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Medical bioengineering professionals are expected to play a leading role in the development of advanced algorithms and methods with the greater goal to enhance health care in fields like diagnosis, monitoring, and therapy. In a recent study on the current research areas in big data analytics and artificial intelligence in health care, after a systematic review of literature, it was found that the primary interest area is medical image processing and analysis followed by decision-support systems and text mining. Case-based learning is an instructional design model that is learner-centered and intensively used across a variety of disciplines. In this paper, it is presented a set of deep learning tools and a case study that help medical bioengineering students to grasp both theoretical concepts (medical, such as gynecological disorders and technological, such as deep learning, neural network architectures, learning algorithms) and delve into practical applications of these techniques in medical image processing. The case study concerns the automated diagnosis of cervigrams (also called cervicographic images), that are colposcopy images used by the gynecologist for cervical cancer diagnosis. The tools described in this paper are based on using PyTorch and are implemented in a Jupyter notebook. The notebook introduces the students to the problem of cervigrams classification, provides access to the dataset, allows data augmentation, data visualization and performing transfer learning. The notebook ends with study questions. [ABSTRACT FROM AUTHOR]

Case retrieve is the key link in Case-Based Reasoning(CBR)system, and the distribution of attribute weights affects the retrieval accuracy directly. However, the traditional retrieval methods do not pay much attention to the weights distribution which led to low retrieval efficiency. In this paper, a new method based on Water-Filling principle in wireless communication field is proposed to optimize the case attribute weights. Regarding every single attribute in the case base as a sub channel, then calculates the importance of each case attribute by analyzing the data volatility with the Water-Filling principle. To prove the availability of the method, a glass identification dataset from the UCI database is used for a simulation experiment and the result illustrates that the new method could get a better and more accurate retrieval result compared with the traditional methods. The method could dig the inner information of each case attribute, and could assign proper weight to each attribute, which improves the retrieval accuracy and proves that the method the paper introduced is effective for CBR system. [ABSTRACT FROM PUBLISHER]

The topic of this paper is a new method of knowledge discovery in documents called "Case Insight" (abbreviated to CI). The research question that led to this development was "How can we discover knowledge through case studies and make it usable for case-based problem solving?" To answer this question, this research took a Systems Thinking and Networked Thinking qualitative approach. Case-based problem-solving uses knowledge contained in authentic case descriptions (i.e. "good practice" or even "best practice" cases) and adapts it to the requirements of a new problem. Who can use this? Managers and management consultants who are starting out in their careers can benefit in particular from the CI method as it allows them to expand their repertoire of experience in problem-solving on the basis of case studies, i.e. without being involved in projects. All those interested in solving complex management problems in a case-based way also form part of the target audience. Case studies contain a great deal of problem-solving knowledge but only part of that knowledge can be absorbed through simple reading. The rest remains difficult to access, a hidden treasure, so to speak. Why is that? The reason is that knowledge discovery in case studies is made more difficult due to two obstacles: firstly, the texts are not sufficiently brain-friendly and secondly, they are not designed holistically enough. The CI method makes it possible to overcome these obstacles by means of CI tools and CI models. Firstly, CI tools are used to analyse case studies by comparing concepts, ideas, etc. and combining them into a whole; secondly, CI models make knowledge discovered in this way usable in the form of brain-friendly and holistic knowledge structures. Thus, knowledge discovery through the CI method complies with Immanuel Kant's definition of knowledge as "a whole of compared and linked ideas". [ABSTRACT FROM AUTHOR]

Information security is concerned with the protection of information, which can be stored, processed or transmitted within the critical information systems from organizations, against loss of confidentiality, integrity or availability. Protection measures to prevent these problems result through the implementation of controls at several dimensions: technical, administrative or physical. A vital objective for military organizations is to ensure superiority in contexts of information warfare and competitive intelligence. Therefore, the problem of information security in military organizations has been a topic of intensive work at both national and transnational levels, and extensive conceptual and standardization work is being produced. A current effort is to develop automated decision support systems to assist military decision makers, at different levels in the command chain, to provide suitable control measures that can effectively deal with potential attacks and, at the same time, prevent, detect and contain vulnerabilities targeted at their information systems. The concept and processes of the Case-Based Reasoning (CBR) methodology outstandingly resembles classical military processes and doctrine, in particular the analysis of "lessons learned" and definition of "modes of action". Therefore, the present paper addresses the modeling and design of a CBR system with two key objectives: to support an effective response in context of information security for military organizations; to allow for scenario planning and analysis for training and auditing processes. [ABSTRACT FROM AUTHOR]

This paper shows an improvement of legal decision-making via digitally produced verdicts. We investigate the use of Artificial Intelligence (AI) in relation to rendering arbitrational verdicts. The data was provided by e-Court, the first private online court of the Netherlands. In our survey the standard debt collection proceedings under Dutch Civil and Procedural law are used as a case study. The introduction of the subject matter is followed by an overview of the key-parameters required by e-Court for rendering a verdict in default cases. The reasoning methodologies of Intelligent Systems in the legal domain are then discussed. Following this discussion we will analyze the nature of the e-Court System to understand how it benefits from the various types of Intelligent Systems. Subsequently, we will discuss the rationale behind the choices made, the legal implications and the handling process within the public courts. Our contribution lies also in the investigation of the characteristics of the e-Court system for rendering default verdicts in debt collection proceedings. In our conclusion we will consider to what extent intelligent systems will be used in the contemporary digital court houses. [ABSTRACT FROM AUTHOR]

Aiming at many problems in the design process of Permanent magnet synchronous motor (PMSM), such as the complexity of design process, the over reliance on designers' experience and the lack of accumulation and inheritance of design knowledge, a design method of PMSM Based on CBR is proposed in order to solve those problems. In this paper, case-based reasoning (CBR) methods of cases similarity calculation is proposed for reasoning suitable initial scheme. This method could help designers, by referencing previous design cases, to make a conceptual PMSM solution quickly. The case retain process gives the system self-enrich function which will improve the design ability of the system with the continuous use of the system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a context-based hybrid semantic matching framework for e-mentoring system. The framework adopts ontology technologies to define the attributes of mentors and mentees, the matching results, and satisfaction values of the mentoring, and to recommend adequate mentors via semantic reasoning. In the developed ontology named mentoring-matching ontology (M2O), the pairs of mentor-mentee and the satisfaction values of mentoring are managed as cases. To increase the satisfaction of matching, the framework performs rule-based filtering and two kinds of semantic matching to find appropriate mentees and the most similar mentor with the new mentee. We implemented a mobile e-mentoring system named Mint Story with the proposed matching framework. Also, we show the superiority of our framework in the evaluation section. [ABSTRACT FROM PUBLISHER]

This work presents a framework for structuring knowledge using case-based reasoning (CBR). This framework provides a basis to develop the knowledge module that is represented by cases. Similarity measures can be used to compare and recover cases. The paper investigates the use of CBR in the domain module and also the influence of different measures when cases are compared and the modeling, where distinct attributes define different cases to represent an specific knowledge. A tutorial system to train helicopter pilots illustrates our approach as a case study. [ABSTRACT FROM PUBLISHER]

Case-Based Reasoning (CBR) is a branch of Artificial Intelligence (AI). It is the process of solving new problems based on the solutions of similar past problems. It is captured by the CBR cycle consisting of the fourth R's: Retrieve, Reuse, Revise, and Retain. When a new problem is encountered, similar past cases are retrieved from the case base, their information is reused to construct solutions, their solutions are revised to fit current needs, and the new experience is retained for future use. Robotics is a branch of engineering that involves the conception, design, manufacture, and operation of robots. This field overlaps with electronics, computer science, artificial intelligence, mechatronics, nanotechnology and bioengineering. CBR has been applied by many famous organizations such as IBM, Visa International, Volkswagen, British Airways, and NASA. Many case studies in the real world environment have shown that the techniques of design and implementation of Case-Based Reasoning allow for the development of robust and reliable systems. In this paper, we will explore the use of CBR in Robotics. We will show trough a case study and step-by-step examples how to design and implement a robust CBR system in robotics and how CBR is reliable and suited for building robot systems. We will demonstrate how it is relatively simple to add CBR components to existing information systems. [ABSTRACT FROM AUTHOR]

Computer-based decision support systems are often used for dedicated tasks such as the detection of sepsis. However, positive predictive values for sepsis detection are reported to achieve only around 46%. In this paper we describe a novel approach to use temporal data of electronic patient records based on similarity measures. We apply the concept of casebased reasoning, which is well-established in many fields of medical informatics. Temporal patient data are organized in a time-graph structure. For the quantification of similarity between cases, we exploit graph theory based approaches. For development and evaluation of our time-graph similarity frame we use the open MIMIC III dataset. In a later phase, we envision to transfer our concept from sepsis to other diseases. [ABSTRACT FROM AUTHOR]

This paper introduces three case-based modules for teaching civil engineering students about decision making for sustainability. The purpose of these modules is to connect civil engineering design to recent advances in behavioral decision science. Students who recognize their own decision biases will be better able to manage their decisions and be better able to recognize how their designs influence stakeholder decisions in the future. The three case studies varied in design topic and behavioral decision science concepts, from land development (choice overload) to renewable energy (status quo bias) to wastewater infrastructure (risk aversion). The case-based modules were developed using structured interviews with engineering design teams from each of the case projects. Relevant concepts from behavioral decision science were identified while interviewing the engineering design teams. The developed modules were tested with over 280 undergraduate engineering students. Methods to evaluate learning include pre and post-module surveys and free-response questions. After the module, students were more likely to mention and articulate the role that humans' mental barriers, like choice overload, bounded rationality, and satisficing play in decision making for sustainability. They also recognized how tools like Envision can help reduce these cognitive biases. In addition to integrating diverse topics and disciplines into a unified and relevant teaching module, the intention is that other faculty can also use these cases. Slides (either one or two-day instruction), teaching notes, and grading rubrics are available for other instructors to download and use and can be found in the Center for Sustainable Engineering repository. [ABSTRACT FROM AUTHOR]

The paper deals with the design and development of a Cloud Management Case-Based Teaching System with Case-Based-Reasoning (CBR) and Big Data Technologies. "Cloud management" is the management of cloud computing products and services. "Cloud Computing" or "Cloud Learning" is an "Online Learning", or "eLearning", that is available in the Cloud; this means that resources are stored in a virtual environment, accessed from various forms of web-enabled devices. In radiology, "Cloud Computing" is a web-based image sharing platform. It centralizes the radiology infrastructure, imaging software, data storage, and the network configuration delivered to any internet-based computer as a service based on user demand. It will allow a fast deployment of radiology imaging solutions to any user and flexibility in imaging. The flexibility of anywhere, anytime access to medical data allows radiologists to report remotely from outside the hospital. Case-Based Reasoning (CBR) is the process of solving new problems based on the solutions of similar past problems and structured as cases. The medical diagnosis is based on the experts' expertise (past experience). This experience is under form clinical cases. These cases are examples resulting from real situations. This process is similar to the daily practice of experts. Case-based Reasoning is appropriate to medical field. A cloud management system combines software and technologies in a design for managing cloud environments. The paper proposes a case representation model for cloud management grouping clinical and radiological cases and define similarity functions according to the CBR approach. We will illustrate the case representation model by an example on medical field (breast cancer). [ABSTRACT FROM AUTHOR]

Case-Based Reasoning may help address problems with difficult mathematical formalizations in artificial intelligence, such as decision making. While these models usually carry a series of structural and functional dependencies that may affect parallel speedup and efficiency, there are other performance features that justify parallel implementations. This paper presents a reworking of the classical structures and methods of Case-Based Reasoning, redefined for parallel environments, alongside performance analysis techniques suitable for symbolic representations. [ABSTRACT FROM PUBLISHER]

A method integrates case-based reasoning (CBR) with rule-based reasoning (RBR) is developed in this paper. Based on these two artificial intelligence techniques, it is used in the design of maintenance decision system. According to the characteristics and requirements of equipments' health condition, the structure and workflow of health condition-based maintenance decision intelligent reasoning method is proposed, then a system was explored and deliberated. [ABSTRACT FROM PUBLISHER]

Design patterns represent a highly effective way to improve the quality of software system, due to its ability to capture the best practices and design knowledge; this enable software engineer to develop their applications. Unfortunately, choosing suitable patterns to specific design problems are difficult task for novice. Over the past years of research in the field of software engineering several conferences and workshops are focused on writing and publishing patterns; this increases the degree of challenges that face the developer in determining the suitable design patterns. This paper presents method to facilitate and assist the designers in selecting the suitable patterns for their problems. We propose a strategy method to obtain the appropriate recommendation. [ABSTRACT FROM PUBLISHER]

Situation-aware case-based decision support (SACBDS) systems comprise two distinct parts: situation awareness (SA) and case-based reasoning (CBR). The SA part keeps a finite history of the time space information of the domain and uses rules to interpret cues from the environment with respect to an individual user's context, and then anticipates future situations by performing statistical inference over historical data. The CBR part is the part that seeks to accomplish a particular task with knowledge of the environment from the SA component. This paper discusses the fusion of the CBR model and the SA model into a case-based situation awareness (CBSA) model for situation awareness based on experience rather than rule, similarity assessment and problem solving prediction. The CBSA system perceives the users' context and the environment and uses them to understand the current situation by retrieving similar past situations. Every past situation has a history. The future of a new situation (case) is predicted through knowledge of the history of a similar past situation. The paper evaluates the concept in the flow assurance control domain to predict the formation of hydrate in sub-sea oil and gas pipelines. The results provided the CBSA system with greater number of accurate predictions than the SACBDS system. [ABSTRACT FROM PUBLISHER]

This paper introduces a case-based reasoning hybrid agent structure in the background of machine tools of an intelligent design system. The structure consists of eight modules and the core ones are learning modules, retrieval modules and decision-making modules. The study mainly concerns on the common knowledge sharing and reuse in system and its purpose is to reduce the design redundancy of the process, improve intelligence of the design process and enhance the efficiency of parts design. [ABSTRACT FROM PUBLISHER]

Case-Based Reasoning (CBR) is a technique which consists of learning from past experiences. Its use is very interest in domains where experience plays an important role in the resolution of new problems, which is the case in medical diagnosis. This paper presents a decision making support system based on CBR and applied to the diagnosis of Chronic Obstructive Pulmonary Disease (COPD), a dangerous respiratory disease bound to tobacco. In medical activity, the physicians are often in situations where they have to make decision whereas they have not all necessary data then they are essentially based on their experiences to find the most probable diagnosis. Our system aims to reproduce this behavior of physicians by estimating similarity on attributes with missing data in the most important stage of CBR process consisting to retrieve the most similar case. We have proposed implemented and tested three ideas to find the real diagnosis of cases which have missing data. Some heuristics functions have been also developed for measuring similarity on attributes with symbolic nature. Preliminary experimentations of these ideas and heuristics have proved a good impact on results. [ABSTRACT FROM PUBLISHER]

This paper provides a new approach emergency plan generating with case-based reasoning (CBR), utilizing knowledge representation methods in artificial intelligence. Based on this approach, we describe the system structure and workflow. Using the object-oriented method, plan cases existed are expressed as a triple-tuple form, including case information, solution information, and result information, and the case similarity computation method is also discussed. The computation and analysis of case similarity prove that the approach is viable, and this approach can offer decision support for the highway tunnel traffic control under emergency. [ABSTRACT FROM PUBLISHER]

The automotive engine performance tune-up is greatly affected by the calibration of its electronic control unit (ECU). The ECU calibration is traditionally done by trial-and-error method. This traditional method consumes a large amount of time and money because of a large number of dynamometer tests. To resolve this problem, case based reasoning (CBR) is employed, so that an existing and effective ECU setup can be adapted to fit another similar class of engines. The adaptation procedure is done through a more sophisticated step called case-based adaptation (CBA) [1, 2]. CBA is an effective knowledge management tool, which can interactively learn the expert adaptation knowledge. The paper briefly reviews the methodologies of CBR and CBA. Then the application to ECU calibration is described via a case study. With CBR and CBA, the efficiency of calibrating an ECU can be enhanced. A prototype system has also been developed to verify the usefulness of CBR in ECU calibration. [ABSTRACT FROM AUTHOR]

With the development of modern technology, modern vehicles adopt electronic control system for injection and ignition. In traditional way, whenever there is any malfunctioning in an automotive engine, an automotive mechanic usually performs a diagnosis in the ignition system of the engine to check any exceptional symptoms. In this paper, we present a case-based reasoning (CBR) approach to help solve human diagnosis problem. Nevertheless, one drawback of CBR system is that the case library will be expanded gradually after repeatedly running the system, which may cause inaccuracy and longer time for the CBR retrieval. To tackle this problem, case-based maintenance (CBM) framework is employed so that the case library of the CBR system will be compressed by clustering to produce a set of representative cases. As a result, the performance (in retrieval accuracy and time) of the whole CBR system can be improved. [ABSTRACT FROM AUTHOR]

This paper presents an approach to holiday scheduling which schedules the activities a tourist can enjoy in a city through an Internet service available 24 hours a day, 7 days a week (at information desks in airports, train stations, etc.). Holiday scheduling turns out to yield constraint satisfaction problems, with the particularity that the amount of constraints in this problem is not critical, but the number of activities is huge. Hence, the methodology proposed is a combination of case-based reasoning and constraint solving algorithms. The benefits of this combination are particularly useful in scheduling real life problems, and is suitable in terms of the particularities of the domain. A case study at the city of Girona is provided. [ABSTRACT FROM AUTHOR]

This paper presents a web based recommendation system aimed at supporting a user in building a personalized travel plan. A travel plan bundles together elementary travel components that are selected by the user in a mixed initiative way: the user poses queries and the recommender helps the user to reformulate the query and finally ranks the selected items. This paper concentrates on the methodological aspects that enable the interactive query refinement of the user queries, and in particular the intelligent relaxation of query constraints in case the query returns no items. [ABSTRACT FROM AUTHOR]

Copyright of CISTI (Iberian Conference on Information Systems & Technologies / Conferência Ibérica de Sistemas e Tecnologias de Informação) Proceedings is the property of Conferencia Iberica de Sistemas Tecnologia de Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper deals with the use of multiple 'teaching cases' based on multiple models and method-based reasoning in the field of senology (breast cancer domain). 'Teaching cases' can be a good support in helping both expert and junior senologists in teaching and learning. Thanks to these 'teaching cases', the experts provide quality training to trainees (junior radiologists). In medicine, the method used in teaching is based on experiments, called 'clinical cases'. We use the Case-Based Reasoning (CBR) approach to represent these 'teaching cases'. CBR starts with a set of cases or training examples; it forms generalizations of these examples, by identifying commonalities between a retrieved case and the target problem. This set of cases (experience) may then be re-used when solving new problems, for example, when making new diagnoses decisions. The radiologist-senologist can benefit from prior experience and cases. According to Kolodner (the founder of the CBR approach), CBR can mean adapting old solutions to meet new demands, using old cases to explain new situations; using old cases to critique new solutions, or reasoning from precedents to interpret a new situation or create an equitable solution to a new problem. We rely on these concepts to develop our reasoning for developing teaching strategies to accompany trainees in their learning advance and decision making. The paper will show that the CBR approach is very much suited to representing the knowledge in medical field, both in learning and training. With the CBR approach we can reason in many ways and represent knowledge efficiently, using the multiple models and methods we have developed. We combine object modelling with UML2 (Unified Modelling Language) with Case-Based Reasoning to represent the knowledge present in the 'teaching cases'. The outcome of the research is both conceptual and practical. It also has a methodological dimension. [ABSTRACT FROM AUTHOR]

Many models to estimate the cost of software projects were devised during the last three decades [1], [3]. Although until now there is no perfect model that can estimate the cost precisely, the estimations varies to be over-estimated that may lead to using the resources inefficiently and losing a lot of business opportunities or under-estimated that may lead to delaying the final software product delivery, project management problems arises, unexpected increase in budget, and low quality of software projects [2]. As a result no accurate decision can be made due to the lack of consistency, this makes the senior project managers depend on their experience to reach the final decision to proceed or cancel the project [4]. In this paper, we apply the artificial intelligence methodology Case-Based Reasoning (CBR) upon an open source software projects dataset in order to assist the project managers to grasp the appropriate cost estimation of a software project. [ABSTRACT FROM AUTHOR]

Data play an important role in simulation system, the development and use of simulation system is affected by data quality. Data verification, validation and certification (VV&C) is an approach to improve data quality, it is so difficult to select appropriate VV&C method for so huge amount of simulation data. Basing on the mapping of data and VV&C methods, an idea of intelligent data VV&C is brought out in this paper, the purpose of this idea is selecting right VV&C methods for simulation data. An architecture of data VV&C system based on Case-Based Reasoning (CBR) is founded, the modules and functions of the system are analyzed, finally, the key techniques of system implement is studied. [ABSTRACT FROM PUBLISHER]

Information retrieval mechanisms from the web become tedious as the amount of content is growing dynamically every day. The semantic web has an idea of connecting, integrating and analyzing data from various data sources and forming a new information flow, hence a web of databases connected with each other and machines interacting with other machines to yield results which are user oriented and precise. A New Integrated Case And Relation Based Page Rank Algorithm have been proposed to rank the results of a search system based on a user's topic or query. This paper proposes an optimized semantic searching of keywords represent by simulation an ontology with a proposed algorithm which ramifies the effective semantic retrieval of information which is easy to access and time saving. [ABSTRACT FROM PUBLISHER]

The European Union FP7 CHRON project addresses the challenge of controlling and managing the next generation of heterogeneous optical networks supporting the Future Internet. For that aim, the CHRON project proposes a Cognitive Heterogeneous Reconfigurable Optical Network, which observes, acts, learns and optimizes its performance. The core element of such network is the Cognitive Decision System, which makes decisions on how to deal with traffic demands and network events by using knowledge acquired from previous experience and considering the current network status. In this paper, we present the architecture of this Cognitive Decision System, and then show a set of methods developed for two of its building blocks: the Virtual Topology Design module and the Quality of Transmission (QoT) estimator module. Regarding the former module, we present two multiobjective genetic algorithms to solve the impairment-aware virtual topology design problem, and evaluate their performance under realistic traffic demands. Results show that one of the methods, which is enhanced with additional cognition, leads to better results. For the latter module, we present a cognitive QoT estimator, which achieves more than 99% successful classifications of optical connections into high or low QoT categories, and is much faster for on-line operation than an existing approach. [ABSTRACT FROM PUBLISHER]

The bus crew scheduling problem (BCSP) is to assign drivers and conductors to daily crew duties that cover a set of schedules for buses of a public bus company during a period of time, e.g., a week. By combining case-based reasoning (CBR) and rule-based reasoning (RBR), this paper introduces the design and implementation of a bus crew scheduling system (BCSS). The current system works like this. Firstly, an inference mechanism integrating CBR and RBR is designed to obtain near-optimal solutions. Secondly, based on these near-optimal solutions, a genetic algorithm is developed to produce better solutions. The practical applications show that the system is effective. [ABSTRACT FROM PUBLISHER]

The importance of knowledge management (KM) to organizations in today's competitive environment is being recognized as paramount and significant. As organizations continue to seek optimal levels of competitive advantage and innovation, the search continues for more powerful and successful ways of achieving these goals. Healthcare organizations are facing many challenges in the 21st Century due to changes taking place in global healthcare systems. Increasing costs, financial constraints, increased emphasis on accountability and transparency, changes in education, growing complexities of biomedical research, new partnerships in healthcare and great advances in Information Technology suggest that a predominant paradigm shift is occurring. This shift is necessitating a focus on interaction, collaboration and increased sharing of information and knowledge which is in turn leading healthcare organizations to embrace the techniques of KM in order to create and sustain optimal healthcare outcomes. The objective of this paper is to present and describe the KM capabilities. By reviewing the related literature, the study investigates the definitions and dimensions of KM in the health sector. Next, the importance of measuring knowledge in organizations and KM methodology will be expressed and at the end, it will discuss how KM can make the health sector more efficient and effective. [ABSTRACT FROM AUTHOR]

Autonomic systems are the software systems capable to manage themselves. These systems undergo a learning process to achieve this capability. Case-based reasoning (CBR) is one of the promising learning paradigms for autonomic managers. Autonomic managers monitor the pulse of the monitored system on periodic basis and analyze the captured state of the system. In case of a problematic state, autonomic managers use their CBR based decision support system to rectify the problem. One of the critical problems in such systems is recovery from failures. The problem of identifying the factors affecting the performance of CBR system is a key element to build successful and accurate decision support systems. For this purpose, a hybrid CBR based self-healing system supported by attribute selection methods has been proposed. An empirical investigation has been conducted in this paper using different similarity measures, solution adaptation methods and attribute selection techniques. To address the performance problem of CBR in self-healing systems, we have conducted experiments on an emulator of self-healing systems called RUIBiS using different machine learning techniques to determine the significance of weights for these similarity distances. [ABSTRACT FROM PUBLISHER]

The manually online trade matching is becoming the bottleneck for electronic commerce brokers while the electronic commerce exhibits more popular than ever. In this paper, we propose a solution for semi-automatic online trade matching. The system architecture of the Case-Based Reasoning (CBR) paradigm is presented, followed by the detailed CBR engine with Genetic Algorithm (GA) supporting in feature weighting. The experiment followed reveals its performance. [ABSTRACT FROM PUBLISHER]

This paper represents using Case-Based Reasoning (CBR) in order to support undergraduate control engineering students for learning nonlinear systems and control applications. CBR is an experience-based problem solving methodology; thus, it is used for helping control engineering students to design conventional PID controllers for nonlinear systems. For this purpose, GUNT pH neutralization process is used as the experimental setup and a GUI is designed for cased-based reasoning in MATLAB. It is observed that undergraduate students can learn and increase their experience on nonlinear systems using the proposed new CBR-GUI. [ABSTRACT FROM PUBLISHER]

A cooperative negotiation and problem solving method, ANC (Automated Negotiation and Case-based reasoning), is proposed in this paper. The goal of this method is to provide a suitable smart home service for multiple user requirements. The motivation in this paper is that agreeing on a common service is difficult when different users propose different requirements. Therefore, in ANC, cooperation negotiation is considered for resolving conflicts for making the requirements consistent among users, and based on such requirements, a common solution is provided through a reasoning process. There are five stages in ANC: issue acquisition, conflict detection, issue decision, automated negotiation, and problem solving. To make the ANC system provide personalization, a learning method based on attributes weighting has been integrated with the advantage of the constant learning ability of case-based reasoning. [ABSTRACT FROM PUBLISHER]

This paper presents a Community Computer Environment (CCE), which supports a Community of Practice (CoP). This community brings together teachers users of a teaching method allowing their students to learn and acquire professional knowledge and know-how. This method uses an approach based on a group per project pedagogy. During its use in a classroom, teachers can find difficulties to apply the method. We detail the layer of the environment that permits to assist teachers in solving their problems. This layer is based on Case-Based Reasoning (CBR). [ABSTRACT FROM PUBLISHER]

Case-based reasoning is applied to teaching process in order to make students learn well on the basis of previous teaching experience. Through the detailed introduction of implementation details of case-based reasoning: Similar case definition and search, case adjustment and the addition of new cases, this paper introduces a way that cannot only carry out uncertainty information reasoning, but also give students some teaching methods according to statistical information in advance. [ABSTRACT FROM PUBLISHER]

The foundations and technologies of Case-Based Reasoning (CBR) has emerged from academic projects to commercial used applications which are supporting everyday life. Everything changes in the long term which includes planning, modeling and developing reasoning software due to new aspects such as big data and various new services provided by cloud computing. Maintaining a balance between achieving the requirements and maximizing the return on investment of a software application can be seen as a difficult task. In this paper we will present the evolution of CBR software over the past years in order to gain an understanding concerning some crucial points when creating a CBR system. [ABSTRACT FROM PUBLISHER]

In this paper we propose a more effective use of the eLearning platforms resources, in order to decrease the effort and time required to derive useful knowledge and bringing up together multiple valuable contributions to overcome resource maintenance difficulties. The primary target of our work is the role of a Web based platforms' administrator (and promoter) that hasn't enough experience on site optimization and resource maintenance. Our idea to deal with this challenge is to assist such tasks, providing a system that acquires and reuses the knowledge gained from the experience in solving problems concerning administration activities. The system explores the case-based reasoning approach to accomplish its duty of suggesting a suited solution for particular usage data analysis problem, on an eLearning Web based platform, as well, supporting the collecting and structuring of the knowledge contained on successful solved usage problems. We describe the system and propose its application to the specific domain of eLearning Web based platforms optimization. We are specifically engaged in facilitating the collection and cataloguing of knowledge in this area, so it can be shared, evaluated and finally reused in new situations. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application of software reuse concept together with case-based reasoning to solve time-tabling problem. The combination of both concept was induce through the similarity solution pattern which is both revise to previous cases as reference to solve current problem. A brief process flow is explained in this paper to show the possibility of combining case-based reasoning and software reuse to bring a new solution method. [ABSTRACT FROM PUBLISHER]

Multi-sensor intrusion detection systems (IDSs) combine the alerts raised by individual IDSs and possibly other kinds of devices such as firewalls and antivirus software. A critical issue in building a multi-sensor IDS is alert-correlation, i.e., determining which alerts are caused by the same attack. This paper explores a novel approach to alert correlation using case-based reasoning (CBR). Each case in the CBR system’s library contains a pattern of alerts raised by some known attack type, together with the identity of the attack. Then during run time, the alert streams gleaned from the sensors are compared with the patterns in the cases, and a match indicates that the attack described by that case has occurred. For this purpose the design of a fast and accurate matching algorithm is imperative. Two such algorithms were explored: (i) the well-known Hungarian algorithm, and (ii) an order-preserving matching of our own device. Tests were conducted using the DARPA Grand Challenge Problem attack simulator. These showed that the both matching algorithms are effective in detecting attacks; but the Hungarian algorithm is inefficient; whereas the order-preserving one is very efficient, in fact runs in linear time. [ABSTRACT FROM AUTHOR]

The article discusses a study that demonstrates how the integration of social cognitive theory, script development, and motivation concepts help to create a case-based modeling [CBM] instructional model that can assist individuals to build their own guides for performance in various managerial tasks. The article discusses motivation, self-efficacy, scripts and instruction, and the process of CBM. The article concludes that the comparison information reveals that the CBM method has promise and should receive further study.

This paper describes the application of case-based reasoning (CBR) to provide decision support for the NDT engineer during Inspection Qualification. A key element of this process is to present experimental evidence on the effectiveness of the inspection. A prototype for the system has been developed using a case-base which describes the defect, the probe and response. This paper discusses the motivation for the system, the case structure, weightings, similarity measures, retrieval techniques and its design and implementation. © 2003 American Institute of Physics [ABSTRACT FROM AUTHOR]

Bruises can appear when blood vessels rupture, which can lead to the risk of blood leakage into the surrounding tissues. Evaluation and detection of these symptoms, especially those related to health problems or accidents, are very important in medical environments. Bruises can also serve as an alert sign of health problems that a medical evaluation is recommended and might be urgently needed. Unfortunately, it can be very challenging for medical practitioners to appropriately identify and categorize bruises due to the complexity of the situations and many types of bruises. The main goal of this study is to promote the use of Artificial Intelligence (AI) in healthcare systems. It aims to help improve computer-aided practices by making use of an open-source algorithm such as YOLOv8 and incorporate it into a case-based reasoning (CBR) approach for fast and precise identification of bruises. In this study, we introduce an approach to this problem by using the CBR-YOLO approach. The use of the CBR-YOLO approach can help and support the decision-making practice. Although bruises have many types and the same appearance, it can still provide recommendations and commentary on the bruises. This method can be useful in diagnosing patients in a timely manner. [ABSTRACT FROM AUTHOR]

Delays in construction projects are inherent. Utilizing the experience from past construction projects can be helpful in predicting the delay in construction projects. Case-based reasoning (CBR), a technique that uses knowledge from past experiences, can be viewed as an effective method in estimating the construction delay. Even though CBR has been frequently used to predict the construction project costs in previous studies, studies performed for construction schedule and delay are rare. Moreover, the recent increase in the construction in Indian real estate industry shows a rise in residential high-rise construction. With emphasis on residential high-rise construction and using preliminary project information, this paper focuses on developing a methodology for predicting construction delay using CBR with the attribute weights being input from multiple regression analysis (MRA). The results indicate that the as compared to conventional multiple regression methods, CBR performed better in forecasting the construction delay. Though this study focusses on residential high-rise construction, the construction industry may be benefitted using the learnings from this study. It can prove to be useful for the construction professionals not familiar with the challenges existing in new locations. Consequently, this study can provide means of predicting the construction schedule in construction projects using preliminary project information [ABSTRACT FROM AUTHOR]