Speech neuromuscular decoding based on spectrogram images using conformal predictors with Bi-LSTM

Wang, You; Zhang, Ming; Wu, Ru Feng; Wang, Hengyang; Luo, Zhiyuan; Li, Guang

doi:10.1016/j.neucom.2021.03.025

Cited by 19 publications

(10 citation statements)

References 27 publications

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“…Although the novel CPSC proposed in this paper outperformed the conventional CPKNN in many aspects in the offline and online tasks, there are still limitations in our research which must be mentioned. First, there lacks a comparison between CPSC and other more complicated CP framework, such as CPSVM and CP-LSTM [26], which may be able to perform better than CPKNN. In this study, we focused on the comparison with CPKNN.…”

Section: Discussionmentioning

confidence: 99%

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CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose

Liu¹,

Zhan²,

Yang³

et al. 2021

Preprint

Self Cite

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In machine learning applications, the reliability of predictions is significant for assisted decision and risk control. As an effective framework to quantify the prediction reliability, conformal prediction (CP) was developed with the CPKNN (CP with kNN). However, the conventional CPKNN suffers from high variance and bias and long computational time as the feature dimensionality increases. To address these limitations, a new CP frameworkconformal prediction with shrunken centroids (CPSC) is proposed. It regularizes the class centroids to attenuate the irrelevant features and shrink the sample space for predictions and reliability quantification. To compare CPKNN and CPSC, we employed them in the classification of 12 categories of alternative herbal medicine with electronic nose as a case and assessed them in two tasks: 1) offline prediction: the training set was fixed and the accuracy on the testing set was evaluated; 2) online prediction with data augmentation: they filtered unlabeled data to augment the training data based on the prediction reliability and the final accuracy of testing set was compared. The result shows that CPSC significantly outperformed CPKNN in both two tasks: 1) CPSC reached a significantly higher accuracy with lower computation cost, and with the same credibility output, CPSC generally achieves a higher accuracy; 2) the data augmentation process with CPSC robustly manifested a statistically significant improvement in prediction accuracy with different reliability thresholds, and the augmented data were more balanced in classes. This novel CPSC provides higher prediction accuracy and better reliability quantification, which can be a reliable assistance in decision support.

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Section: Discussionmentioning

confidence: 99%

“…Conformal predictor leverages a measurable function A (2) as the nonconformity measurement. For SC, inspired by the previous works [21,26], the function A was defined as:…”

Section: Conformal Prediction With Shrunken Centroids (Cpsc)mentioning

confidence: 99%

CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose

Liu¹,

Zhan²,

Yang³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…We use three highly effective and widely used architectures trained on the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC), InceptionV3, Inception-ResNetV2, and Xception as base classifiers for the FNs (Byeon et al, 2020 ; Ali et al, 2021 ; Wang et al, 2021a , b ; Yildirim and Çinar, 2021 ). In theory, these three deep networks can be replaced with other networks based on specific classification tasks.…”

Section: Disease Detection Algorithm For Retinal Oct Based On An Fnmentioning

confidence: 99%

FN-OCT: Disease Detection Algorithm for Retinal Optical Coherence Tomography Based on a Fusion Network

Huang

Feng

et al. 2022

Front. Neuroinform.

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Optical coherence tomography (OCT) is a new type of tomography that has experienced rapid development and potential in recent years. It is playing an increasingly important role in retinopathy diagnoses. At present, due to the uneven distributions of medical resources in various regions, the uneven proficiency levels of doctors in grassroots and remote areas, and the development needs of rare disease diagnosis and precision medicine, artificial intelligence technology based on deep learning can provide fast, accurate, and effective solutions for the recognition and diagnosis of retinal OCT images. To prevent vision damage and blindness caused by the delayed discovery of retinopathy, a fusion network (FN)-based retinal OCT classification algorithm (FN-OCT) is proposed in this paper to improve upon the adaptability and accuracy of traditional classification algorithms. The InceptionV3, Inception-ResNet, and Xception deep learning algorithms are used as base classifiers, a convolutional block attention mechanism (CBAM) is added after each base classifier, and three different fusion strategies are used to merge the prediction results of the base classifiers to output the final prediction results (choroidal neovascularization (CNV), diabetic macular oedema (DME), drusen, normal). The results show that in a classification problem involving the UCSD common retinal OCT dataset (108,312 OCT images from 4,686 patients), compared with that of the InceptionV3 network model, the prediction accuracy of FN-OCT is improved by 5.3% (accuracy = 98.7%, area under the curve (AUC) = 99.1%). The predictive accuracy and AUC achieved on an external dataset for the classification of retinal OCT diseases are 92 and 94.5%, respectively, and gradient-weighted class activation mapping (Grad-CAM) is used as a visualization tool to verify the effectiveness of the proposed FNs. This finding indicates that the developed fusion algorithm can significantly improve the performance of classifiers while providing a powerful tool and theoretical support for assisting with the diagnosis of retinal OCT.

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“…Moreover, in Mughees's work [20], the bidirectional LSTM network model is proven to be not only capable of learning long-term dependencies between the time steps of sequence data, but also can effectively use past and future information for prediction. Consequently, thanks to the ability to process time series data, the Bi-LSTM model has been successfully applied in many time series prediction tasks [21][22][23][24][25][26][27]. Inspired by this idea, this paper aims to exploit the learning method based on Bi-LSTM model to deal with event trend prediction with a sequence to label model.…”

Section: Related Workmentioning

confidence: 99%

Trend Prediction of Event Popularity from Microblogs

Zhao

2021

Future Internet

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Owing to rapid development of the Internet and the rise of the big data era, microblog has become the main means for people to spread and obtain information. If people can accurately predict the development trend of a microblog event, it will be of great significance for the government to carry out public relations activities on network event supervision and guide the development of microblog event reasonably for network crisis. This paper presents effective solutions to deal with trend prediction of microblog events’ popularity. Firstly, by selecting the influence factors and quantifying the weight of each factor with an information entropy algorithm, the microblog event popularity is modeled. Secondly, the singular spectrum analysis is carried out to decompose and reconstruct the time series of the popularity of microblog event. Then, the box chart method is used to divide the popularity of microblog event into various trend spaces. In addition, this paper exploits the Bi-LSTM model to deal with trend prediction with a sequence to label model. Finally, the comparative experimental analysis is carried out on two real data sets crawled from Sina Weibo platform. Compared to three comparative methods, the experimental results show that our proposal improves F1-score by up to 39%.

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Speech neuromuscular decoding based on spectrogram images using conformal predictors with Bi-LSTM

Cited by 19 publications

References 27 publications

CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose

CPSC: Conformal prediction with shrunken centroids for efficient prediction reliability quantification and data augmentation, a case in alternative herbal medicine classification with electronic nose

FN-OCT: Disease Detection Algorithm for Retinal Optical Coherence Tomography Based on a Fusion Network

Trend Prediction of Event Popularity from Microblogs

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