Mouth features extraction for emotion classiﬁcation

Robert, Staniucha; Wojciechowski, Adam

doi:10.15439/2016f390

Cited by 14 publications

(3 citation statements)

References 38 publications

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“…Next step is to construct a distributed computational environment for big experimental measurement data employing map-reduced paradigm in order to cope and test CDPM performance extensively. It would be interesting to see the ARbased study and track users what and how these professionals perceive the industrial environments to obtain a baseline for further development [15][25] [26].…”

Section: B Discussion and Directions For Future Workmentioning

confidence: 99%

Contextual processing of electrical capacitance tomography measurement data for temporal modeling of pneumatic conveying process

Romanowski

2018

Annals of Computer Science and Information Systems

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This work covers deployment of contextual processing of measurement data in application to temporal modeling of pneumatic conveying industrial process. Electrical capacitance tomography (ECT) used as a non-invasive process monitoring tool is supported by data mining for regularization of nonlinear inverse problem solution. Processing of a larger number of archived experimental datasets enables extracting additional constraints for inference. Contextual data processing model (CDPM) extracts demanded information from the data in order to incorporate it as an expert knowledge about the process temporal behavior. Then it is incorporated into the Bayesian inference framework. Comparative analysis with previous work and domain expert prepared baseline to the proposed approach is demonstrated. Additionally, simplified parameterization is tested and verified by the quantitative experimental analysis.

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Section: B Discussion and Directions For Future Workmentioning

confidence: 99%

Contextual processing of electrical capacitance tomography measurement data for temporal modeling of pneumatic conveying process

Romanowski

2018

Annals of Computer Science and Information Systems

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“…Moreover, the problem of high-dimensional data and feature selection techniques should be considered. More and more data are collected either by interviews, equipment [39] or extraction from text [45], speech [46] or images [47], including medical imaging [48]. In high-dimensional datasets missing data may be more frequent [49] and appropriate feature selection technique [50], [51] may improve the imputation accuracy [10].…”

Section: Discussionmentioning

confidence: 99%

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

Wosiak

Glinka

Zamecznik

et al. 2018

Annals of Computer Science and Information Systems

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The paper describes the study on the problem of missing values in medical data collected to discover new dependencies between parameters in children born with intrauterine growth restriction disorder. The aim of the research is to propose a procedure that may be taken to improve the medical inference in the presence of missing data. The approach with use of unconditional mean and k-nearest neighbor imputation has been applied. The experiments proved that application of missing data imputation in original dataset yields more valuable dependencies when compared to original data, maintaining the confidence interval for goodness of fit with the original distribution above 90%. The discovered dependencies in data may establish the basis for new treatment procedures of children with intrauterine growth restriction disorder.

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“…A well-established technique in this field is linear discriminant analysis (LDA) [ 22 ], which separates data representing different classes by a hyperplane. Another group of methods for between-classes hyperplane construction are the support vector machines (SVM) [ 23 , 24 , 25 ]. Other approaches employ the Bayesian classifier [ 26 ], which assigns a feature vector to the class, or the Gaussian mixture model—a clustering method which rests on using the probability density function [ 27 ].…”

Section: State Of the Artmentioning

confidence: 99%

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

Opałka

Stasiak

Szajerman

et al. 2018

Sensors

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Mental tasks classification is increasingly recognized as a major challenge in the field of EEG signal processing and analysis. State-of-the-art approaches face the issue of spatially unstable structure of highly noised EEG signals. To address this problem, this paper presents a multi-channel convolutional neural network architecture with adaptively optimized parameters. Our solution outperforms alternative methods in terms of classification accuracy of mental tasks (imagination of hand movements and speech sounds generation) while providing high generalization capability (∼5%). Classification efficiency was obtained by using a frequency-domain multi-channel neural network feeding scheme by EEG signal frequency sub-bands analysis and architecture supporting feature mapping with two subsequent convolutional layers terminated with a fully connected layer. For dataset V from BCI Competition III, the method achieved an average classification accuracy level of nearly 70%, outperforming alternative methods. The solution presented applies a frequency domain for input data processed by a multi-channel architecture that isolates frequency sub-bands in time windows, which enables multi-class signal classification that is highly generalizable and more accurate (∼1.2%) than the existing solutions. Such an approach, combined with an appropriate learning strategy and parameters optimization, adapted to signal characteristics, outperforms reference single- or multi-channel networks, such as AlexNet, VGG-16 and Cecotti’s multi-channel NN. With the classification accuracy improvement of 1.2%, our solution is a clear advance as compared to the top three state-of-the-art methods, which achieved the result of no more than 0.3%.

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Mouth features extraction for emotion classiﬁcation

Cited by 14 publications

References 38 publications

Contextual processing of electrical capacitance tomography measurement data for temporal modeling of pneumatic conveying process

Contextual processing of electrical capacitance tomography measurement data for temporal modeling of pneumatic conveying process

Imputing Missing Values for Improved Statistical Inference Applied to Intrauterine Growth Restriction Problem

Multi-Channel Convolutional Neural Networks Architecture Feeding for Effective EEG Mental Tasks Classification

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