Wyniki wyszukiwania - BazTech

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An intelligent neural network algorithm for uncertainty handling in sensor failure scenario of food quality assurance model

Deepa S. N., Jayalakshmi N. Yogamba

Computer Assisted Methods in Engineering and Science

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2022

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Vol. 29, no. 1-2 spec.

105--123

EN

The quality of food is usually tested by sensing the product odor using e-nose technique.However, in a real-time testing environment, some of the employed sensors may fail tooperate, which imposes great uncertainty on the food quality assurance model. To handlethe uncertainty, a support vector machine (SVM) classifier algorithm is developed todeal with the failure sensor effect using a data imputation strategy. The proposed modelis evaluated experimentally by means of benchmark datasets, and validated in a real-time environment by programming an Arduino-UNO controller in the internet of things(IoT) environment.

2

Missing-data imputation using wearable sensors in heart rate variability

Tlija A., Węgrzyn-Wolska K., Istrate D.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2020

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Vol. 68, nr 2

255--261

EN

The objective of this work is to set up a methodology that considers missing data from a connected heartbeat sensor in order to propose a good replacement methodology in the context of heart rate variability (HRV) computation. The framework is a research project, which aims to build a system that can measure stress and other factors influencing the onset and development of heart disease. The research encompasses studying existing methods, and improving them by use of experimental data from case study that describe the participant’s everyday life. We conduct a study to modelize stress from the HRV signal, which is extracted from a heart rate monitor belt connected to a smart watch. This paper describes data recording procedure and data imputation methodology. Missing data is a topic that has been discussed by several authors. The manuscript explains why we choose spline interpolation for data values imputation. We implement a random suppression data procedure and simulate removed data. After that, we implement several algorithms and choose the best one for our case study based on the mean square error.

3

Influence of missing data imputation method on the classification accuracy of the medical data

Orczyk T., Porwik P.

Journal of Medical Informatics & Technologies

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2013

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Vol. 22

111--116

EN

Aim of this study is to show the dangers of filling missing data - particularly medical data. Because there are many dedicated medical expert systems and medical decision support systems, a special attention must be paid on the construction of classifiers. Medical data are almost never complete, and completion of the missing data requires a special care. The safest approach of dealing with missing data would be removing records with missing parameters and/or removing parameters that are missing in the records. Unfortunately reducing data set that is already very small is not always an option. Dangers coming out from data imputation are shown in the article, which presents the influence of selected missing data filling algorithms on the classification accuracy.