Parameterized SVM for personalized drug concentration prediction

You, Wenqi; Simalatsar, Alena; Micheli, Giovanni De

doi:10.1109/embc.2013.6610867

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Regarding network security posture prediction, the literature [14] proposes a pose prediction method that supports vector machine parameter optimization. The literature [15] proposes a support vector machine approach to network security posture prediction based on optimizing artificial bee colony algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Hidden Markov Model-Based Network Security Posture Prediction Model

Yang

2023

Applied Mathematics and Nonlinear Sciences

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As a key technology of network security situational awareness, this paper focuses on network security situational prediction technology and proposes a new network security situational prediction model based on Hidden Markov Model. The paper proposes a network security posture prediction method based on the improved Hidden Markov Model for the problem that the Baum-Welch parameter training method of the traditional Hidden Markov Model for posture prediction is sensitive to initial values and easily falls into local optimum. The method obtains the initial parameters by introducing the simulated annealing algorithm and using its excellent probabilistic burst-jump property to find the optimal in the global range. The Baum-Welch algorithm is used to optimize the initial parameters further to obtain the optimal model parameters, and then a more accurate posture prediction model is established. The probability of occurrence of the alarm information sequence corresponding to the network security posture value of 3 at t= 4 is obtained by simulating the network environment for testing, which is 0.000268, 0.000152, 0.000147, 0.000284, and 0.000187. Comparing the generated network security posture values with the real situation, it is found that the predicted results in this paper are highly similar to the real values. It is verified that the improved Hidden Markov method can effectively improve the accuracy of the network security posture prediction model and reflect the network security situation more objectively and realistically.

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Section: Introductionmentioning

confidence: 99%

A Hidden Markov Model-Based Network Security Posture Prediction Model

Yang

2023

Applied Mathematics and Nonlinear Sciences

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Personalized Drug Administrations Using Support Vector Machine

et al. 2013

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Safe Implementation of Embedded Software for a Portable Device Supporting Drug Administration

Simalatsar

Bornet

You

et al. 2014

2014 IEEE International Conference on Bioinformatics and Bioengineering

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Abstract-Poor adherence to medical regimen causes approximately 33% to 69% of medication-related hospitalizations and accounts for $100 billion in annual health care costs. In this paper we address the problem of unintentional non adherence, when patient fails to take a medication due to forgetfulness or carelessness. We present the safe approach to software implementation of a portable reminder device with enabled personalization of medical regimen. The presented prototype is designed for imatinib administration, a drug used to treat Chronic Myeloid Leukemia (CML). However, thanks to the component-based structure of the software, the method can be applied to other cases by replacing implementation of certain components.

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Parameterized SVM for personalized drug concentration prediction

Cited by 4 publications

References 11 publications

A Hidden Markov Model-Based Network Security Posture Prediction Model

A Hidden Markov Model-Based Network Security Posture Prediction Model

Personalized Drug Administrations Using Support Vector Machine

Safe Implementation of Embedded Software for a Portable Device Supporting Drug Administration

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