Adaptive Delivery of Trainings Using Ontologies and Case-Based Reasoning

Mansouri, Dounia; Mille, Alain; Hamdi-Cherif, Aboubekeur

doi:10.1007/s13369-013-0761-4

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Case-based reasoning, as one of the most important machine learning algorithms, has been widely studied. A case-based reasoner uses old experiences or cases to suggest solutions to new problems, to point out potential problems, to interpret a new situation and make predictions on what might happen, or to create arguments to measure the conclusion [19,20]. Among machine learning algorithms, case-based reasoning (CBR) has a higher flexibility and requires less maintenance effort.…”

Section: Case-based Reasoning Systemmentioning

confidence: 99%

Fuzzy Case-Based Reasoning System

Bai

Zhang

et al. 2016

Applied Sciences

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Abstract:In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR) system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature), according to the maximum degree principle. Thus, we add the "vague" concept into a case-based reasoning system. It is these somewhat vague inputs that make the outcomes of the prediction more meaningful and accurate, which illustrates that it is not necessarily helpful when we always create accurate predictive relations through crisp cases. Finally, we prove this and apply this model to practical weather forecasting, and experiments show that using fuzzy cases can make some prediction results more accurate than using crisp cases.

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Section: Case-based Reasoning Systemmentioning

confidence: 99%

Fuzzy Case-Based Reasoning System

Bai

Zhang

et al. 2016

Applied Sciences

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“…Fuzzy description logics support the implementation of intelligent, semantic, or knowledge-intensive-CBR systems (Ali et al 2015). Experience-based problems can be handled better by CBR for many reasons (Mansouri, 2014).…”

Section: Case-based Reasoning For Diabetes Managementmentioning

confidence: 99%

“…CBR can reason from complete specific episodes (cases or patient encounters). Its knowledge base can be created from the EHR data, which solve the knowledge acquisition bottleneck problem found in other reasoning methods such as rule-based systems (Branden et al 2011); • Experts usually define their experience in the form of cases rather than rules (Mansouri, 2014).…”

Section: Introductionmentioning

confidence: 99%

Medical Case Based Reasoning Frameworks

El–Sappagh

Elmogy

2016

International Journal of Decision Support System Technology

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Case-Based Reasoning (CBR) is one of the most suitable AI techniques for building clinical decision support systems. Medical domain complexity introduces many challenges for building these systems. Building the systems' knowledge base from the Electronic Health Record (EHR), the encoding of case-base knowledge with standard medical ontology, and the handling of vague data are examples of these challenges. Although several advantages of using CBR in medicine have been identified, there are no real systems acceptable to physicians. This systematic review examines the current state of CBR and its limitations in the medical domain, especially for diabetes mellitus. The critical evaluation of the status of diabetes CBR systems presents unique opportunities for improving these systems. The literature review covers most of the English language studies extracted from relevant databases by using search terms relating CBR, ontology, Fuzzy, and standard terminology concepts. The authors identify 38 articles published between 1999 and 15 January 2015, which represent original researches in CBR domain. The study includes 15 (39.5%) non-medical studies and 23 (60.5%) medical studies with ~22% for diabetes CBR. A list of 18 integrated evaluation metrics has been proposed and used in the analysis. The results show that the non-medical CBR systems achieved higher advances (50%) than medical systems (42.9%). In addition, the diabetes management CBR systems achieve the lowest advances (21.4%) compared to other systems. These shortages explain the question “why CBR paradigm are not fully utilized in the commercial medical systems?” As a result, there is a distinct need for more comprehensive enhancements in clinical CBR especially diabetes systems.

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“…In case-based learning, the training samples, the cases, are stored and accessed to solve a new problem [14,15]. It is very important to find a way to represent the data.…”

Section: Case-based Reasoningmentioning

confidence: 99%

Case-Based FCTF Reasoning System

Zhang

Li³

et al. 2015

Applied Sciences

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Case-based reasoning uses old information to infer the answer of new problems. In case-based reasoning, a reasoner firstly records the previous cases, then searches the previous case list that is similar to the current one and uses that to solve the new case. Case-based reasoning means adapting old solving solutions to new situations. This paper proposes a reasoning system based on the case-based reasoning method. To begin, we show the theoretical structure and algorithm of from coarse to fine (FCTF) reasoning system, and then demonstrate that it is possible to successfully learn and reason new information. Finally, we use our system to predict practical weather conditions based on previous ones and experiments show that the prediction accuracy increases with further learning of the FCTF reasoning system.

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Adaptive Delivery of Trainings Using Ontologies and Case-Based Reasoning

Cited by 7 publications

References 14 publications

Fuzzy Case-Based Reasoning System

Fuzzy Case-Based Reasoning System

Medical Case Based Reasoning Frameworks

Case-Based FCTF Reasoning System

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