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This paper describes the possibilities of variable selection in large-scale industrial systems. It introduces knowledge-based, data-based and model-based methods for this purpose. As an example, Case-Based Reasoning application for the evaluation of the web break sensitivity in a paper machine is introduced. The application uses Linguistic Equations approach and basic Fuzzy Logic. The indicator combines the information of on-line measurements with expert knowledge and provides a continuous indication of the break sensitivity. The web break sensitivity defines the current operating situation at the paper mill and gives new information to the operators. Together with information of the most important variables this prediction gives operators enough time to react to the changing operating situation. [ABSTRACT FROM AUTHOR]

Patients experiencing homelessness visit the emergency department (ED) often and have worse clinical outcomes. Caring for this patient population is complex, challenging, and resource‐intensive. Emergency medicine (EM) education is lacking in formal curricula on the topic of homelessness, despite benefits for resident morale and patient care. Our goals were to identify a gap in EM education and training of the intersection of housing and health and propose educational topics and teaching methods to be included in residency curricula. Methodology was based on the development of a didactic session at the 2021 SAEM Annual Meeting. A needs assessment was performed through a review of medical education literature, a national survey of EM residency curricula, the individual curricula utilized by respective team members, and perspective from the team's own individual experiences with teaching about homelessness. Topics presented were chosen through discussion between the authors and determined to be common and relevant and cover a broad spectrum of content. The four presented topics included the intersection of COVID‐19 and housing, the impact of LGBTQIA+ status on homelessness, housing status related to health system utilization and health outcomes, and housing inequity as a means of perpetuating structural racism. Suggestions for education of these topics included case‐based learning, journal clubs, simulation, collaboration with social work, quality improvement projects, and engagement with community leaders. The ED is uniquely positioned to encounter the impacts of homelessness on health. Emergency physicians should be prepared to effectively care for these patients with complex social needs. Structured learning on this topic would benefit EM resident growth and lead to better patient care through improved screening, recognition of risk factors, and use of social resources. [ABSTRACT FROM AUTHOR]

Background: Paediatric orthopaedics is a significant and difficult for undergraduate students to master. During the COVID-19 pandemic, we used the WeChat platform to combine the advantages offered by problem-based learning (PBL), case-based learning (CBL) and paper review teaching methods to establish a new blended online teaching model and demonstrated its feasibility and effectiveness. Objective: This study aims to demonstrate the feasibility and effectiveness of a new blended pedagogical method that uses the WeChat platform and combines PBL, CBL and paper review. Methods: We enrolled 22 students participating in the Department of Paediatric Orthopaedics. They participated in the WeChat blended pedagogy mode. Their departmental rotation examination scores were compared with those of 23 students who participated in the traditional teaching method. Moreover, an anonymous questionnaire was used to evaluate students' perceptions and experiences. Results: The total average scores of students who participated in the WeChat blended pedagogy mode and the traditional teaching method were 47.27 and 44.52, respectively. There were no statistically significant differences between the online teaching mode and the traditional teaching method in terms of possessing professional accomplishment, gaining knowledge and promoting interpersonal skills (P = 0.07, P = 0.12 and P = 0.65, respectively). In terms of independent clinical thinking, self-improving capability and improving clinical skills, the scores associated with the WeChat blended pedagogy mode were 8.00, 8.00 and 6.00, whereas those associated with the traditional teaching method were 6.70, 6.87 and 7.48. The overall satisfaction with the WeChat blended pedagogy mode reached 100%. A total of 64%, 86%, 68%, 64% and 59% of students chose very large or large in response to the items concerning professional accomplishment, knowledge absorption, independent clinical thinking skills, English reading and literature exploring capacity, as well as interpersonal skills, respectively. Fifteen participants claimed that the WeChat blended pedagogy mode was less helpful to them with regard to promoting the improvement of their clinical skills. Nine students claimed that the WeChat blended pedagogy mode was time-consuming. Conclusions: Our study verified the feasibility and effectiveness of the WeChat blended pedagogy mode for undergraduate paediatric orthopaedics internships. Trial Registration: Retrospectively registered. [ABSTRACT FROM AUTHOR]

Critical thinking is a common and important learning outcome in college curricula. Case-based and problem-based learning can be used to assess and foster critical thinking skills. HNF 250–Contemporary Issues in Human Nutrition is a critical thinking course developed during the redesign of a nutritional sciences major program. Course assignments were designed to assess the course and nutritional sciences major learning outcomes. The nutrition and health claim assignment is scaffolded across the academic semester as three assignments: 1) bibliography assignment; 2) poster presentation; and 3) paper. Course lectures and materials have been designed to prepare students for completion of each assignment. The assignments have been modified over time based on classroom observations and student performance. In 2021, the course learning outcomes were examined by assessing several assignments including the nutrition and health claim poster and paper. Course learning outcome benchmarks using these assessments generally included 80% of students achieving an 80% for each criterion. Results revealed that students were not meeting most of these assessment benchmarks during the 2021 iteration, although benchmark data from other course assessments were more satisfactory. It is possible that the transition from a virtual to an in-person format negatively influenced student performance on these course learning outcomes. This course and the nutrition and health claim assignment example can provide a course design and learning outcome assessment framework for other higher education critical thinking courses. NEW & NOTEWORTHY: This paper describes how a scaffolded nutrition and health claim assignment is used to teach critical thinking skills among nutritional sciences students and examine the program's learning outcomes. Further, this course example is to serve as an model for STEM majors on how to incorporate case-based and problem-based learning strategies into an undergraduate course. Listen to this article's corresponding podcast at https://apspublicationspodcast.podbean.com/e/teaching-critical-thinking-in-nutritional-sciences/. [ABSTRACT FROM AUTHOR]

Pulp properties are key factors to assessing the performance of a refining process, evaluating operational conditions, and optimizing the process of stock preparation. This paper presents a data-driven approach to estimate the beating degree and wet weight of pulp after refining using case-based reasoning (CBR). Historical data generated in a refining process at a paper mill was used to evaluate the proposed model. The root mean square error (RMSE) and coefficient of variance of the root mean square error (CVRMSE) of the beating degree estimation results in CBR were 1.30 and 4.32%, respectively, and the RMSE and CV-RMSE of the wet weight were 0.50 and 19.09%, respectively. The results of beating degree prediction were satisfactory, and the results of wet weight were also acceptable. To test the performance of CBR model, support vector machine algorithm (SVM) were employed to verify the effectiveness and accuracy. The RMSE and CV-RMSE of the beating degree estimation results in SVM were 1.20 and 4.02%, respectively, and the RMSE and CV-RMSE of the wet weight were 0.44 and 16.73%, respectively. As a result, the proposed model was as accurate as the SVM method. [ABSTRACT FROM AUTHOR]

Vernacular architecture offers valuable technological and typological solutions rooted in knowledge systems that connect environmental, social, economic, and cultural contexts. This paper introduces the Heritage for People Platform, a collaborative digital tool developed under the VerSus+ project (Creative Europe Program), aimed at organising and disseminating vernacular architectural knowledge with a focus on sustainable practices that are both culturally and environmentally conscious. The platform builds upon lessons learned from similar projects, offering key improvements such as the adoption of a Case-Based Reasoning methodology, which organises examples by geographic location, materials, and intervention types. A structured classification system based on sustainability principles and strategies enables cross-disciplinary research and comparison. The design process was highly participatory, incorporating user feedback at every stage to ensure the platform is accessible to a wide range of users, including designers, scholars, craftspeople, and the general public. A cross-device, user-centered approach further broadens its accessibility. The platform provides a public, web-based geospatial repository where users can create, update, and explore a dynamic collection of artefacts, design models, people, and institutions from across the globe. This tool marks a significant advance in promoting and preserving both physical and intangible heritage, with strong educational and practical applications for sustainable architecture. The paper details the theoretical framework and real-world potential of the platform as a critical resource for promoting sustainability in the built environment. [ABSTRACT FROM AUTHOR]

Those with intellectual and developmental disabilities (I/DD), especially autism, represent an often misunderstood and underserved population. In order to address health disparities faced by those with I/DD in Washington state, the Extension for Community Healthcare Outcomes (Project ECHO) model was implemented to build capacity to serve these individuals and their families in their home communities. Through this year-long telementorship and learning community, expert teams lead clinics based on interdisciplinary case-based discussion and knowledge sharing with medical, behavioral, and mental healthcare providers. A cornerstone of these expert teams are autistic self-advocates, who provide insight into lived experience. In this paper, we will discuss how the inclusion of six autistic self-advocates across four different ECHO programs has allowed ECHO participants to broaden their horizons and gain new insight into supports for their I/DD clients across multiple aspects of care. In addition to the unique knowledge provided by self-advocates, their participation illustrates the potential for all people with I/DD to live rich and fulfilling lives. The initial implementation and on-going success of including lived experience within the ECHO model can be used as an example of how to create partnerships that meaningfully inform decisions and improve equitable outcomes in service provision. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To provide a cost-saving innovative audience response system (ARS) that permits typing texts and compare its efficiency to the paper-based method in case-based learning (CBL). MATERIALS AND METHODS: Orthodontic clinical cases were presented to 149 undergraduate dental students for discussion among teammates. Responses were collected using ARS-based and paper-based. ARS was constructed from an online survey platform (Google forms) then QR code was created for easy and fast access. Students used their cellphones to scan code, view questions, discuss, type, and submit answers within 10 minutes. Feedbacks were collected using a feedback survey. Outcome measures included the number of words, spelling mistakes, time required by the instructor to read submissions, and activity time compliance. RESULTS: The average number of words submitted by females in ARS-based 47 ± 8 was significantly higher than 35 ± 16 paper-based, and male ARS-based 36 ± 18 (P < 0.05). ARS-based submissions required significantly less time to read compared to paper-based among whole group, females, males (P < 0.001, P < 0.05, P < 0.001), respectively. Spelling mistakes were lower 1 ± 1 in ARS-based compared to paper-based 2 ± 2. The ARS-based first submission was (-4.28) minutes before the deadline, while paper-based last submission was (+2.19) minutes after (P < 0.05). ARS-based submissions were 12.5 seconds faster to read than paper-based (P < 0.001). Out of 56.4% of respondents, 63.1% preferred using ARS-based and 80% agreed that it provided immediate feedback, with high overall satisfaction. CONCLUSIONS: This innovated ARS was found to facilitate CBL. It is superior in time and cost-saving to paper-based and other ARSs. It could be useful in distance learning especially during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The authors suggest to trade-off diagnosis quality versus runtime performance, exploiting learning for constraint ordering and where matrix factorization is used to estimate diagnoses. The final manuscript, "Applying matrix factorization to consistency-based direct diagnosis", authored by Seda Polat Erdeniz et al., deals with diagnosis as a basis for configuration problems when efficient solving is required in real-world applications. Screening all the accepted papers, the top 7 authors of the accepted papers were encouraged to submit their papers for this special issue, asking for substantial extensions to their submissions for creating manuscripts that are appropriate for being published in Applied Intelligence. [Extracted from the article]

Background: Clinical decision-making skills are essential for providing high-quality patient care. To enhance these skills, many institutions worldwide use case-based learning (CBL) as an educational strategy of pre-clinical training. However, to date, the influence of different learning modalities on students' clinical decision-making processes has not been fully explored. This study aims to explore the influence of video and paper case modalities on the clinical decision-making process of midwifery students during CBL. Methods: CBL involving a normal pregnant woman was provided for 45 midwifery students. They were divided into 12 groups; six groups received the video modality, and six groups received the paper modality. Group discussions were video-recorded, and focus groups were conducted after the CBL. Transcripts of the group discussions were analysed in terms of their interaction patterns, and focus groups were thematically analysed based on the three-stage model of clinical decision-making, which includes cue acquisition, interpretation, and evaluation/decision-making. Results: The students in the video groups paid more attention to psychosocial than biomedical aspects and discussed tailored care for the woman and her family members. They refrained from vaginal examinations and electric fetal heart monitoring. Conversely, the students in the paper groups paid more attention to biomedical than psychosocial aspects and discussed when to perform vaginal examinations and electric fetal heart monitoring. Conclusion: This study clarified that video and paper case modalities have different influences on learners' clinical decision-making processes. Video case learning encourages midwifery students to have a woman- and family-centred holistic perspective of labour and birth care, which leads to careful consideration of the psychosocial aspects. Paper case learning encourages midwifery students to have a healthcare provider-centred biomedical perspective of labour and childbirth care, which leads to thorough biomedical assessment. [ABSTRACT FROM AUTHOR]

Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence (AI). In this article, the development of CBR is reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were designed to solve problems by CBR are summarized. Then, the application fields of CBR are summarized. Finally, according to the idea of deep learning and interpretable AI, the main challenges for the future development of CBR are proposed. [ABSTRACT FROM AUTHOR]

In order to avoid missing the best opportunity for emergency rescue in the event of spontaneous combustion and to prevent accidents from developing further, this paper proposed a method to generate an emergency response plan for spontaneous combustion based on case-based reasoning (CBR). Firstly, we adopted Hadoop big data retrieval technology to establish a case library for spontaneous combustion; then, our team applied CBR technology and introduced the differential determination symbol to calculate case similarity; furthermore, we quickly generated an emergency response plan for spontaneous combustion with the help of the Python program; and finally, we used a case to verify the effectiveness of the method. Overall, the results showed that the emergency response plan generated using this proposed method was consistent with the actual situation of the accident case and, compared with other relevant representative algorithms, the results in this paper were more accurate. In practice, this method may be helpful in providing support for generating emergency response plans for spontaneous combustion. [ABSTRACT FROM AUTHOR]

Wildfires are a significant problem in Irkutsk Oblast. They are caused by climate change, thunderstorms, and human factors. In this study, we use the Random Forest machine learning method to map the wildfire susceptibility of Irkutsk Oblast based on data from remote sensing, meteorology, government forestry authorities, and emergency situations. The main contributions of the paper are the following: an improved domain model that describes information about weather conditions, vegetation type, and infrastructure of the region in the context of the possible risk of wildfires; a database of wildfires in Irkutsk Oblast from 2017 to 2020; the results of an analysis of factors that cause wildfires and risk assessment based on Random Forest in the form of fire hazard mapping. In this paper, we collected and visualized data on wildfires and factors influencing their occurrence: meteorological, topographic, characteristics of vegetation, and human activity (social factors). Data sets describing two classes, "fire" and "no fire", were generated. We introduced a classification according to which the probability of a wildfire in each specific cell of the territory can be determined and a wildfire risk map built. The use of the Random Forest method allowed us to achieve the following risk assessment accuracy indicators: accuracy—0.89, F1-score—0.88, and AUC—0.96. The comparison of the results with earlier ones obtained using case-based reasoning revealed that the application of the case-based approach can be considered the initial stage for deeper investigations with the use of Random Forest for more accurate forecasting. [ABSTRACT FROM AUTHOR]

Expert systems are computer programs that use knowledge and reasoning to solve problems typically solved by human experts. Expert systems have been used in medicine to diagnose diseases, recommend treatments, and plan surgeries. Interpretability of the results in medical applications is crucial since the decision that will be taken based on the system's output has a direct effect on people's health and lives which makes expert systems ideal choices when dealing with these applications in contrast to other machine learning approaches. An expert system has the ability to explain its own line of reasoning providing a robust way of diagnosis. This paper presents two types of expert systems for medical diagnosis. The first system is a case-based reasoning system using a database of previously diagnosed cases to diagnose a new case. The second system is a rule-based expert system that uses a set of if–then rules extracted from a decision tree classifier to make diagnoses. In this paper, machine learning-based similarity functions are proposed and compared with other traditional similarity functions. The results of this study suggest that expert systems can be a valuable tool for medical diagnosis. The two systems presented in this paper achieved competitive results, and they provide diagnoses similar to those made by human experts. [ABSTRACT FROM AUTHOR]

Systems thinking is crucial for understanding and solving complex problems and is considered an important thinking skill in engineering. Active learning is considered an effective approach for fostering STEM students' systems thinking. However, viable methods for teaching and assessing systems thinking with active learning across STEM disciplines, particularly in first-year undergraduate education, are still under-researched. In this paper, we introduce a research-based framework named System Architecture-Function-Outcome to help first-year STEM instructors both foster and assess students' introductory systems thinking. To conduct an initial evaluation of the framework's suitability in active learning settings, we designed a directed case-based learning assignment with an adapted article and a rubric for assessing 'introductory systems thinking', as defined in the framework. We deployed the assignment among 84 first-year STEM students and successfully tested its inter-rater reliability, with 75–100% inter-rater agreement across all assessment criteria. We discuss the implications of our results on fostering and assessing first-year STEM students' systems thinking, and outline examples for potential applications of the framework, pending further validation, in case-based learning settings of varying degrees of learner autonomy, from lecture-based to problem-based learning. [ABSTRACT FROM AUTHOR]

The grinding process of transmission gear with worm grinding wheel usually relies on the manual parameter setting by experienced engineers, but the reliability and validity are quite hard to control due to many grinding factors that affect the surface quality (e.g. structure, size, and materials of gear, heat treatment process, surface accuracy requirements). Therefore, it is necessary to set up a decision system for assisting engineers with designing the process plan efficiently and reasonably. A modified model of the revision method based on the radial basis function (RBF) neural network and case-based reasoning (CBR) considering evaluation index was proposed in this paper. A similar case retrieval mainly relies on the soft likelihood function considering a compound evaluation index model, where the score value of each new case execution test is converted into the corresponding index to achieve effective case storage. Process solutions for similar cases that perform better will be recommended from the case database by means of CBR. These candidates can be revised systematically based on a self-learning model. A modified RBF neural network trained by the existing cases will apply the learned experience to revise solution in part or in whole, which utilizes a self-filtering attribute method based on the correlation coefficient matrix so as to gain the primary attributes. In the case of the grinding gear with worm grinding wheel, the applicability of this technology was demonstrated. A decision-making system applying this method was developed by using.Net framework4.0 and SQL Server. Consequently, the technique can quickly generate a feasible process plan for specific gear. [ABSTRACT FROM AUTHOR]

Through data mining technology, the hidden information behind a large amount of data is discovered, which can help various management services and provide scientific basis for leadership decision-making. It is an important subject of current police information research. This paper conducts in-depth research on the investigation analysis and decision-making of public security cases and proposes a case-based reasoning model based on two case databases. Moreover, this paper discusses in detail the use of data mining technology to automatically establish a case database, which is a useful exploration and practice for the public security department to establish a new and efficient case investigation auxiliary decision-making system. In addition, this paper studies the method of using data mining technology to assist in the establishment of a case database, analyzes the characteristics of traditional case storage methods, and constructs a case investigation model based on artificial intelligence data processing. The research results show that the model constructed in this paper has certain practical effects. [ABSTRACT FROM AUTHOR]

The article discusses papers published within the issue including the use of case-based reasoning for workflow reparation, digital preservation of cultural property, and the evolution of robust database.

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. [ABSTRACT FROM AUTHOR]

Global warming has seriously affected the local climate characteristics of cities, resulting in the frequent occurrence of urban waterlogging with severe economic losses and casualties. Aiming to improve the effectiveness of disaster emergency management, we propose a novel emergency decision model embedding similarity algorithms of heterogeneous multi-attribute based on case-based reasoning. First, this paper establishes a multi-dimensional attribute system of urban waterlogging catastrophes cases based on the Wuli-Shili-Renli theory. Due to the heterogeneity of attributes of waterlogging cases, different algorithms to measure the attribute similarity are designed for crisp symbols, crisp numbers, interval numbers, fuzzy linguistic variables, and hesitant fuzzy linguistic term sets. Then, this paper combines the best-worst method with the maximal deviation method for a more reasonable weight allocation of attributes. Finally, the hybrid similarity between the historical and the target cases is obtained by aggregating attribute similarities via the weighted method. According to the given threshold value, a similar historical case set is built whose emergency measures are used to provide the reference for the target case. Additionally, a case of urban waterlogging emergency is conducted to demonstrate the applicability and effectiveness of the proposed model, which exploits historical experiences and retrieves the optimal scheme for the current disaster emergency with heterogeneous multi attributes. Consequently, the proposed model solves the problem of diverse data types to satisfy the needs of case presentation and retrieval. Compared with the existing model, it can better realize the multi-dimensional expression and fast matching of the cases. [ABSTRACT FROM AUTHOR]

Improvements in the emergency response efficiency and management level of power construction sites are conducive to reducing the construction safety risk of power infrastructure projects and then achieving the sustainability of construction site safety. Therefore, this paper proposes an emergency decision-making method for electric power personal accidents, which applies ontology and case-based reasoning to electric power emergency decision making. Firstly, ontology technology is used to structurally represent power accident case knowledge and clarify concepts and their relationships. Then, a power accident knowledge ontology hierarchy is designed, and a powerful personal accident case library is established. Secondly, by calculating cases' conceptual similarity, attribute similarity, and structural similarity, a global power accident case similarity calculation method is proposed, and case matching is performed based on the calculation results to achieve case knowledge retrieval and reuse. Finally, the results of the example-based study show that the method effectively achieves the accurate retrieval of electric power accident cases, improves the efficiency of the emergency decision response to electric power construction site accidents, and then provides support for emergency decision making for electric power construction site accidents. [ABSTRACT FROM AUTHOR]

To enable mobile robots to effectively deal with the emergency of visual contamination, contingency planning based on case-based reasoning (CBR) was performed in this paper. First, for a wheeled mobile robot (WMR) equipped with a chameleon-inspired visual system, a target search model in chameleon-inspired binocular negative correlation movement (CIBNCM) mode was established. Second, a CBR-based contingency planning model of visual contamination for WMRs was established, where the reasoning process using CBR for visual contamination was analyzed in detail. Third, through the analysis of environment perception when visual contamination occurs, a perception model in chameleon-inspired visual contamination for WMRs was built. Finally, to validate the proposed approach, a contingency planning experiment scheme for visual contamination was designed based on the robot's general planning of target tracking, and the experimental result is discussed. The proposed CBR-based contingency planning approach for visual contamination can reason out effective solutions corresponding to the contamination situations. The rationality of the approach was verified by experiments with satisfactory results. Moreover, compared with the contingency planning method based on rule-based reasoning, the accuracy of target retracking after the robot visual system is contaminated is significantly higher for the CBR-based contingent planning method used in this paper. [ABSTRACT FROM AUTHOR]

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset. [ABSTRACT FROM AUTHOR]

In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal algorithm. Furthermore, the scarcity of sufficient training data, denoted by dimensions representing the number of datasets and the feature space described by meta-feature perspectives, adds further complexity to the process of algorithm selection using classical machine learning methods. This research paper presents an integrated framework called eML-CBR that combines edge edge-ML and case-based reasoning methodologies to accurately address the algorithm selection problem. It adapts a multi-level, multi-view case-based reasoning methodology, considering data from diverse feature dimensions and the algorithms from multiple performance aspects, that distributes computations to both cloud edges and centralized nodes. On the edge, the first-level reasoning employs machine learning methods to recommend a family of classification algorithms, while at the second level, it recommends a list of the top-k algorithms within that family. This list is further refined by an algorithm conflict resolver module. The eML-CBR framework offers a suite of contributions, including integrated algorithm selection, multi-view meta-feature extraction, innovative performance criteria, improved algorithm recommendation, data scarcity mitigation through incremental learning, and an open-source CBR module, reshaping research paradigms. The CBR module, trained on 100 datasets and tested with 52 datasets using 9 decision tree algorithms, achieved an accuracy of 94% for correct classifier recommendations within the top k=3 algorithms, making it highly suitable for practical classification applications. [ABSTRACT FROM AUTHOR]

Phishing attacks are increasing every year, both in terms of number and technique. Using only human weaknesses, an attacker can easily obtain the victim's credentials or access their network. The problem persists despite many approaches offered by researchers, due to its dynamic nature, in which new phishing tactics are created every time. We, therefore, need more robust and effective methods to detect phishing emails. In this paper, we aim to detect phishing emails using the body text of the email with the hybrid approach combining case-based reasoning (CBR) and a deep learning model. Our proposed model, called DL-CBR, consists of a Bidirectional Long Short-Term Memory (Bi-LSTM) + Temporal Convolutional Network (TCN) network with an attention mechanism followed by a CBR classifier. The deep learning model is used for email representation, where it is trained using the N -pair loss function. To demonstrate the performance of DL-CBR, evaluation metrics, such as precision, accuracy, recall, and F-measure, were used, where we obtained an accuracy of 98.28%. The results show that our model outperformed other CBRs that utilize classical text representations like TF-IDF and Bag-of-Words. Additionally, while our model's performance is slightly below that of the state-of-the-art models, it offers several advantages inherent to CBR. For instance, it can learn from new cases and update their database accordingly. [ABSTRACT FROM AUTHOR]

Forest fire is one of the serious threats to the population and infrastructure of Irkutsk Oblast because its territory is heavily forested. This paper discusses the main stages of solving the problem of forecasting the risk of forest fires via a case-based approach, including data preprocessing, formation of a case model, and creation of a prototype of a case-based expert system. The main contributions of the paper are the following: a case model that provides a compact representation of information about weather conditions, vegetation type, and infrastructure of the region in relation to the possible risk of a wildfire; a case-base containing information about wildfires in Irkutsk Oblast for the period from 2017 to 2020; and a methodology for creating prototypes of case bases providing the transformation of decision tables of a special type. The approbation of the approach was carried out for separate forest districts, namely Bodaibinsk and Kazachinsk-Lena. The accuracy score was used for the evaluation of the results of forecasting the risk of wildfires. The average score value reached 0.51. The evaluation results revealed that application of the case-based approach can be considered as the initial stage for deeper investigations with the use of different methods (data mining, neural networks) for more accurate forecasting. [ABSTRACT FROM AUTHOR]

The paper aims to present application of Educational Data Mining and particularly Case-Based Reasoning (CBR) for students profiling and further to design a personalised intelligent learning system. The main aim here is to develop a recommender system which should help the learners to create learning units (scenarios) that are the most suitable for them. First of all, systematic literature review on application of CBR and its possible implementation to personalise learning was performed in the paper. After that, methodology on CBR application to personalise learning is presented where learning styles play a dominate role as key factor in proposed personalised intelligent learning system model based on students profiling and personalised learning process model. The algorithm (the sequence of steps) to implement this model is also presented in the paper. [ABSTRACT FROM AUTHOR]

The article presents a calendar of events in different countries for September 2006. The "ANTS 2006 Fifth International Workshop on Ant Colony Optimization and Swarm Intelligence" will be held on September 4-7 in Brussels, Belgium. The "SMPS 2006 The Third International Conference on Soft Methods in Probability and Statistics" will be held in Bristol, England on September 5-7. Fethiye, Turkey will be hosting the "ECCBR 2006 International Workshop on Case-based Reasoning and Context Awareness" on September 5.

As a part of the food industry, the dairy industry is one of the most important sectors of the process industry, keeping in mind the number of employees in that sector, the share in the total industrial production, and the overall value added. Many strategies have been developed over time to satisfy customer needs and assess customer satisfaction. This paper proposes an innovative model based on adaptive neuro-fuzzy inference system (ANFIS) and elements of the ACSI (American customer satisfaction index) for assessing and monitoring the level of customer satisfaction in a dairy manufacturing company where there are no large seasonal variations. In terms of an innovative approach, the base of fuzzy logic rules is determined by applying the fuzzy Delphi technique for the application of the ANFIS algorithm and assessment of customer satisfaction. The verification of the model is delivered by testing a real sample from a company of the dairy industry. As decisions on the strategic company level may be impacted by customer satisfaction, the company management should choose the most precise methodology for customer satisfaction assessment. The results are compared with other methods in terms of mean absolute deviation (MAD), mean squared error (MSE), and mean absolute percentage error (MAPE). Results show that ANFIS outperformed other methods used for assessing the level of customer satisfaction, such as case-based reasoning and multiple linear regression. [ABSTRACT FROM AUTHOR]

Case-based planning is an approach to planning where previous planning experience provides guidance to solving new problems. Such a guidance can be extremely useful, or even necessary, when the new problem is very hard to solve, or the stored previous experience is highly valuable, because, e.g., it was provided or validated by human experts, and the system should try to reuse it as much as possible. To do so, a case-based planning system stores in a library previous planning experience in the form of already encountered problems and their solutions. The quality of such a plan library critically inuences the performance of the planner, and therefore it needs to be carefully designed and created. For this reason, it is also important to update the library during the lifetime of the system, as the type of problems being addressed may evolve or differ from the ones the library was originally designed for. Moreover, like in general case-based reasoning, the library needs to be maintained at a manageable size, otherwise the computational cost of querying it grows excessively, making the entire approach ineffective. In this paper, we formally define the problem of maintaining a library of cases, discuss which criteria should drive the maintenance, study the computational complexity of the maintenance problem, and propose offline techniques to reduce an oversized library that optimize different criteria. Moreover, we introduce a complementary online approach that attempts to limit the growth of the library, and we consider the combination of offline and online techniques to ensure the best performance of the case-based planner. Finally, we experimentally show the practical effectiveness of the offline and online methods for reducing the library. [ABSTRACT FROM AUTHOR]

A significant amount of research has been conducted on the segmentation of large-scale 3D point clouds. However, efficient point cloud feature identification from segmentation results is an essential capability for computer vision and surveying tasks. Feature description methods are algorithms that convert the point set of the point cloud feature into vectors or matrices that can be used for identification. While the point feature histogram (PFH) is an efficient descriptor method, it does not work well with objects that have smooth surfaces, such as planar, spherical, or cylindrical objects. This paper proposes a 3D point cloud feature identification method based on an improved PFH descriptor with a feature-level normal that can efficiently distinguish objects with smooth surfaces. Firstly, a feature-level normal is established, and then the relationship between each point's normal and feature-level normal is calculated. Finally, the unknown feature is identified by comparing the similarity of the type-labeled feature and the unknown feature. The proposed method obtains an overall identification accuracy ranging from 71.9% to 81.9% for the identification of street lamps, trees, and buildings. [ABSTRACT FROM AUTHOR]

As main welded components of the rail vehicle, the railcar frames have specific needs in preparing a suitable welding document for each welded joint to guide the welding process during the manufacturing process. In this paper, the expert system and database technology are introduced into the field of rail vehicle welding process preparation, and a knowledge-based software for railcar frame welding process is developed. This system could maintain the normal operation of the enterprise rail welding system based on knowledge base and database. The numerous paper-based and fragmented electronic documents are stored in a unified format within the system's database, and a reasoning mechanism based on Rule-Based Reasoning (RBR) and Case-Based Reasoning (CBR) is designed to intelligently match the welding procedure specification (WPS) and welding procedure qualification report (WPQR) online. Furthermore, the system supports multiple departments and personnel by integrating the function of workflow to carry out the WPS prepare and review process. Thousands of welded joints with various structures and complex positions in the same vehicle body can be aggregated into a welding summary table to reduce the repeated welding procedure qualifications (WPQ) of the same or similar welding joint. This method not only saves manpower and resources, but also ensures the accuracy of these process documents. In the end, the future development trend of this kind of application software is prospected. [ABSTRACT FROM AUTHOR]

The geopolitical axis of the 21st century is indicative of a new world order that reflects power transitions as a vehicle for accelerated conflict. However, precedent rests not with historical analysis but with global changes towards political polarizing agendas. This article refutes the proposition that power shifts, such as those which caused so much conflict in the twentieth century, will be a key driver of conflict in the twenty-first century. Accordingly, the case is put forward that the fundamental ontological, and therefore epistemological, conditions of the international system have changed so radically in the last hundred years that the kinds of outcomes witnessed in the previous century, and previous system, are extremely improbable in the current day. For a case study, this paper looks at the clash between Western and Islamic civilisation, paying close attention to the rise in Islamism generally, Islamic terror groups specifically, and the confluences - in civilisational terms - between these two phenomena. In conclusion, the paper asserts that much evidence is available to support the contention that a clash between these civilisations will be a potent driver of conflict in the immediate future. [ABSTRACT FROM AUTHOR]

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper's factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models. [ABSTRACT FROM AUTHOR]

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.

In the process design and reuse of marine component products, there are a lot of heterogeneous models, causing the problem that the process knowledge and process design experience contained in them are difficult to express and reuse. Therefore, a process knowledge representation model for ship heterogeneous model is proposed in this paper. Firstly, the multi-element process knowledge graph is constructed, and the heterogeneous ship model is described in a unified way. Then, the multi-strategy ontology mapping method is applied, and the semantic expression between the process knowledge graph and the entity model is realized. Finally, by obtaining implicit semantics based on case-based reasoning and checking the similarity of the matching results, the case knowledge reuse is achieved, to achieve rapid design of the process. This method provides reliable technical support for the design of ship component assembly and welding process, greatly shortens the design cycle, and improves the working efficiency. In addition, taking the double-deck bottom segment of a ship as an example, the process knowledge map of the heterogeneous model is constructed to realize the rapid design of ship process, which shows that the method can effectively acquire the process knowledge in the design case and improve the efficiency and intelligence of knowledge reuse in the process design of the heterogeneous model of a ship. [ABSTRACT FROM AUTHOR]

(1) Background: Personalized product customization is an important direction in the development of the chair industry. This paper studies an intelligent design method for the rapid realization of personalized office chair customization; (2) Methods: based on the case-based reasoning (CBR) method, the characteristic attributes of office chair patterns are analyzed, and an attribute model is established. According to office chair data and customer demand, an intelligent design model using multi-layer weighted k-nearest neighbor (K-NN) for chair patterns is developed using the entropy weight method and an analytic hierarchy process. In addition, an example is employed for verification of the K-NN and multi-layer weighted K-NN retrieval models; (3) Results: both models are able to effectively retrieve chair type cases that meet the target requirements from the office chair pattern base; the case matching similarity of the multi-layer weighted K-NN retrieval model was higher, with an average increase of about 3.9%, and the chair pattern case results obtained by setting different customer needs are different, indicating that the case can be selected according to different customer preferences, which is more conducive to personalized product customization design; (4) Conclusions: The multi-layer weighted K-NN model for intelligent chair pattern design proposed in this paper is more conducive to personalized product customization design. [ABSTRACT FROM AUTHOR]

Knowledge-based recommender systems have always had their privileged place among all Decision Support Systems (DSS), given their advantage on several points over other techniques. Our paper presents a framework implementing a hybrid form of Rule-Based Reasoning and Case-Based Reasoning (RBR-CBR), to address the rarely discussed domain of educational planning. The system has been tested and presented outstanding results with a high accuracy, which will benefit educational planners' decision support. We have also developed a dedicated application for this project to visualize the results obtained. [ABSTRACT FROM AUTHOR]

Conventional approaches to business-to-business (B2B) negotiation use primary negotiation terms (PNTs) such as price or order quantity for modeling and analysis, but pay little attention to such secondary negotiation terms (SNTs) as resource availability and corporate culture. This paper argues that SNTs also contribute to good negotiation decisions because PNTs and SNTs are closely interlinked in the form of causal relationships. Moreover, B2B negotiation demands a practical and useful framework that can reuse past negotiation knowledge and perform ‘what-if’ analysis. This paper proposes a framework that consists of formalization, reuse, and problem-solving phases. The framework first formalizes TAKBN (tacit knowledge about B2B negotiation) with both PNTs and SNTs using a cognitive map and case-based reasoning, then stores them in case bases as cases that can be retrieved for later use and problem solving. This framework provides a platform with which decision makers can study past B2B negotiation cases, apply them to current B2B negotiation problems, and simulate different negotiation situations before making decisions. The framework has been tested using two practical scenarios. A structured, 13-item questionnaire was rigorously developed and applied to evaluate the validity of the proposed framework based on 16 B2B negotiation experts' judgments. Statistical tests proved that the proposed framework could improve decision performance significantly in B2B negotiations. [ABSTRACT FROM AUTHOR]

This paper presents a case-based reasoning (CBR) method in combination with ontology theory to carry out decision making for assembly sequence planning (ASP). Due to the ontology unifying various kinds of assembly sequence-related knowledge from different sources, the CBR approach enables a unified structured representation of previous cases and target cases to achieve integration and sharing of knowledge. Based on the similarity measure of classes and properties in ontology theory, the similarity calculation between target ASP case and previous ASP cases is carried out by considering the connection type, motion-transmission type, and location-support type, and a similarity-based previous cases retrieval algorithm is proposed. The combination of ontology and CBR enables flexible and high-quality assembly sequence decisions under various conditions; the ontology-based rule-based reasoning (RBR) method is also adopted as a supplement to CBR in the assembly sequence construction process. Additionally, a reducer case is used to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The subject of damaged stability has greatly profited from the development of new tools and techniques in recent history. Specifically, the increased computational power and the probabilistic approach have transformed the subject, increasing accuracy and fidelity, hence allowing for a universal application and the inclusion of the most probable scenarios. Currently, all ships are evaluated for their stability and are expected to survive the dangers they will most likely face. However, further advancements in simulations have made it possible to further increase the fidelity and accuracy of simulated casualties. Multiple time domain and, to a lesser extent, Computational Fluid dynamics (CFD) solutions have been suggested as the next "evolutionary" step for damage stability. However, while those techniques are demonstrably more accurate, the computational power to utilize them for the task of probabilistic evaluation is not there yet. In this paper, the authors present a novel approach that aims to serve as a stopgap measure for introducing the time domain simulations in the existing framework. Specifically, the methodology presented serves the purpose of a fast decision support tool which is able to provide information regarding the ongoing casualty utilizing prior knowledge gained from simulations. This work was needed and developed for the purposes of the EU-funded project SafePASS. [ABSTRACT FROM AUTHOR]

Precast components are a critical part of the life cycle of prefabricated construction. Component joints and connections are essential to structural stability, posing rework problems. The design of precast component joints requires tacit and explicit knowledge. Existing joint design approaches and construction requirements underuse tacit knowledge. This paper proposes a case-based reasoning (CBR) model to support the design of component joints based on this tacit knowledge. A case library of 299 valid precast slabs was used to develop the model. The test cases in the model were used to verify the methodology. Records for 64 test cases with the same building information and 64 test cases from different buildings were used to validate the usability and effectiveness of the model. The results show that the accuracy rates are 100% and 94% when considering the comprehensive performance, and the model is more effective for different buildings than for the same building. The proposed model can improve the efficiency of joint design without expert participation and improve the standardization and coordination of design and construction. [ABSTRACT FROM AUTHOR]

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

As a significant part of sustainable urban development proposed by the United Nations, urban planning is related to the ecological environment and transportation, especially affecting quality of life and social well-being. In the process of urban planning, the public express their opinions on open network platforms, resulting in large quantities of network public opinion data, which has important implications for evaluating urban planning. Based on the idea of geographical case-based reasoning (CBR), this paper constructs an expression framework for urban planning cases in the form of a 'case problem–case attribute–case result' triad. On this basis, this paper proposes a similarity calculation method of urban planning cases that integrates case attribute. Finally, based on an improvement to the traditional k-nearest neighbors method, the proposed public opinion feature calculation model considers similarity weights, which allow us to predict network public opinion features, including viewpoint-level emotional tendency and concerned groups of urban planning cases. The experimental result shows similarity weights (SWs) model could effectively improve the prediction accuracy, where the average MIC-F1 score reached more than 74%. Based on CBR, the proposed method can predict the development trends of public opinion in future planning cases, and provide scientific and reasonable decision support for urban planning. [ABSTRACT FROM AUTHOR]

Featured Application: Exemplar-based approach for students' learning style diagnosis described in this paper may be applied in virtual learning environments; automatically predicted learning style may be used for personalizing virtual learning environments. A lot of computational models recently are undergoing rapid development. However, there is a conceptual and analytical gap in understanding the driving forces behind them. This paper focuses on the integration between computer science and social science (namely, education) for strengthening the visibility, recognition, and understanding the problems of simulation and modelling in social (educational) decision processes. The objective of the paper covers topics and streams on social-behavioural modelling and computational intelligence applications in education. To obtain the benefits of real, factual data for modeling student learning styles, this paper investigates exemplar-based approaches and possibilities to combine them with case-based reasoning methods for automatically predicting student learning styles in virtual learning environments. A comparative analysis of approaches combining exemplar-based modelling and case-based reasoning leads to the choice of the Bayesian Case model for diagnosing a student's learning style based on the data about the student's behavioral activities performed in an e-learning environment. [ABSTRACT FROM AUTHOR]

The excavation adaptability evaluation decision process for the TBM (tunnel boring machine) in long and large tunnels under complex geological conditions is an uncertain and fuzzy problem affected by multiple factors. Aiming to shift the status quo of TBMs tunneling adaptability evaluation and the ineffective use of related accident cases, the TBM tunneling adaptability evaluation method based on case-based reasoning is proposed and researched. The case-based reasoning method can use existing engineering experience and knowledge to answer newly encountered problems, and can accurately and efficiently evaluate the adaptability of TBM tunneling. Based on the nearest neighbor method, this paper establishes the calculation formula of TBM tunneling adaptability similarity. Based on the statistical analysis method, the evaluation indicators that play a relatively important role in the system are selected, and the TBM tunneling adaptability evaluation index system is constructed. The analytic hierarchy process (AHP) is used to determine the weight of the evaluation indicators at each level. According to the characteristics of TBM tunneling adaptability evaluation, the overall design of the case-based reasoning-based TBM tunneling adaptability evaluation decision system CBR-TBMEAEDS (case-based reasoning-TBM excavation adaptive evaluation decision system) is proposed, and the TBM tunneling adaptability evaluation case is expressed The case-based reasoning method and modification method were designed, and CBR-TBMEAEDS was developed. The system can be used to evaluate the adaptability of TBM to the constructed case library, and the evaluation results are consistent with the actual situation. [ABSTRACT FROM AUTHOR]

To improve the accuracy of similarity measures in case-based reasoning, in this paper, we propose a deep metric learning method based on a self-attention mechanism and a Siamese neural network to realize the weighted similarity measure between cases. In this method, weight assignment and similarity measurement processes are integrated into a deep network. The method can map cases to a new feature space through nonlinear processing of the network layer to obtain better feature representation. The inner relationship between the features is captured by the self-attention mechanism, which is connected to the previous network layer, and the weight of the features is determined by the scoring function. Finally, a metric function is added to the contrastive loss to measure the case similarity. Experiments show that the accuracy of this method is better than that of other algorithms in the similarity measure and can improve the accuracy of case retrieval. [ABSTRACT FROM AUTHOR]

Conceptual cost estimation is an important step in project feasibility decisions when there is not enough information on detailed design and project requirements. Methods that enable quick and reasonably accurate conceptual cost estimates are crucial for achieving successful decisions in the early stages of construction projects. For this reason, numerous machine learning methods proposed in the literature that use different learning mechanisms. In recent years, the case-based reasoning (CBR) method has received particular attention in the literature for conceptual cost estimation of construction projects that use similarity-based learning principles. Despite the fact that CBR provides a powerful and practical alternative for conceptual cost estimation, one of the main criticisms about CBR is its low prediction performance when there is not a sufficient number of cases. This paper presents a bootstrap aggregated CBR method for achieving advancement in CBR research, particularly for conceptual cost estimation of construction projects when a limited number of training cases are available. The proposed learning method is designed so that CBR can learn from a diverse set of training data even when there are not a sufficient number of cases. The performance of the proposed bootstrap aggregated CBR method is evaluated using three data sets. The results revealed that the prediction performance of the new bootstrap aggregated CBR method is better than the prediction performance of the existing CBR method. Since the majority of conceptual cost estimates are made with a limited number of cases, the proposed method provides a contribution to CBR research and practice by improving the existing methods for conceptual cost estimating. [ABSTRACT FROM AUTHOR]