Case-based system: Indexing and retrieval with fuzzy hypercube

In some applications with case-based system, the attributes available for indexing are better described as linguistic variables instead of receiving numerical treatment. In these applications, the concept of fuzzy hypercube can be applied to give a geometrical interpretation of similarities among cases. This paper presents an approach that uses geometrical properties of fuzzy hypercube space to make indexing and retrieval processes of cases.

Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system

A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined.

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.

Development of an open case-based decision-support system for diagnosis in oral pathology

Making diagnoses in oral pathology are often difficult and confusing in dental practice, especially for the lessexperienced dental student. One of the most promising areas in bioinformatics is computer-aided diagnosis, where a computer system is capable of imitating human reasoning ability and provides diagnoses with an accuracy approaching that of expert professionals. This type of system could be an alternative tool for assisting dental students to overcome the difficulties of the oral pathology learning process. This could allow students to define variables and information, important to improving the decision-making performance. However, no current open data management system has been integrated with an artificial intelligence system in a user-friendly environment. Such a system could also be used as an education tool to help students perform diagnoses. The aim of the present study was to develop and test an open case-based decisionsupport system.Methods: An open decision-support system based on Bayes' theorem connected to a relational database was developed using the C++ programming language. The software was tested in the computerisation of a surgical pathology service and in simulating the diagnosis of 43 known cases of oral bone disease. The simulation was performed after the system was initially filled with data from 401 cases of oral bone disease.Results: the system allowed the authors to construct and to manage a pathology database, and to simulate diagnoses using the variables from the database.Conclusion: Combining a relational database and an open decision-support system in the same user-friendly environment proved effective in simulating diagnoses based on information from an updated database.

IIS : an intelligent interaction system that provides e-service, based on interaction and case-based reasoning

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

IIS : an intelligent interaction system that provides e-service, based on interaction and case-based reasoning

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Skapandet av ett webbaserat ärendehanteringssystem för affärssystemet Pyramid med Scrum : Creation of a web based case management system for the business system Pyramid with Scrum

The business system known as Pyramid does today not provide its user with a reasonable system regarding case management for support issues. The current system in place requires the customer to contact its provider via telephone to register new cases. In addition to this, current system doesn’t include any way for the user to view any of their current cases without contacting the provider.A solution to this issue is to migrate the current case management system from a telephone contact to a web based platform, where customers could easier access their current cases, but also directly through the website create new cases. This new system would reduce the time required to manually manage each individual case, for both customer and provider, resulting in an overall reduction in cost for both parties.The result is a system divided into two different sections, the first one is an API created in Pyramid that acts as a web service, and the second one a website which customers can connect to. The website will allow users to overview their current cases, but also the option to create new cases directly through the site. All the information used to the website is obtained through the web service inside Pyramid. Analyzing the final design of the system, the developers where able to conclude both positive and negative aspects of the systems’ final design. If the platform chosen was the optimal choice or not, and also what can be include if the system is further developed, will be discussed.The development process and the method used during development will also be analyzed and discussed, what positive and negative aspects that where encountered. In addition to this the cause and effect of a development team smaller than the suggested size will also be analyzed. Lastly an analysis of actions that could’ve been made in order to prevent certain issues from occurring will.

Efficiency and feasibility of product disassembly: A case-based study

The productivity associated with commonly available disassembly methods today seldomly makes disassembly the preferred end-of-life solution for massive take back product streams. Systematic reuse of parts or components, or recycling of pure material fractions are often not achievable in an economically sustainable way. In this paper a case-based review of current disassembly practices is used to analyse the factors influencing disassembly feasibility. Data mining techniques were used to identify major factors influencing the profitability of disassembly operations. Case characteristics such as involvement of the product manufacturer in the end-of-life treatment and continuous ownership are some of the important dimensions. Economic models demonstrate that the efficiency of disassembly operations should be increased an order of magnitude to assure the competitiveness of ecologically preferred, disassembly oriented end-of-life scenarios for large waste of electric and electronic equipment (WEEE) streams. Technological means available to increase the productivity of the disassembly operations are summarized. Automated disassembly techniques can contribute to the robustness of the process, but do not allow to overcome the efficiency gap if not combined with appropriate product design measures. Innovative, reversible joints, collectively activated by external trigger signals, form a promising approach to low cost, mass disassembly in this context. A short overview of the state-of-the-art in the development of such self-disassembling joints is included. (c) 2008 CIRP.

A risk-based system to penalize and reward line management for occupational safety and health performance

Penalizing line management for the occurrence of lost time injuries has in some cases had unintended negative consequences. These are discussed. An alternative system is suggested that penalizes line management for accidents where the combination of the probability of recurrence and the maximum reasonable consequences such a recurrence may have exceeds an agreed limit. A reward is given for prompt effective control of the risk to below the agreed risk limit. The reward is smaller than the penalty. High-risk accidents require independent investigation by a safety officer using analytical techniques. Two case examples are given to illustrate the system. Continuous safety improvement is driven by a planned reduction in the agreed risk limit over time and reward for proactive risk assessment and control.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Combined heat and power and consumption optimization in a SCADA-based system

The operation of power systems in a Smart Grid (SG) context brings new opportunities to consumers as active players, in order to fully reach the SG advantages. In this context, concepts as smart homes or smart buildings are promising approaches to perform the optimization of the consumption, while reducing the electricity costs. This paper proposes an intelligent methodology to support the consumption optimization of an industrial consumer, which has a Combined Heat and Power (CHP) facility. A SCADA (Supervisory Control and Data Acquisition) system developed by the authors is used to support the implementation of the proposed methodology. An optimization algorithm implemented in the system in order to perform the determination of the optimal consumption and CHP levels in each instant, according to the Demand Response (DR) opportunities. The paper includes a case study with several scenarios of consumption and heat demand in the context of a DR event which specifies a maximum demand level for the consumer.

Case-based reasoning for self-optimizing behavior

In this paper we present a Self-Optimizing module, inspired on Autonomic Computing, acquiring a scheduling system with the ability to automatically select a Meta-heuristic to use in the optimization process, so as its parameterization. Case-based Reasoning was used so the system may be able of learning from the acquired experience, in the resolution of similar problems. From the obtained results we conclude about the benefit of its use.

Neuroendocrine Tumours of the Female Genital Tract: a Case-Based Imaging Review with Pathological Correlation

BACKGROUND: Both primary and secondary gynaecological neuroendocrine (NE) tumours are uncommon, and the literature is scarce concerning their imaging features. METHODS: This article reviews the epidemiological, clinical and imaging features with pathological correlation of gynaecological NE tumours. RESULTS: The clinical features of gynaecological NE tumours are non-specific and depend on the organ of origin and on the extension and aggressiveness of the disease. The imaging approach to these tumours is similar to that for other histological types and the Revised International Federation of Gynecology and Obstetrics (FIGO) Staging System also applies to NE tumours. Neuroendocrine tumours were recently divided into two groups: poorly differentiated neuroendocrine carcinomas (NECs) and well-differentiated neuroendocrine tumours (NETs). NECs include small cell carcinoma and large cell neuroendocrine carcinoma, while NETs account for typical and atypical carcinoids. Cervical small cell carcinoma and ovarian carcinoid are the most common gynaecological NE tumours. The former typically behaves aggressively; the latter usually behaves in a benign fashion and tends to be confined to the organ. CONCLUSION: While dealing with ovarian carcinoids, extra-ovarian extension, bilaterality and multinodularity raise the suspicion of metastatic disease. NE tumours of the endometrium and other gynaecological locations are very rare. TEACHING POINTS: • Primary or secondary neurondocrine (NE) tumours of the female genital tract are rare. • Cervical small cell carcinoma and ovarian carcinoids are the most common gynaecological NE tumours. • Cervical small cell carcinomas usually behave aggressively. • Ovarian carcinoids tend to behave in a benign fashion. • The imaging approach to gynaecological NE tumours and other histological types is similar.

Design of a case-based reasoner for information security in military organizations

Information security is concerned with the protection of information, which can be stored, processed or transmitted within critical information systems of the 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 therefore 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.

Diagnosing Patients Combining Principal Components Analysis and Case Based Reasoning

This paper addresses the application of a PCA analysis on categorical data prior to diagnose a patients data set using a Case-Based Reasoning (CBR) system. The particularity is that the standard PCA techniques are designed to deal with numerical attributes, but our medical data set contains many categorical data and alternative methods as RS-PCA are required. Thus, we propose to hybridize RS-PCA (Regular Simplex PCA) and a simple CBR. Results show how the hybrid system produces similar results when diagnosing a medical data set, that the ones obtained when using the original attributes. These results are quite promising since they allow to diagnose with less computation effort and memory storage