide an adaptive reaching process model with a dynamic interactive strategy to obtain similar historical emergency events. In the second stage, we apply the prosper theory and the linguistic term information to select the optimal emergency plan. To test the robustness of the two stage EDM methodology, we verify the feasibility and effectiveness of the method with a case study about evaluating emergency plans for public health emergencies. Lastly, we conclude this work with more sensitivity analysis and some discussions about future research.

A case-based reasoning strategy of integrating case-level and covariate-level reasoning to automatically select covariates for spatial prediction

ABSTRACT Spatial prediction is essential for obtaining the spatial distribution of geographic variables and selecting appropriate covariates for this process can be challenging, especially for non-expert users. For easing the burden of selecting the appropriate covariates, two case-based reasoning strategies, namely the most-similar-case and covariate-classification strategies, have been proposed for automated covariate selection. The former may suggest nonessential covariates due to its case-level reasoning way. And the latter with covariate-level reasoning may overlook related covariates and recommend fewer covariates than the case-level reasoning. In this study, we propose a new strategy of integrating case-level and covariate-level reasoning to effectively leverage the strengths of both previous strategies while also addressing their limitations. The proposed strategy is validated through a case study of automatically selecting covariates for digital soil mapping under reasoning with a case base containing 189 cases. The leave-one-out evaluation demonstrated that our proposed strategy outperformed the previous two strategies.

A User-friendly Cloud-based Multi-agent Information System for Smart Energy-saving

&lt;p&gt;The study focused on leveraging artificial intelligence (AI) for efficient energy conservation in scientific applications. The proposed cloud-based multi-agent system merges various intelligent technologies to swiftly gather high-quality cloud data for effective smart energy-saving. Incorporating case-based reasoning (CBR), big data analysis, and intelligent user interfaces as key functionalities, the system utilized Web services, ontology, open data, and data mining. It expanded on the practical advancements of the multi-agent Dr. What-Info system for information collection. A Web services platform seamlessly gathers cloud interactions among subagents processing energy-saving data. Rigorous performance and operational experiments were conducted to demonstrate the efficiency and effectiveness of the system interface, offering detailed insights into relevant R&amp;D technologies and outcomes.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Implementasi Metode Case Base Reasioning dan SUPR-Q Untuk Sistem Diagnosis Kerusakan Lokomotif CC 201

The rapid growth of information and communication technology has also reached the transportation sector, especially in Indonesia. Railway locomotives have experienced significant advancements by transitioning from conventional systems to the use of microcontroller-based sensors. However, to expedite repairs, swift identification of faults is necessary. Railway workers need to continuously improve the quality of human resources and develop application systems to facilitate technicians in identifying issues with CC 201 locomotives. The aim of this research is to build a system that can identify faults and facilitate technicians in diagnosing and providing repair solutions for CC 201 locomotives, using the Case-Based Reasoning method. Research results indicate that this system can be used to diagnose CC 201 locomotive issues based on observed symptoms. Usability testing for 20 respondents using the SUPR-Q method shows an excellent level of system ease and satisfaction, with a score of 90.86%.

Data Mining from Knowledge Cases of COVID-19

A lot of articles were produced during the pandemic of COVID-19 and continue to be produced. The article proposes a system for diagnosis of COVID-19 disease. Also nowadays, the presentation of knowledge and the research for the reasoning algorithms are progressively improving in the domain of Artificial Intelligence. Besides these, distributed reasoning as a part of data mining has become a solution for the increasing everyday data amount. As a result, the paper proposes a case-based non-monotonic reasoner for uncertain and vague COVID-19 information that is appropriate for work with Big Data. Also, a COVID-19 knowledge base model is proposed. The reasoner implements rules for the distribution of the information that gives the possibility to work with Big data. The proposed reasoning algorithm is applied for COVID-19. It shows the implementation of the reasoner into the data mining system and the returned results from the system are evaluated. The results show that the system returns relatively high results concerning the other system for recommendation.

Classification algorithm for land use in the giant panda habitat of Jiajinshan based on spatial case-based reasoning

Jiajin Mountain, where the giant pandas reside, is an essential nature reserve in China. To comprehend the land use classification of the habitat, this article proposes a remote sensing interpretation algorithm based on spatial case reasoning, known as spatial case-based reasoning (SCBR). The algorithm incorporates specific spatial factors into its framework and does not require an extensive amount of domain knowledge and eliminates the need for a complex model training process, making it capable of completing land use classification in the study area. SCBR comprises a spatial case expression model and a spatial case similarity reasoning model. The paper conducted comparative experiments between the proposed algorithm and support vector machine (SVM), U-Net, vision transformer (ViT), and Trans-Unet, and the results demonstrate that spatial case-based reasoning produces superior classification outcomes. The land use classification experiment based on spatial case-based reasoning at the Jiajinshan giant panda habitat produced satisfactory experimental results. In the comparative experiments, the overall accuracy of SCBR classification reached 95%, and the Kappa coefficient reached 90%. The paper further analyzed the changes in land use classification from 2018 to 2022, and the average accuracy consistently exceeds 80%. We discovered that the ecological environment in the region where the giant pandas reside has experienced significant improvement, particularly in forest protection and restoration. This study provides a theoretical basis for the ecological environment protection of the area.

(Re) active materials for sensitive, adaptive and interactive design. Practices for a (re) generation of sense for well-being and comfort

The article aims to read the transition of material design approaches identifying an evolutive models with major principles and contribution into the health improvement through material innovations. The analysis follows a bottom-up approach, using a case-based reasoning methodology to study well-being concept evolution and identify new material experimentations frontiers within three approaches: Imitative, Augmentative, and Mutational. The contribution of the article reflects and suggests the introduction of new material design approaches to manage the design of advanced materials’ behaviuors in their future development, highlighting their potential impact on user satisfaction and acceptance, collective welfare improvement, performance optimization, and environmental concerns.