Case-based reasoning classifier based on learning pseudo metric retrieval

Yan, Aijun; Yu, Hang; Wang, Dianhui

doi:10.1016/j.eswa.2017.07.022

Cited by 21 publications

(10 citation statements)

References 33 publications

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“…CBR is a promising AI technique, which has a self-learning capability, providing valuable knowledge to support decision making (Kolodner, 1993). It supposes that problems can be solved by using similar past solutions, particularly when occurrence of these problems is high (Yan et al , 2017). By utilizing the knowledge and previous experience stored in the CBR case library, users can generate solutions to cope with a new problem (Prentzas and Hatzilygeroudis, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

An IoMT-based geriatric care management system for achieving smart health in nursing homes

Tang

Choy

et al. 2019

IMDS

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Purpose The purpose of this paper is to develop an Internet of medical things (IoMT)-based geriatric care management system (I-GCMS), integrating IoMT and case-based reasoning (CBR) in order to deal with the global concerns of the increasing demand for elderly care service in nursing homes. Design/methodology/approach The I-GCMS is developed under the IoMT environment to collect real-time biometric data for total health monitoring. When the health of an elderly deteriorates, the CBR is used to revise and generate the customized care plan, and hence support and improve the geriatric care management (GCM) service in nursing homes. Findings A case study is conducted in a nursing home in Taiwan to evaluate the performance of the I-GCMS. Under the IoMT environment, the time saving in executing total health monitoring helps improve the daily operation effectiveness and efficiency. In addition, the proposed system helps leverage a proactive approach in modifying the content of a care plan in response to the change of health status of elderly. Originality/value Considering the needs for demanding and accurate healthcare services, this is the first time that IoMT and CBR technologies have been integrated in the field of GCM. This paper illustrates how to seamlessly connect various sensors to capture real-time biometric data to the I-GCMS platform for responsively supporting decision making in the care plan modification processes. With the aid of I-GCMS, the efficiency in executing the daily routine processes and the quality of healthcare services can be improved.

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Section: Literature Reviewmentioning

confidence: 99%

An IoMT-based geriatric care management system for achieving smart health in nursing homes

Tang

Choy

et al. 2019

IMDS

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“…CBR has received considerable attention with its ability to use historical data, such as cases or experiences, to predict a solution to the current problem [15]- [17]. The general principle of CBR is that similar problems have similar solutions [28].…”

Section: ) Case-based Reasoning Methodologymentioning

confidence: 99%

“…If the similarity reaches an appropriate threshold, then the models (including SMM and monitoring model) of the most similar case are directly switched to perform soft measurement and process monitoring under the current WC. Case-based correction is based on the idea of the case-based reasoning (CBR) methodology [15]- [17], and it can improve the utilization rate of historical information and avoid repetitive modeling for similar WCs. If there are no similar cases that have enough similarities to the current WC in the historical case base (HCB), there is always one case that is closest to the current WC.…”

Section: Introductionmentioning

confidence: 99%

Process Feature Change Recognition Based on Model Performance Monitoring and Adaptive Model Correction for the Gold Cyanidation Leaching Process

Liu

Shi

et al. 2019

IEEE Access

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The gold cyanidation leaching process (GCLP) is the central unit operation in hydrometallurgy, and satisfactory gold recovery is highly significant in practice. However, GCLP faces the challenge of an irregular slow time-varying feature (STVF), which seriously affects gold recovery, and blind treatment for STVF also has drawbacks, which results in the need for the recognition of STVF for purposeful, rather than blind, treatment. Meanwhile, it also faces the problem of change of working condition (COWC) due to the variability of mineral resources. Both STVF and COWC may cause degradation of the soft-measuring model, which presents the need for model correction. Therefore, a coping strategy is proposed to solve these existing problems. First, an improved model-based principal component analysis monitoring is proposed to detect model mismatch and monitor the change of process feature. Next, a support vector machine-based process feature change recognition method is presented to recognize change type, which not only provides guidance in treating STVF but also makes it possible to implement targeted model correction for STVF and COWC. Finally, an adaptive model correction strategy that combines case-based correction and just-intime learning-based correction is proposed. The simulation studies have verified the validity of the proposed coping strategy. INDEX TERMS Gold cyanidation leaching process, model-based principal component analysis, process feature change recognition, adaptive model correction.

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“…Similarly, in (Brown et al, 2017), the authors proposed a temporal CBR for diabetes insulin by looking at prior events such as blood pressure level, physical activities and carbohydrate ingestion. Additionally, similarity metrics like Euclidean distance have also used with CBR for pattern classification (Yan et al, 2017).…”

Section: Learning Based Approachesmentioning

confidence: 99%

QoS-aware service provisioning in fog computing

Murtaza

Akhunzada

Islam

et al. 2020

Journal of Network and Computer Applications

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Fog computing has emerged as a complementary solution to address the issues faced in cloud computing. While fog computing allows us to better handle time/delay-sensitive Internet of Everything (IoE) applications (e.g. smart grids and adversarial environment), there are a number of operational challenges. For example, the resource-constrained nature of fog-nodes and heterogeneity of IoE jobs complicate efforts to schedule tasks efficiently. Thus, to better streamline time/delay-sensitive varied IoE requests, the authors contributes by introducing a smart layer between IoE devices and fog nodes to incorporate an intelligent and adaptive learning based task scheduling technique. Specifically, our approach analyzes the various service type of IoE requests and presents an optimal strategy to allocate the most suitable available fog resource accordingly. We rigorously evaluate the performance of the proposed approach using simulation, as well as its correctness using formal verification. The evaluation findings are promising, both in terms of energy consumption and Quality of Service (QoS).

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Case-based reasoning classifier based on learning pseudo metric retrieval

Cited by 21 publications

References 33 publications

An IoMT-based geriatric care management system for achieving smart health in nursing homes

An IoMT-based geriatric care management system for achieving smart health in nursing homes

Process Feature Change Recognition Based on Model Performance Monitoring and Adaptive Model Correction for the Gold Cyanidation Leaching Process

QoS-aware service provisioning in fog computing

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